1. INTRODUCTION

1.1 INTRODUCTION:In this paper the available diversity channels are utilized by coding in Adaptive

Binary Fashion to improve the reliability of the system, as well as reduce the fading

effects. Adaptive Binary Coding has been proposed as an alternative basis of

implementing diversity selection and it has been shown to be more attractive than the

conventional diversity selection based on power measurement and reliability of the

received signals. It is found that the proposed system provides noticeable gain over the

classical diversity system when binary BCH codes with hard-decision decoding are used.

Moreover, the proposed system offers flexibility in choosing the throughput of the

system, which the diversity system lacks. The advantages of the proposed system are

obtained at only slight increase in implementation complexity. Among the countless

efforts to guarantee the quality of service under this hostile environment, this project

focuses on how to combat path loss and fading.

Fading is a severe problem that most of the wireless communication systems face.

Fading, also called the small-scale path loss, comes from the multipath propagation of

signals. Fading signicantly degrades the performance of wireless communication systems.

In a wireless transmission the signal quality suffers severely from a bad channel quality

due to fading caused by multi-path propagation. Basically, fading in a wireless channel

refers to the time and frequency variations of the channel quality. To reduce such effects,

Diversity can be used to transfer the different samples of the same signal over

independent channels realized in time, frequency, polarization or over space.

The diversity technique can be used to maximise the received signal strength or to

minimise the delay spread. In Diversity Technique several replicas of the information

signal can be transmitted over independently fading channels. At receiver end, atleast one

of the signal will be present which is not severely degraded by fading.

Diversity along with Coding are two powerful techniques to combat fading

effects on communication channels. In this paper the available diversity channels are

utilized by forward error correction coding in an adaptive fashion to improve the

reliability of the system. Based on the quality of the diversity channels, the code rate over

each channel is determined using discrete optimization of the overall error probability,

subject to the constraint of fixed overall throughput rate. Diversity schemes are effective

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each channel is determined using discrete optimization of the overall error probability,

subject to the constraint of fixed overall throughput rate. Diversity schemes are effective

over independently fading channels because it is very improbable that all the received

copies of the signal are affected by deep fade. Coding, on the other hand, gives the system

the capability of combating the bit errors that are caused by channel noise. For time-

varying channels adaptive coding, in which the error correction capability is matched to

the prevailing channel conditions, improves the performance significantly.

Coding and diversity have been combined in a number of ways to further enhance

the performance of digital communication systems over fading channels. A

straightforward way of employing both techniques is first to encode the information

sequence for the purpose of error correction (Forward Error Correction Systems, FEC)

and then send the resulting codeword over several diversity channels. Upon receiving the

several copies of the transmitted signal, the receiver may either select the best one or use

them all to obtain an estimate of the transmitted codeword that is more reliable than that

obtainable from any of the diversity channels. In both cases decoding is performed

afterwards.. Based on the quality of the diversity channels, the code rate over each

channel is determined using discrete optimization of the overall error probability, subject

to the constraint of fixed overall throughput rate.

Adaptive techniques seek to modify the transmission scheme used by the sender

according to the state of the channel seen by the receiver. Generally, such schemes

involve feedback, concerning the state of the channel, from the receiver to the sender. A

number of adaptive signalling techniques have been proposed for fading channels, which

adjust certain parameters of the transmitted signal to compensate for channel conditions,

such as the transmitted signal power or the signalling rate. These techniques are

extremely effective for fading channels with AWGN. Power control is a commonly used

type of adaptive transmission. Many practical schemes consider modifying the

modulation used in order to combat fading. A common means of adapting transmission is

to use different types of modulation according to the state of the channel and possibly

other considerations such as multiuser interference. But none of the above schemes

considers dynamically adapting the codes at the transmitter to the quality of the channel

surement at the receiver.

In this project we consider a different type of Adaptive scheme, using Binary

Coding For Diversity Communication Systems, that would be successful in not only

reducing the Signal Fading, but also Identify the Type of Communication Channel in

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order to increase the Transmission Rate. While the scheme still adapts the transmission to

the channel seen by the receiver, the transmitter does not use feedback to determine its

policy. Instead, the transmitter takes into account the time-varying quality of the channel

measurement available at the receiver in order to modify its transmission policy. Thus,

the transmitter adapts its signaling and coding to the quality of the channel measurement,

rather than to the quality of the channel (in terms of carrier to noise ratio or other

metric).This system uses adaptive forward error correction scheme where the distribution

of the information bits, and hence the encoding process, are dependent on the relative

channel qualities. Both correlated and uncorrelated channels will be considered. The

performance criterion of the system is to communicate at as small an error as possible

whilst maintaining a fixed throughput rate. The system, in implementation terms, reduces

to that of selecting the best group of codes for a given channel estimation. Therefore, the

system proposed here may be viewed as a wider-range selective diversity system.

1.2 BACKGROUND:In 1999 Philips introduced two cordless telephones: the ‘Kala’ for the consumer

market and the ‘Zenia’ for the semi- professional market. Their radio interface was based

on the Digital European Cordless Telecommunication (DECT) standard. The usable

indoor range was maximised by two internal antennas, which was used in combination

with fast switching technology to ensure good reception quality throughout the area

served by the DECT network.

It was observed that

Indoor range increased from about 40 to 50 metres (+30%),

Total muted time reduced from 2.2% of the total time to 0.25% (-90%),

Number of audible clicks reduced from 4 per minute to 0.

Experiments proved that the improvement was obtained by an implementation of

the antenna-diversity principle. The performance of an antenna-diversity transceiver was

better than a standard transceiver with a single-antenna. This improvement could not

easily and inexpensively be obtained by other techniques.

Since then, various researches have been carried out on Diversity Techniques to

achieve a System that combats the Fading Effects and Reliability Issues associated with

the current Communication Systems.

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Benelli suggested using a number of independent channels in a manner different

from that of classical diversity systems. He proposed a system in which each the code

words to be transmitted are divided into L sub-blocks for transmission over L channels

such that the ith sub-block of all code words are transmitted as one block over the ith

channel such that 1< i < L. His technique was extremely effective for fading channels

with Additive White Gaussian Noise (AWGN).

Today Digital transmission that would assure high-level voice security over

wireless systems has recently become a subject of active study. Several methods of

modulation and coding have been proposed for digital transmission over various fading

conditions. Unfortunately, no conclusion has been reached yet regarding the optimum

choices of signal shaping, modulation, and coding and lots of challenges still remain open

in-front of the communication engineers for designing an optimum communication

technique for the near future.

1.3 OBJECTIEVES: The main objectives of this project are:

To define and verify a procedure for designing Diversity Communication

Systems using Adaptive Binary Coding.

To identify the type of communication channel, in order to improve the

transmission rate.

To implement detectors for Diversity Communication Systems using Adaptive

Binary Codes in the fading channels.

Investigate the performance of the prescribed system and identify areas for

improvement.

Introduce and develop improvements to the existing diversity techniques to

further reduce loss rates of signals and improve their reliability.

Investigate through simulations the performance achieved by the Adaptive

Binary Coded Diversity Communication Systems.

Investigate possible applications of Binary Adaptive Coding in Diversity

Communication Systems.

Discuss the strengths and weaknesses of the proposed communication

technique.

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1.4 NECESSITY:The main necessities of Adaptive Binary Coding for Diversity Communication

Technique are:

To reduce signal fading.

To enhance the error performance.

To identify the type of communication channel.

To increase signal reliability.

To increase signal to noise ratio.

To improve the transmission rate.

To resolve the problems of hand off.

.

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2. LITERATURE SURVEY

The discussions done by M. A. Kousa and S. A. Al-Semari in their paper titled

“Adaptive Binary Coding for Diversity Communication Systems”, proposed that the

available diversity channels could be utilized effectively by forward error correction

coding in an adaptive fashion to improve the reliability of the system. Based on the

quality of the diversity channels, the code rate over each channel is determined using

discrete optimization of the overall error probability, subjected to the constraint of fixed

overall throughput rate. Their proposed system provides noticeable gain over the classical

diversity system when binary BCH codes with hard-decision decoding are used.

Moreover, the proposed system offers flexibility in choosing the throughput of the

system, which the diversity system lacks. Finally they proposed that using binary BCH

codes with hard-decision decoding, the proposed system offers a gain of 0.4-1.3 dB over

classical diversity systems for diversity orders of 2-4. Moreover, the proposed system can

operate at any desirable throughput rate which is an added advantage compared to

selective diversity systems.

According to a Cooperative Diversity in Wireless Networks discussed by J.

Nicholas Laneman, David N. C. Tse and Gregory W. Wornell in their paper titled

“Cooperative Diversity in Wireless Networks: Efficient Protocols and Outage Behaviour”

the authors proposed and analyzed low-complexity cooperative diversity protocols that

combat fading induced by multipath propagation in wireless networks. The underlying

techniques exploit space diversity available through cooperating terminals relaying

signals for one another. Their developed scheme gave the performance characterizations

in terms of outage events and associated outage probabilities, which measure robustness

of the transmissions to fading, focusing on the high signal-to-noise ratio (SNR) regime.

Finally they got from the scheme that all of our cooperative diversity protocols are

efficient in the sense that they achieve full diversity (i.e., second-order diversity in the

case of two terminals), and moreover, are close to optimum (within 1.5 dB) in certain

regimes.

A. Afrashteh and D. Chukurov proposed a diversity technique in their paper titled

“Performance Of A Novel Selection Diversity Technique In An Experimental TDMA

System For Digital Portable Radio Communications”. Their developed scheme described

the measured average Bit Error and Block Error Ratio performance of a coherent burst

Time Division Multiple Access radio link in a simulated flat Rayleigh fading environment

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using a unique technique for implementing two-branch selection diversity. The link

consists of a 500 Kbits Quadrature Amplitude Modulation burst transmitter and a

coherent receiver with a fast carrier recovery circuit. Their proposed technique gives the

performance of selection diversity is achieved with only one receiver chain. Results

obtained by the author indicated that the presented showing link performance as functions

of Rayleigh fading rate (Fd) and the time delay between signal measurement diversity

selection and the actual data burst.

Rodney G. Vaughan and J. Bach Andersen developed antenna diversity in their

paper titled “Antenna Diversity in Mobile Communication”. Their developed scheme

describes the conditions for antenna diversity actions are investigated. In terms of the

fields, a condition is shown to be that the incident field and the far field of the

diversity antenna should obey (or nearly obey) an orthogonality relationship. From

their scheme they getting the result that high gain antennas at the mobile, which

approximates most practical mobile antennas, is shown to be zero (or low) mutual

resistance between elements. This is not the case at the base station, where the condition

is necessary only.

Elisabeth A. Neasmith and Norman C. Beaulieu are proposed on selection of

diversity in their paper titled “New Results on Selection Diversity”. Their proposal

scheme discuss the performances of selection diversity receiver structures in a slow flat

Rayleigh-fading environment are assessed. In this structure a number of new and

interesting results are obtained. Binary digital signaling using non-coherent frequency-

shift keying (NCFSK), differential phase-shift keying (DPSK), coherent phase-shift

keying (CPSK), and coherent frequency-shift keying (CFSK) is considered. According to

their proposal scheme results show that S + N selection systems perform better than

predicted by the Traditional Selection Diversity Model.

Lukas Leijten proposed the antenna diversity transceivers for wireless consumer

product in his paper titled “Design of Antenna Diversity Transceivers for Wireless

Consumer Products”. He give a discussion of his thesis is that Antenna-diversity

implementations consist of two or more antennas and a circuit to combine the antenna

signals in an optimum way. The performance of an antenna-diversity transceiver is better

than a standard transceiver with a single-antenna. This improvement cannot easily and

inexpensively be obtained by other techniques. Antenna diversity is therefore an

important principle that can be implemented in many wireless consumer products, like

mobile phones and wireless networks. He found result from his scheme that to define and

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verify a procedure to design adaptive diversity implementations for portable consumer

products. This procedure focuses on an optimal design with respect to circuit complexity,

power dissipation, size, cost and other relevant issues. The procedure enforces a

systematic approach towards designing and testing diversity implementations. To verify

the procedure, it has been applied to design a state-of-the-art antenna-diversity receiver.

This receiver has been built and tested. The performance of the receiver is close to that

predicted by simulations.

Choo Chiap Chiau developed the diversity antenna array in his paper titled “Study

of the Diversity Antenna Array for the MIMO Wireless Communication Systems”. The

main challenge in designing two or more antennas on a small mobile terminal is to

achieve a high isolation between the antennas. So he proposed a scheme to accept the

challenge and describe two approaches to address in antenna design. Firstly, a compact

self-balanced antenna which is a folded loop antenna with a loaded dielectric slab is

proposed. The second method is to employ Electromagnetic Band-Gap (EBG) structures

on the ground plane of the antennas to suppress the surface currents at specific

frequencies band (i.e. stop-band region). A new EBG structure with smaller dimensions

and wider stop-band is developed. Finally he getting result that the future IEEE 802.11n

standard is going to support the current Wi-Fi frequency bands at 2.4GHz and 5.2GHz.

The proposed dielectric loaded folded loop antenna could be further developed to operate

at 2.4GHz band or to operate at both bands.

Andrea Conti, Moe Z. Win and Marco Chiani developed adaptive M-ary

quadrature amplitude modulation (QAM) with antenna subset diversity in his paper titled

“Slow Adaptive M-QAM with Diversity in Fast Fading and Shadowing”. In their

proposed scheme gives slow adaptive modulation (SAM) technique that adapts the

constellation size to the slow variation of the channel due, for example, to shadowing.

The proposed SAM technique is more practical than conventional fast adaptive

modulation (FAM) techniques that require adaptation to fast fading variations. Our results

show that the SAM technique can provide a substantial increase in throughput with

respect to fixed schemes while maintaining an acceptable low bit-error outage. We also

compare SAM and FAM techniques, showing that through put of SAM can be, in many

practical cases, close to that of FAM, despite the fact that SAM is less complex and

requires a lower feedback rate. After their proposed scheme getting to compared SAM

with FAM and non-adaptive modulation schemes in terms of both bit-error outage and

normalized throughput (SE) for coherent detection of M-QAM with ASD in the presence

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of Rayleigh fading and log-normal shadowing. It is shown that the SAM technique can

provide substantial improvement over non-adaptive schemes in terms of both SE and

BEO. In particular, it is shown that by using the SAM technique with modulation levels

in {Mmin, , , , , , , , Mmax}, a substantial increase in SE can be achieved while maintaining

the same outage of non-adaptive modulation using M=Mmin. We also showed that the

performance of SAM is close to FAM, despite the need for a lower feedback rate, and

thus less complexity, with SAM. The proposed methodology is applicable to other

modulation formats, diversity techniques, and fading channels. By using the proposed

methodology, one can obtain SE and BEO for various channel parameters as well as for

different diversity techniques.

Jingxian Wu, Chengshan Xiao and Norman C. Beaulieu developed optical

diversity in their paper titled “Optimal Diversity Combining based on Noisy Channel

Estimation”. In their proposed scheme describe the optimal diversity receiver for

coherent reception with noisy channel state information and independent and identically

distributed fading channels is derived. Exact expressions for the average error probability

of optimal diversity MPSK with noisy channel estimation are derived for Rayleigh and

Ricean fading channels; closed-form expressions are obtained for some special cases.

Some interesting observations regarding practical diversity receiver design for higher-

order modulation formats are drawn. From their scheme they finally got the result of the

optical diversity was that a novel diversity receiver structure which is optimal for noisy

channel state information has been derived. Exact, closed-form expressions for the

average error probability of the optimal diversity receiver operating with noisy channel

state information have been derived for MPSK modulation in both Rayleigh and Ricean

channels. The new results for systems with noisy channel state information include

systems with perfect channel state information as special cases. Simulation results are in

excellent agreement with the theoretical results. A useful observation of significant

practical design value was that improving the channel estimation.

Ibrahim Abou-Faycal, Muriel Medard, and Upamanyu Madhow developed binary

adaptive coded on Pilot symbol assisted modulation (PSAM) on their paper titled “Binary

Adaptive Coded Pilot Symbol Assisted Modulation over Rayleigh Fading Channels

without Feedback”. In their proposed scheme show that PSAM schemes can be improved

by adapting the coded modulation strategy at the sender to the quality of the channel

measurement at the receiver, without requiring any channel feedback from the receiver.

They consider performance in terms of achievable rate for binary signalling schemes. The

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transmitter employs interleaved codes, with data symbols coded according to their

distance from the nearest pilot symbols. Symbols far away from pilot symbols encounter

poorer channel measurements at the receiver and are therefore coded with lower rate

codes, while symbols close to pilot symbols benefit from recent channel measurements

and are coded with higher rate codes. The performance benefits from this approach are

quantized in the context of binary signalling over time-varying Rayleigh fading channels

described by a Gauss–Markov model. Causal and non-causal channel estimators of

varying complexity and delay are considered. They also shown that, by appropriate

optimization for the spacing between consecutive pilot symbols, the adaptive coding

techniques proposed can improve achievable rate, without any feedback from the receiver

to the sender. At finally they got the result of their scheme was that they had considered

no feedback and hence no adaptation at the sender to the state of the channel, their

scheme can be applied when there is channel side information at the sender. For instance,

power control when there is adaptive coding of the type described in their paper may

yield more power allocation to better SNR realization than when there is no adaptation to

the channel estimation error variance at the receiver. The benefits of the latter adaptation

they had shown to increase with SNR. Therefore, the benefit of high SNR over low SNR

would, in turn, increase through the use of coding adaptation to channel estimation error

variance.

Ola Jetlund, Geir E. Oien, Kjell J. Hole, Vidar Markhus and Bard Myhre proposed

adaptive coding and modulation (ACM) in their paper titled “Rate-Adaptive Coding and

Modulation with LDPC Component Codes”. In their scheme they take a closer look at the

consequences of introducing block codes into an adaptive coding and modulation (ACM)

system. One key issue is the bit-error rate (BER) performance of the error correcting

codes used in the scheme. They have simulated the BER performance for the promising

low density parity check (LDPC) codes. Also they made some simple considerations

regarding the mobility constraints of such a scheme. Their proposed scheme gave the

results strongly indicate the trade-off between good BER performance (i.e., longer code-

words) and mobility.

The effective capacity (EC) proposed by Qing Wang, Dapeng Wu and Pingyi Fan

provides a powerful tool for the design of quality of service (QoS) provisioning

mechanisms in their paper titled “Effective Capacity of a Correlated Rayleigh Fading

Channel”. In their proposal scheme new discrete-frequency EC formula for a correlated

Rayleigh fading channel; different from the EC formula developed in earlier by Wu and

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Negi and their new discrete-frequency EC formula can be used in practice. Through

simulation, they verify that the EC formula developed by Wu and Negi is accurate.

Furthermore, to facilitate the application of the EC theory to the design of practical QoS

provisioning mechanisms in wireless networks, their propose a spectral-estimation-based

algorithm to estimate the EC function, given channel measurements; they also analyze the

effect of spectral estimation error on the accuracy of EC estimation. Finally from their

proposal simulation scheme describe the results to showing that their proposed spectral-

estimation-based EC estimation algorithm is accurate and indicating the excellent

practicality of their algorithm.

In adaptive coded modulation described by Bengt Holter in his paper titled

“Adaptive Coded Modulation in Spatial and Multiuser Diversity Systems”. In his

proposed scheme was devoted to performance analysis of an adaptive coded modulation

(ACM) scheme based on multidimensional trellis codes. The performance of the ACM

scheme is evaluated for slowly flat-fading channels. His analysis scheme was focused on

two different combining techniques: maximum ratio combining (MRC) and switched

combining (SC). A multiple-input multiple output (MIMO) diversity system is also

considered, in which case the combined effect of both transmit and receive diversity is

realized by using space-time block coding at the transmitter. At last he was getting result

from his proposed scheme that to reduce the Average feedback load (AFL) in multiuser

systems relying on feedback to maximize the Average spectral efficiency (ASE). Also he

getting numerical results quantifying the trade-off between ASE and AFL have been

presented, showing that the AFL can be reduced significantly compared to the optimal

SCT scheme without experiencing a big performance loss in ASE. The proposed access

schemes are quite attractive also from a fairness perspective.

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3. SYSTEM IMPLEMENTATION

3.1. DIVERSITY TECHNIQUES:The Diversity Technique uses several replicas of the information signal

transmitted over independently fading channels. At receiver end, at least one of the

signals will be present which is not severely degraded by fading. The objective of

diversity is to provide a communication system with two or more paths from transmitter

to receiver across the radio channel so that the fading phenomena of these paths are as

uncorrelated as possible. Continuously selecting the path with the best signal quality will

result in an improved communication link. Different ways of accessing the radio channel

(diversity) are possible to obtain uncorrelated paths. The application of diversity results in

an improved signal-to interference ratio. The various types of diversity techniques are

discussed below.

3.1.1 Time Diversity

The properties of the radio channel changes as a function of time. By

transmitting a message in two or more time slots on the same frequency across the radio

channel, the probability of good reception is increased. For achieving D independently

fading versions of the same information-bearing signal is to transmit the same

information in D different time slots, where the time separation between successive time

slots equals or exceeds the coherence time Tct of the channel.

Figure 3.1.1: Time diversity illustrated by a data block that is transmitted twice with delay td.

Increasing the time interval between the messages increases the de-correlation

between the received signal levels of the messages. A measure of this de-correlation is the

coherence time. This time diversity implementation, however, result in half the available

bandwidth in terms of bytes per second. As a result, this implementation is not very

popular. At the moment it is only used in paging systems. The major advantage is of

course that no additional hardware is needed. In Time Division Multiple Access (TDMA)

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a derivative of time diversity is sometimes used. In TDMA system the information of the

source is compressed and subsequently transmitted on a single carrier frequency together

with the information of other sources. These information packages are called time slots.

In the case of interference due to a time slot of an adjacent communication cell, a

different time slot on the same or on different carrier frequency can be chosen. This slot

hopping results in an improved signal-to interference ratio.

3.1.2. Frequency Diversity:

The fading characteristics of the radio channel are not the same for different

carrier frequencies. Transmitting the information using different carrier frequencies may

result in uncorrelated signals. This diversity implementation is called Frequency

Diversity. Thus for achieving D independently fading versions of the signal same

information-bearing signal is transmitted on D Orthogonal Frequency Division

Multiplexing (OFDM) carrier frequencies, where the separation between successive

carriers equals or exceeds the coherence bandwidth Bcb of the channel.

Figure 3.1.2: Frequency diversity using three carrier frequencies f0:1, f0:2 and f0:3

The frequency separation between the carrier frequencies determines the amount

of de-correlation of the signals. The frequency separation for which sufficient de-

correlation is obtained is related to the coherence bandwidth. This form of diversity is not

very efficient with respect to the use of the available bandwidth. In most implementations

the signal is not simultaneously transmitted on several carrier frequencies but only on the

one that will result in good transmission. This form of diversity is used in modern multi-

carrier communication systems, like GSM or DECT. The hopping between different

frequencies results in an increased circuit complexity. The carrier frequency de

correlation is also exploited in modulation techniques, like Orthogonal Frequency

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Division Multiplexing (OFDM) and in access techniques, like Code Division Multiple

Access (CDMA). A communication system based on these techniques is more robust

against frequency selective fading or interfering transmitters.

3.1.3. Space Diversity

Multiple receive (or transmit) antennas can be placed at different positions at a

distance less than the signal wavelength. Each antenna will receive the transmitted signals

via different paths through the radio channel. The received signals of the different

antennas will therefore be mutually de-correlated. The amount of de-correlation depends

on the antenna separation. The receiving antennas must be spaced sufficiently far apart so

that the multipath components in the signal have significantly different propagation paths.

Usually, a separation of a few wavelengths is required between a pair of receiving

antennas in order to obtain signals that fade independently. In most cases, a separation of

approximately half a wavelength is sufficient. This principle of obtaining de-correlated

signals is called Space Diversity.

Figure 3.1.3: Space diversity illustrated by three antennas separated by distances r1, r2 and r3.

A disadvantage of space diversity is the increased volume needed to contain

multiple antennas. The attractiveness of this diversity implementation is its simplicity.

Space Diversity is the most widely used diversity technique in wireless communication

system. Space Diversity can be further classified into 3 types.

3.1.3.1. Feedback or Scanning Diversity:

Principle: Scanning all the signals in a fixed sequence until the one with SNR

more than a predetermined threshold is identified.

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Fig 3.1.3.1: Block diagram of Feedback Diversity

This method is very simple to implement, requiring only one receiver. The resulting fading statistics are somewhat inferior to those obtained by the other

methods3.1.3.2. Max

imal Ratio CombiningPrinciple: Combining all the signals in a co-phased and weighted manner so

as to have the highest achievable SNR at the receiver at all times.

Figure 3.1.3.2: Block diagram of Maximal Ratio CombiningEqual Gain Combining

Combining all the signals in a co-phased manner with unity weights for all signal levels

so as to have the highest achievable SNR at the receiver at all times.

The probability of producing an acceptable signal from a number of unacceptable inputs is still retained. In certain cases it is not convenient to provide for the variable weighting capability.

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This allows the receiver to exploit signals that are simultaneously received on each branch.

The probability of producing an acceptable signal from a number of unacceptable inputs is still retained.

The performance is marginally inferior to maximal ratio combining and superior to selection Diversity.

3.1.4. Polarization Diversity

The polarization of the electromagnetic fields has different orientations at

different positions. If a linear receive antenna is used, there will be a probability that the

orientation or polarization of the antenna does not match the polarization of the

electromagnetic fields. This will result in a low received signal level. A differently

oriented linear antenna will result in a higher received signal level. Using multiple

differently polarized antennas is a way of obtaining uncorrelated signals. This

implementation, called Polarization Diversity, is relatively simple to implement.

Figure 3.1.4: Polarization diversity illustrated by two dipoles, one in the x direction and one in the z direction

A disadvantage is the increased volume to contain the antennas. For polarization

diversity the combination of horizontally and vertically polarized linear antennas

(monopole or dipole antennas) is quite often suggested. However, the horizontally

polarized antenna has two large nulls in its directivity pattern in the horizontal plane.

These nulls reduce the mean received power, because the power is predominantly

transmitted in the horizontal plane. A vertically polarized antenna is Omni-directional in

the horizontal plane, which results in a higher mean received power. Using two diversity

antennas which have a certain angle with respect to the horizontal, e.g. 45 and 45 degrees,

results in a better performance than that of the horizontal-vertical implementation. This

type of polarization diversity is referred to as slanted polarization diversity. For mobile

communication systems, like GSM, the transmitted signals are scattered and reflected by

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many objects. In this system polarization diversity seems to have a performance close to

that of space diversity.

3.1.5. Field-Component Diversity

At a certain location in a multi-path environment with many reflections the

electric field strength can be completely different from the magnetic field strength. This is

similar to standing waves occurring in resonators and transmission lines. For these

standing waves the maximum of the electric field coincides with the minimum of the

magnetic field and vice versa. Using antennas that predominantly couple to only one of

the field components can result in uncorrelated signals. The advantage of this field-

component diversity implementation is that the antennas can be situated at the same

location.

Figure 3.1.5: Field-component diversity illustrated by two antennas, electricdipole and magnetic loop.

The major disadvantage is the relatively low efficiency of small antennas

(including the matching circuits) that predominantly couple to the magnetic field.

Especially in the frequency range from 1 to 5 GHz the electric-field antennas have a

better efficiency. Directly summing the signals of an antenna that couples to the electric

field (dipole) with one that couples the magnetic field (loop) can result in a ’diversity’

antenna with a better output signal than a single antenna [Young, 2000]. In this case no

additional signal processing is needed.

3.1.6. Angle Diversity

Local variations of the electromagnetic field are the result of interference between

two or more reflected waves. By using a directional antenna one of the reflected waves

can be selected and the other ones can be suppressed. A good angle diversity

implementation is established either if more than one directional antenna is used or if a

directional antenna is used that can change its direction.

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Figure 3.1.6: Angle diversity illustrated by three directional horn antennas, dotted lines indicates directivity patterns.

A significant advantage of using angle diversity is the reduction of the time delay

spread due to the reduction of received (reflected) waves. This is an interesting feature,

especially in environments or locations with high delay-spread values, like highly

reflective assembly halls or at the boundaries of communication cells.

A disadvantage of directional antennas is that they have to be large in terms of

wavelengths. For the mobile communications frequency range, approximately from 1 to 5

GHz, antennas are needed that are too large to fit in a handset. However, phased arrays

can also adaptively suppress undesired waves by means of null and beam steering. The

smallest phased array consists of two antennas and a variable phase shifter. Space

diversity using equal-gain or maximum-ratio combining constitutes a phased array.

3.1.7. Selection Diversity:

Given that the information is transmitted to the receiver via D independently

fading channels, there are several ways that the receiver may extract the transmitted

information from the received signal. The simplest method is for the receiver to monitor

the received power level in the D received signals and to select for demodulation and

detection the strongest signal. In general, this approach results in frequent switching from

one signal to another. A slight modification that leads to a simpler implementation is to

use a signal for demodulation and detection as long as the received power level in that

signal is above a preset threshold. When the signal falls below the threshold, a switch is

made to the channel which has the largest received power level. This method of signal

selection is called Selection Diversity.

The main advantage of Selection diversity offers an average improvement in

the link margin without requiring additional transmitter power or sophisticated receiver

circuitry.

18

3.2. CLASSIFICATION OF DIVERSITY SYSTEMS:Diversity Systems can be classified into Macroscopic and Microscopic

diversity.

Fig 3.2: Classification of Diversity

» Macroscopic Diversity:

• Prevents Large Scale fading, caused by shadowing due to variation in both the

terrain profile and the nature of the surroundings.

• Large Scale fading is log normally distributed signal.

• This fading is prevented by selecting an antenna which is not shadowed when

others are; this allows increase in the signal-to-noise ratio.

» Microscopic Diversity:

• Prevents Small Scale fading, caused by multiple reflections from the

surroundings.

• It is characterized by deep and rapid amplitude fluctuations which occur as the

mobile moves over distances of a few wavelengths.

3.3 DIVERSITY COMMUNICATION SYSTEMS:The first scheme, denoted in the following as S1, is illustrated in the block

diagram given in Fig. 3.3. The source generates symbols from a finite alphabet A = {a1,

a2,. . . , aM} with M elements. The information symbols exiting the source may or may not

be encoded through an (n, k) channel code C ( i.e., a code with a codeword length of n

and k information symbols). The code C1 when present, is assumed defined on alphabet B

with N = Mu symbols, U ≥ 1 being an integer. In fact, while code C1 is often introduced to

reduce the error probability at the user side, it is not always used. Code C1 when present,

is assumed capable of correcting t errors; when C is absent, n is naturally equal to k.

19

M a cro s co p ic d ive rs ity M ic ro sco p ic d ive rs ity

D iv e rs ity

The scheme S1 also requires a second error-correcting code, denoted by C1. The

code C1, defined on the alphabet A, has b information symbols and a codeword length of

n = mb, where m ≥ 2 is an integer and is assumed capable of correcting t1 errors.

We shall start by examining a transmit codeword c = {cj} of C, n symbols long. If

U > 1, each component of c, defined on the alphabet B, is transformed in U elements of

the alphabet A.

Therefore, the codeword c is transformed in a vector c’, nc, = nu symbols of the alphabet

A long. The vector c’ = {c’j} for 1 ≤ j ≤ nc is divided into s sub blocks, each b symbols

long, being s = [nc / b], and [x] the lowest integer greater than or equal to x. The ith sub

block, designated ci is given by:

C’i = [ c’(i-1)b+1, c’,.......,c’ib ]

Code C1 encodes each sub block cj into a codeword wi, mb symbols long.

Codeword w, is divided into m sub blocks wi,j = {wi, j ( p ) } ,each b symbols long (1 ≤ j ≤

s, 1 ≤ j ≤ m, and 1 ≤ p ≤ b ). If code C, is a systematic code, then wi,1 = c’i.

After all the s sub blocks have been encoded, m different vectors, each n symbols

of the B alphabet long, are constructed. The jth vector, vi, for 1 ≥ j ≥ m, is:

Vj = [w1,j, w2,j, ....ws,j ]

In the classical diversity techniques, the transmitter sends c over all m channels.

In the scheme S1, the transmitter sends vector vi, n symbols long, over the jth channel so

that the transmitted message differs from one channel to another. In this paper, it is

always assumed an interleaving procedure; in this way, the noise affecting

successive symbols can be assumed as incorrelated.

Let us use r* to denote the vector, nc symbols long, received on the jth channel (1

≤ j ≤ m). The receiver divides r* into s sub blocks; the ith sub block (1 ≤ i ≤ s), b symbols

long, is denoted by: r*j,i, = {r*j,i(p)} for 1 ≤ p ≤ b . This is followed by the construction of

vector ri composed of nl symbols and defined as:

r*i = [ r*1,i, r*2,i, ....., r*s,i ]

The vector r, represents the received version of wi and is therefore given by:

ri= wi + ei

where ei is the error vector, n1 symbols long, representing the errors introduced by the

transmission channel on the ith sub block.

Vector ri is decoded through code C1 and the recovered information symbols are

stored in a vector c*i. This procedure is repeated for each sub block.

20

Figure 3.3 (a): General structure of a classical diversity communication system.

Figure 3.3 (b): General structure of a diversity communication system using the S1

technique.

When all the s sub blocks have been decoded, the vector c* = (c*1, c*2.., c*s),

composed by nc from the alphabet A, is transformed in a vector c” defined on the alphabet

B and composed of n symbols. The vector is sent to the decoder which decodes it in a C

codeword, denoted by c”.

The use of the code C1 which has a code rate of 1/m with m ≥ 2, makes it possible

to achieve lower error probabilities at the C decoder input than attainable with the

classical diversity techniques. Both convolution and block codes can be used as code C1.

A modification of the scheme S1, designated S2, has been developed to enhance

communication system performance in certain applications. While the transmitter

structure and protocol are the same in both techniques, the S2 always requires a C code.

In the S2, C1 is assumed capable of correcting t1, errors and at the same time of detecting

λ ≥ t1 errors, while C1 is assumed capable of correcting both erasures and errors.

Let us consider the ith received sub block r. This sub block is sent to the C,

decoder. If it contains t, or fewer errors, then it is decoded by C1 thereby yielding a sub

block c*i. On the contrary, if an uncorrectable error pattern is detected, C1 does not

21

proceed to decoding, and the ith sub block is declared an erasure vector xi = (x*i,1 , x*i,2 , .

. . , x*i,b ); Each erasure vector contains b erased symbols xi = (1 ≤ j ≤ b). After s sub

blocks have been decoded through C1 vector c contains ne erased sub blocks (0 ≤ ne ≤ s)

and p errors, with 0 ≤ p ≤ (s - ne)b. If code C and C1 are defined on the same alphabet (A

= B), correct decoding of c is obtained through C if neb + p ≤ dH where dH is the minimum

Hamming distance of code C.

However, the performance of the technique S2 is generally enhanced by choosing

the code C defined on an alphabet B # A. In this way, each erasure block xi defined on the

alphabet B with N = Mu symbols gives rise to na = [b/u] erasures. In particular, a good

choice is U = b. If code C is properly selected, lower error probabilities can be attained by

using the scheme S2 with respect to the classical diversity schemes and to the S1 scheme.

A typical class of error correctors suitable for this application is the Reed-Solomon (RS)

codes. As the C1 contains a high number of redundancy symbols in relation to

information symbols, the probability of nondetection of an uncorrectable error pattern,

and therefore of having errors in c*i, can be kept to a low level.

3.4. ADAPTIVE AND NONADAPTIVE CODING:First we consider the scheme where the transmitter does not adapt its transmission

strategy to the statistics of the channel estimates used at the receiver. More precisely, we

compute the achievable rates when the transmitter is using a single fixed input

distribution at all times. For this case, we assume that the transmitter considers the

channel to be block-faded i.e., the fading coefficient is assumed constant over intervals of

length and changing independently from one interval to the next. In order to evaluate the

performance of such a scheme for our model, we find first the optimal input distribution

for the block faded system, and then compute the average mutual information under this

distribution for different values of. This will allow us to quantify the performance of a

system when the transmitter does not adapt its coding strategy and uses instead one fixed

codebook independently of the time index. Next we consider the scheme where, without

any channel state information, the transmitter takes into consideration the statistics of the

channel estimates at the receiver. It adapts accordingly its modulation and coding to

maximize the rates that can be reliably transmitted over the channel. While no optimal

power allocation is performed here (a constant amount of power is used instead), at each

time step the transmitter uses a “good” codebook achieving the highest mutual

22

information of the Ricean channel the receiver sees. Equivalently, one can think of the

problem as that of finding the best input strategy that maximizes the expected mutual

information E X Y Y for each time step between ends. For these computations, we

considered the estimation methods for the pair R given by both set of (4) and (5), or of (5)

and (6), or of (7) and (8). Since no closed form expression can be obtained for the optimal

input distribution, we use standard Matlab tools to optimize, for each time period, the

expected mutual information over the input probability distribution. The input alphabet is

restricted to consist of only two points. The corresponding optimal distribution yields of

course the highest achievable rates depending on how far the transmission is occurring

with respect to the pilot signals. Sending pilot tones frequently clearly reduces the rates as

a significant portion of the time and power is used to estimate the channel and no

information is conveyed from the transmitter to the receiver. On the other hand, when the

pilots are used very infrequently, the channel estimates at the receiver are of poor quality

and the information rates are low. It is worth mentioning that the numerical results

confirm what one expects regarding the optimal input distribution. Namely, the solution

lies between the extremes of on-off keying (optimal for the IID Rayleigh fading case) and

antipodal signaling (optimal for a perfectly known channel). Indeed, the optimal input

distribution consists of two nonzero masses, the first of which is negative located

between-√ Pand zero, and the second, positive greater than√ P.

3.5. ADAPTIVE BINARY CODING IN DIVERSITY COMMUNICATION

SYSTEMS:

The objective of using Adaptive Binary Coding in Diversity Communication

Systems is to provide a communication system with multiple paths from transmitter to

receiver across the radio channel, that would not only Reduce the Fading Phenomena of

these paths but, also improve the Reliability of the System by utilizing the available

communication channels, by forward error correction codes in an adaptive fashion.

Continuously selecting the path with the best signal quality will result in an improved

communication link. Different ways of accessing the radio channel (diversity) are

possible to obtain uncorrelated paths. This results in an improved signal-to-interference

ratio.

For wireless communication systems the level or amplitude of the received signal

varies due to varying radio-channel characteristics. As a result the status of the radio

23

channel itself is unknown and time varying. If the channel is in a deep fade then errors

might occur in the reception of data. The received signal has a certain probability to fade

below a threshold value. Within a diversity system one or more interfaces to the radio

channel diversity branches are connected to a receiver. If the received signals from each

diversity branch fade independently, then the probability that all received signals will

fade below the threshold value is considerably reduced.

The signals at the output of the diversity circuits vary as a function of time and

location. Circuits that are implemented to compensate for the imperfections of the radio

channel should therefore be adaptive. The application of adaptive diversity techniques is

used for indoor portable communication systems at frequencies around 1 to 2 GHz (GSM,

DECT). Space diversity seems to be the most favorable technique. The received signals

from the diversity branches are selected or combined before they arrive at the detector.

The different combining techniques are compared for a space-diversity receiver. The

measure for ranking them is the diversity gain and array gain, which are introduced in the

same section. Equal-gain combining is chosen as the best performing combining

technique with respect to circuit complexity. The equal-gain combiner is in most studies

and an ideal one that has the ability to combine signals with an arbitrary phase difference.

Depending on the type of system the implementation of the diversity algorithm should be

fast enough to track the variations of the received signals.

3.6. MODULATION USING ADAPTIVE BINARY CODING:Adaptive binary coding and modulation have the motivation is to be able to

transmit with an ASE as close to the MASE as possible, at an average bit-error rate

(BER) which fulfills the desired quality requirements. The schemes are based on dividing

a flat fading channel into time slots where the channel introduces impairments that can be

closely approximated by AWGN. The channel is periodically evaluated at the receiver

and an estimate (prediction) of the future channel state is sent back to the transmitter on a

separate channel (the return channel). The transmitter adapts to the instantaneous channel

quality by choosing between deferent available transmission schemes of varying spectral

e ciencies.ffiWhen using code and modulation techniques designed for AWGN channels of

di erent CSNRs, adaptive schemes may behave in a fashion that allows transmission atff

an ASE close to the MASE.

24

3.6.1. Adaptation Strategy:

In this report we use a rate-discrete transmission scheme which approximates the

Optimal Rate constant power Adaptation (ORA). This implies that the average transmit

power is held constant, and the adaptation is done changing the channel code and

modulation constellation, and thus the spectral e ciency according to the channelffi

condition. The constraint used is that the overall codec should have a BER lower than a

certain target bit error rate, BER, for any given CSNR, except for very low CSNR values,

when the channel will not be used for transmission.

The CSNR (ϒ) can take on all values larger than zero. We divide ϒ {0, 8} into∈

N +1 region (indexed with n ∈ {0, 1... N}) as shown in Fig. 2a. The CSNR will at any

given time fall into one of these regions (often referred to as fading regions).

Figure 3.6.1: (a) Fading Regions (b) Transmission System

The estimator at the receiver determines the CSI, and sends a message to the

receiver indicating which region n the CSNR is most likely to fall into during the next

transmission (see Fig. 2b). For each region there is assigned a component codec, i.e., a

proper chosen channel coding and modulation technique. When the estimated index is n =

0, the channel is in such a bad state that it should not be used for transmission. The

number of component codecs to use (or equivalent, the number of fading regions) should

be so high that the AWGN assumption is a good one over each fading region. However, it

should be low enough for the ACM system to be able to adapt reliably between the

codecs as the channel varies. The performance of these component codes, which must of

course be known, is measured in BER as a function of CSNR. The performance can be

25

approximated by closed form expressions using curve fitting techniques on simulated

data. This is of interest when designing—and analyzing the average BER performance of

—ACM schemes.

Several of the ACM systems that have been presented so far apply trellis coded

modulation. ACM systems using trellis codes are not very sensitive to channel variations

over each transmitted channel symbol block, since only a short sequence of channel

symbols are transmitted between each channel estimate. In this report however we

consider the use of block codes, in particular low-density parity check (LDPC) codes,

which have shown very promising performance on AWGN channels. A more

comprehensive description of LDPC codes are given in Sec. 3. For now it is su cient toffi

say that LDPC codes are block codes, whose promising behavior is based on transmission

of relatively long sequences or blocks of channel symbols, combined with soft decision

iterative decoding of relatively low complexity. We shall combine LDPC codes with

quadrature amplitude modulation (QAM) and phase shift keying (PSK) to produce

codewords of multilevel channel symbols. To fully utilize the error correcting properties

of the LDPC codes we must ensure that the channel stays within one and the same fading

region for the time period, T [s], used to transmit each codeword. For the BER-CSNR

relationship of LDPC codes, MacKay et.al. have found a good closed form

approximation. We have used a slightly modified version of this equation (the

modification being the constant a, which in (was set to 1)

where a, b, c, and d are constants that can be found by using some curve fitting

technique. The thresholds {γ} are dependent on the target BER and can be calculated by

demanding that BER (γn) = BERn for code n, and then using the inverse of the

approximation used to describe the simulated data points

We also define γ N+1 = 8. The performance of the ACM system described above is

measured by the ASE, which is then compared to the MASE to see how far from

optimum the system is operating. The ASE is given by

26

Where Rn is the spectral e ciency of the code used in component codec n, and Pffi

is the probability of that codec being used, i.e., the probability that the CSNR is falling

into fading region n. This probability is given by:

Where m is the Nakagami parameter.

3.7. SYSTEM DESCRIPTION:It is assumed that L fading channels are available for transmission. The L

channels are utilized equally in terms of transmission rate, i.e. an equal number of bps is

transmitted over each channel. Considering a block of K information bits. To utilize the L

channels, the block of K bits will be split into L segments according to the relative

qualities of the channels. Each segment is then encoded into a codeword of length n to be

transmitted over the available channels. This procedure requires obtaining estimates of

the channel quality through a Channel Quality Estimator (CQE) circuit.

The operation of the system can be summarized as follows. Based on the current

information about the quality of each of the L channels, the number of information bits,

and hence the number of check bits that are to be transmitted over each channel is

determined. This is done subject to the constraint that a total of K information bits are

transmitted through the transmission of the L code words. Those channels having a poor

quality are allocated fewer information bits and more check bits, transmitted over each

channel is determined. This is done subject to the constraint that a total of K information

bits are transmitted through the transmission of the L code words. Those channels having

a poor quality are allocated fewer information bits and more check bits, whereas the

better quality channels are allocated an increased number of information bits and a

reduced number of check bits. In other words, the code rates are adjusted to match the

prevailing channel conditions, subject to the constraint of constant average rate.

The L channels are assumed to be slowly-fading Rayleigh channels with AWGN.

During an adaptation period, the ith channel is attenuated by | α |, where α, is a complex

27

Gaussian random variable with zero mean and a variance of one-half for both the real and

imaginary parts, and | • | denotes the envelope, which is Rayleigh distributed in this case.

All the channels are assumed to be identical on the average; i.e., E [ | α i |2 ] = 1. The

channels, however, are not assumed uncorrelated. They are described by the joint

probability density function of the αi‘s. Let αi = {α1, a2, ..., aL}. Then, the probability

density functions of α is expressed as:

f ( α )= 1π L det Kα

exp (−α Kα−1 α¿)

Where (•)* denotes the Hermitian transpose, and Kα is an LxL covariance matrix

with entries (Kα)ij= E [αi αj*] the αi,'s are uncorrelated then Kα will simply reduce to the

identity matrix . Define Eb to be the transmitted energy per information bits and N0 to be

the one-sided power spectral density of the AWGN. Let the L estimates of channel

qualities be denoted by the vector Γ = (γi, γ2,.., γL}, where γi, is the SNR per (information)

bit on the ith channel, defined as (Eb / N0)|αi|2 . Based on Γ, a block of K information bits

will be partitioned into L segments of lengths {k1, k2... kL}. Each segment i, 1 <i < L, will

be encoded into n bits using an (n, ki,) code before transmitting it over the i'h channel.

The throughput of the system, R, is given by:

R=∑i=1

L

k i

n × L= K

n× L

and is kept constant at all times.Let Pi denote the post-decoding bit error probability on the i'h channel. Then the

bit error probability averaged over the L channels is:

P=∑i=1

L

k i Pi

K

The exact equation relating the post-decoding bit error probability to the channel

bit error rate is a function of the code weight structure and the decoding algorithm, and is

unknown for most codes. Various close approximations or bounds are usually involved

for analysis purposes. In this study, the bound is used with slight improvement, to read:

Pi<1n∑i=0

n

min ( j+ ti , n ) .(nj)∈ j(1−∈)n− j

28

Figure 3.7 (a): Block diagram of the system for L=3

Figure 3.7 (b): Distributing K inf. bits for transmission over three channels

Where U is the error correction capability of the code and e, is the bit error rate on

the i'h channel, which is a function of y; for a given modulation scheme. Non-coherent

BFSK is assumed.

Let ƒ (Γ) denote the joint pdf of Γ. The overall average post- decoding bit error

probability of the system is obtained by averaging Equation over ƒ (Γ), that is

P͞ = ʃʃ … ʃ P ׃(Γ )dΓ

The objective of the adaptation process is to minimize Equation subject to the

constraint in Equation. It should be noted that P is a function of k, and s„ and both of

29

them are functions of γi's, the elements of the vector Γ. What is hoped out of the

minimization process is to obtain a set of relations between ki’s minimization process is to

obtain a set of relations between k{s and γj's. Unfortunately, such an analytical solution is

an extremely tedious task due the complexity of the problem. An alternative discrete

optimization is carried out.

The process of error minimization referred to earlier involves (in effect) first

selecting the appropriate L-code set and then allocating its L codes to the appropriate

channels. This amounts to allocating the higher-rate code to the best channel and

continuing down to the point where the lowest-rate code is assigned to the worst channel.

Given a vector Γ, the question of which L-code set yields the best performance can be

determined in a relatively simple manner. As will be shown later, the L dimensional space

of Γ can be neatly partitioned into regions; where in each of these regions a particular L-

code set outperforms other sets. Every new estimate of Γ represents a point in the space;

the region in which this point lies determines the best set to use.

3.8. FLOWCHART AND ALGORITHMS:A flow chart for decoding with channel measurement information is shown in Fig.

3.6. All the decoding algorithms to be discussed will conform to Figure. However, the

actual set of test patterns used in each decoding algorithm will become progressively

smaller. Since these decoding algorithms are to be used with a binary decoder capable of

finding a codeword only when (3) is satisfied, it is possible that no legitimate error pattern

will be found for a given algorithm. Under these circumstances the estimate of the

codeword is given by the received binary sequence despite the fact that this sequence is

surely in error.

30

Figure 3.8: Flowchart for decoding of diversity communication system using adaptive binary coding

3.8.1. A Class of Decoding Algorithms:

In this section a class of decoding algorithms that utilizes the information

provided by the sequence α= α1,α2, … αN is presented. These algorithms are designed to

work with any binary decoder that can correct up to [(d - 1)/2] errors. The binary decoder

will determine the codeword Xm =Xm1, Xm2,Xm3,….XmN which differs in the least

number of places from the received sequence Y = Y1,Y2, . . . YN, pro- vided that this

difference is not greater than [(d - 1)/2]. The sequence that contains a 1 in the places

where Y and Xm differ, i.e., the error sequence, is given by

Zm=Y ⊕Xm=Y 1⊕Xm1 , Y 2⊕X m1 , …, Y N⊕X mN

Where the notation ⊕ represents modulo-2 addition. If we define the binary

weight of a sequence Zm as

31

W ( Zm )=∑i=1

N

Zmi

the function of a binary decoder is to find the codeword, or equivalently the error

sequences, that satisfy

W ( Zm )≤ [( D−1 )/2]

The binary decoder will find a unique codeword if the inequality given by (3) is

satisfied; otherwise, no codeword will be found. (The assumption that the binary decoder

is a bounded distance decoder is for convenience only. The performance of these

decoding algorithms will surely not be degraded if the binary decoder is capable of

finding a codeword when (3) is not satisfied.) A complete4 binary decoder can be

defined as a decoder capable of finding the codeword that satisfies

min W (Y ⊕Xm)

where the range of nz is over all possible codewords. This decoder differs from a

conventional binary decoder given by (3) in that a codeword will always be obtained even

if the error sequence of minimum binary weight has more than [(d - 1)/2] 1s in it.

In a similar manner we can define a complete channel measurement decoder as

one that is capable of finding the codeword that satisfies

min W α ¿)

In this case we are concerned with finding the error pattern Zm = Y ⊕ Xm of

minimum analog weight,’ where the analog weight of a sequence Zm is defined as

W α (Zm )=∑i=0

n

αi Zmi

just as a binary decoder attempts to achieve a performance close to a complete binary

decoder, the channel measurement decoders to be described will attempt to achieve a

performance that is close to that for a decoder that satisfies (5). In Section III we will

show that for certain channels the decoder given by (5) can be made equivalent to a

maximum-likelihood decoder.

The basic concept behind the channel measurement decoding algorithms can be

illustrated with the aid of Fig. 2. A geometric sketch is shown, which illustrates the binary

distance between four codewords XA, XB, Xc, XD, and the received sequence Y. Each

codeword is surrounded by a sphere of radius [(d - 1)/2]. Thus, a unique codeword, or

equivalently a unique error pattern, is obtained by a binary decoder if the received

sequence is within one of these spheres. In our case there is a unique error pattern Z = Y

32

@ XA within the sphere of radius [(d - 1)/2], which surrounds Y. The objective of the

channel measurement decoder is to use a binary decoder to help obtain a relatively small

set of possible error patterns rather than just one error pattern and choose the error pattern

of minimum analog weight as defined by equation.

The set of error patterns considered is obtained by perturbing the received

sequence Y with a test pattern T, which is a binary sequence that contains l’s in the

location of the digits that are to be inverted. By adding this test pattern, modulo-2, to the

received sequence a new sequence

Y’= Y ⊕ T

is obtained and by binary decoding a new error pattern Z` is obtained. The actual error

pattern relative to Y is given by

ZT= T @ Z`

which may or may not be different from the original error pattern depending on whether

or not Y` falls into the sphere of a new codeword. For the class of algorithms under

consideration, the received sequence will always be perturbed within the sphere of radius

d - 1, which surrounds Y.

3.8.1.1 Algorithm 1:

For this particular algorithm a very large set of error patterns is considered. In

fact, we consider the entire set of error patterns within a sphere of radius d - 1 about the

received sequence Y shown in Fig. 2. Thus, all possible error patterns of binary weight

less than or equal to d – 1 are considered. Since the selected error pattern is determined by

its analog weight, not its binary weight, it is quite possible to select an error pattern with

more than [(d - 1)/2] members and thus extend the error correcting capability of

Figure 3.8.1.1: Geometric sketch for decoding with channel measurement information

33

A set of test patterns that are sufficient, but surely not necessary, to generate all

error sequences of binary weight less than d are given by the set of T, which contain [d/2]

l’s in them. Note that the binary decoder is capable of obtaining error patterns of binary

weight up to [(d - 1)/2], which when combined with an appropriate test pattern can yield

any error pattern that has up to (d - 1) members. Needless to say, since there are

( N[ d /2 ])

different test patterns, this method of implementing Algorithm 1 would only be applicable

for codes whose minimum distance is quite small. Actually, a considerable reduction in

the number of test patterns can be achieved by eliminating test patterns that yield identical

error patterns. Nevertheless, one generally would not want to implement this particular

algorithm since there are considerably simpler algorithms that perform almost as well.

However, Algorithm 1 is still of interest since it can be modified (by Theorem III) to

provide a lower bound on a complete channel measurement decoder and does illustrate,

when compared to Algorithms 2 and 3, how the relative performance is affected by

simplifications in decoding procedures.

3.8.1.2. Algorithm 2:

For this algorithm a considerably smaller set of possible error patterns is

considered. Only those error patterns with no more than [(d - 1)/2] errors located outside

the set, which contains the [d/2] lowest channel measurements, are considered. The error

patterns now tested contain no more than (d - 1) errors, but we no longer test all possible

error patterns with (d - 1) or less members. A set of test patterns, which generates all

required error patterns, is given by letting T have any combination of 1 ‘s, which are

located in the [d/2] positions of lowest con- fidence values, i.e., the [d/2] positions with

the lowest channel measurements. Since there are 2[d/2] possible test patterns, including

the all zero pattern, there are at most 2tdi2’ error patterns considered by this decoding

algorithm and generally much less. Note that if the initial error pattern Z has a binary

weight of 1, then all test patterns of binary weight less than or equal to [(d - 3)/2] will

yield ZT = Z.

3.8.1.3. Algorithm 3:

This decoding algorithm is almost identical to Algorithm 2 except the number of

test patterns considered is just [(d/2) + 1] rather than 2 [d/2] each test pattern has i 1’s

located in the i positions of lowest confidence values. For a code with an even value of d,

the values that i takes are given by i = 1,3,…,d - 1 and i = 0, which yields the all-zero test

34

pattern T = 0. When d is an odd number, i = 0,2,4,…, d - 1. This particular algorithm has

the smallest set of possible error patterns and yet has the same asymptotic error

performance as Algorithms 1 and 2. Computer simulated performance of these algorithms

for the Golay code shows that Algorithm 3 is somewhat inferior to Algorithms 1 and 2.

Nevertheless, this particular algorithm requires less computation than the previous

algorithms and thus in some applications may be more desirable. This is especially true

for codes whose minimum distance is large since the number of test patterns grows only

linearly with the minimum distance of the code.

35

4. CONCLUSION

4.1. CONCLUSION:The basic problem in digital communication through a fading channel is that a

large number of errors occur when the channel attenuation is large; i.e., when the channel

is in a deep fade. If we can supply to the receiver two or more replicas of the same

information signal transmitted through independently fading channels, the probability that

all the signal components will fade simultaneously is reduced considerably. If p is the

probability that any one signal will fade below some critical value, than pD is the

probability that all D independently fading replicas of the same signal will fade below the

critical value. There are several ways that we can provide the receiver with D

independently fading replicas of the same information-bearing signal.

For better performance, we may use one of several more complex methods for

combining the independently fading received signals. One that is appropriate for coherent

demodulation and detection requires that the receiver estimate and correct for the

different phase offsets on each of the D received signals after demodulation. Then, the

phase-corrected signals at the outputs of the D demodulators are summed and fed to the

detector. This type of signal combining is called Equal-Gain Combining. If, in addition,

the received signal power level is estimated for each of the D received signals, and the

phase-corrected demodulator outputs are weighted in direct proportion of the received

signal strength (square-root of power level) and then fed to the detector, the combiner is

called a Maximal-Ratio combiner. On the other hand, if orthogonal signals are used for

transmitting the information through D independently fading channels, the receiver may

employ non-coherent demodulation. In such a case the outputs from the D demodulators

may be squared, summed, and then fed to detector. This combiner is called a Square-Law

Combiner.

All these types of combining methods lead to performance characteristics that

result in a probability of error which behaves as KD/ D where KD is a constant that

depends on D, and D is the average SNR/diversity channel. Thus, we achieve an

exponential decrease in the error probability.

It is apparent that a large reduction in SNR/bit is achieved in having D = 2 (dual

diversity) compared to no diversity. A further reduction in SNR is achieved by increasing

the order of diversity to D = 4, although the additional gain from D = 2 to D = 4 is smaller

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than going from D = 1 to D = 2. Beyond D = 4, the additional reduction in SNR is

significantly smaller.

These performance results illustrate that efficient use of transmitter power in a

Rayleigh fading channel can be achieved by using some form of diversity to provide the

receiver with several independently fading signals all carrying the same information. The

types of diversity that we described (time or frequency) are a form of channel coding

usually called repetition coding where the code rate is 1/D. Thus, if each information bit

is transmitted twice in two widely separated time slots or in two widely separated

frequency bands, we have a dual diversity (D = 2) system obtained with a repetition code

of rate Rc = 1/2. However, in general, a nontrivial code of rate 1/2 will yield significantly

better performance if the coded bits are interleaved prior to transmission, so that the

fading on each bit of a code word is statistically independent. In particular, a binary linear

(n, k) code with minimum Hamming distance dmin results in a performance that is

equivalent to a repetition code of diversity dmin when soft-decision decoding is used and

dmin/2 when hard-decision decoding is used. Therefore, for any code rate 1/D, a nontrivial

code can be selected which has a minimum Hamming distance dmin > D and, thus,

provides a larger order of diversity than the corresponding repetition code of the same

rate.

An adaptive forward error correction scheme based on binary codes and operating

over diversity channels has been presented and analyzed in this paper. Based on the

quality of the diversity channels, the code rate over each channel is determined using

discrete optimization of the overall error probability, subject to the constraint of fixed

overall throughput rate. Using binary BCH codes with hard-decision decoding the

proposed system offers a gain of 0.4-1.3 dB over classical diversity systems for diversity

orders of 2-4. Moreover, the proposed system can operate at any desirable throughput rate

which is an added advantage compared to selective diversity systems. The effect of

channel correlation on system performance is similar to that in selection diversity.

4.2 FUTURE SCOPE AND APLICATIONS :The uses of multiple antennas at both transmit and receive results in a multiple-

input multiple-output (MIMO) system. The use of diversity techniques at both ends of the

link is termed space–time coding.

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Today, many Research papers and journals are being published on the

transformation of various Adaptive Diversity concept to combat multi-path fading into

the Space-Time coding concept, that exploit the multi-path effects to increase channel

capacity. This Space-Time coding strives for a joint optimization of advanced signal

processing and coding techniques in combination with the diversity and receiver circuits.

At this moment it is not clear for which type of communication system or product this

technique is most suitable or when it will appear in commercial products. A possible

research project is to extend the simulation tools and measurement set-up such that they

can be used for analyzing various Diversity Systems. In this way a quantitative

comparison between these new systems and the classical diversity systems is possible.

A more advanced measurement set-up could be devised based on an arbitrary

waveform generator. In this way the modulated high frequency signals can be generated

with the arbitrary waveform generator, transmitted across the radio channel and

subsequently received with an analogue to digital convertor or sampling scope. With such

a set-up the propagation effects on the transmitted signals can be analyzed in detail. By

using deep-memory sampling scopes, the received data can be stored on a computer, such

that the performance of different implementations of signal processing methods, like

diversity and equalizing, can be analyzed. The diversity prototype uses a received signal-

strength indicator as a quality signal for the adaptive circuits. If this method is

implemented then the expected behavior and performance of this method in the presence

of interfering systems could be analyzed.

Adaptive Binary Coding for Diversity in communication system is yet to be

implemented on hardware, due to its complexity and ongoing experiments. However the

system shows enormous potential for being used in wireless communication.

It could also be used is in Wi-Fi networking gear and cordless telephones to

compensate for multipath interference. The base station could switch reception to one of

two antennas depending on which it is currently receiving a stronger signal. For

microwave bands, where the wavelengths are under 100 cm, this could often be done with

two antennas attached to the same hardware. For lower frequencies and longer

wavelengths, the antennas must be several meters apart, making it much less reasonable.

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4.3 ADVANTAGES: Adaptive Binary Coded Diversity System has the freedom to avoid using a

channel when it is in deep fade, thus it shows more improvement in the

performance than any other systems.

The Adaptive Binary System can operate at any desirable throughput rate which is

an added advantage compared to selective diversity systems which operates at

fixed rates of 1/L.

The signal to noise ratio ( E0 / N0 ) increases with increasing the diversity order,

thus the error probability ( pe ) decreases.

The system utilizes the available diversity channels by forward error correction

coding in an adaptive fashion to improve the reliability and transmission rate of

the system.

The system reliability is improved without increasing the signal power or the

antenna size.

The system, in implementation terms, reduces to that of selecting the best group of

codes for a given channel estimation. Therefore, the system proposed here may be

viewed as a Wider-Range selective diversity system.

Based on the quality of the diversity channels, the code rate over each channel is

determined using discrete optimization of the overall error probability, subject to

the constraint of fixed overall throughput rate.

As the order of diversity becomes higher, the Bandwidth requirement is lower,

thereby improving the Bandwidth Requirement.

As the number of carrier ( Q ) increases, the symbol duration increases Q times

thereby increasing the spectrum efficiency and capacity.

4.4. LIMITATIONS:Diversity Communication Techniques using Adaptive Binary Coding is still at its

infantry. Various researches and experiments are still being carried out using simulation

programmers. The circuit could still not be implemented on hardware due to its design

complexity.

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