Enhanced Antennae System with OFDM and FRM Technique

Ajay Kr. Singh and Soni Kumari

Department of Computer Science and Engineering Jaypee University of Information Technology, Solan -173 215, India

E-mail:- ajay41274@yahoo.com

Abstract—Accuracy and efficiency multipurpose structural antenna system for ship can be enhanced by using OFDM features and FRM technique. The basic model of OFDM based spatial multiplexing is fit for high data rate transmission which will increase the diversity and gain of the antenna. FRM technique will provide more accurate frequency range; hence this can be used for software defined radio.

Keywords- Frequency Response Masking (FRM), Orthogonal Frequency Division Multiplexing (OFDM), Multiple Input and Multiple Output (MIMO), Filter Impulse Response (FIR).

I. INTRODUCTION Now a day’s Software defined radio (SDR) is being equipped for both civil and tactical purpose having capability to simultaneously receive and transmit multiple waveform of the same or different type with 2MHZ-2GHZ frequency range [1].

Computationally the most intensive and important part of a software defined radio (SDR) is its receiver which channalize it to operates at the highest sampling rate. The channelization process is consisting of digital down conversion (DDC), channel filtering and sample rate conversion (SRC). In this process the channelizer extracts multiple narrowband channels from a wideband input signal using a huge number of channel filters. In the current software radio systems, there are two main structures. One is based on the programmable hardware such as FPGA and DSP and other is based on PCs or workstations, which is called virtual radio. In both systems, digital filter is still an important component of the systems. According to the architecture of the software radio system, signals are usually digitized after IF section. The bandwidth requires high-speed filter and decimation/interpolation functions are also needed. In software radio systems, the flexibility and programmability of the filter are preferred. Therefore, not only the speed achieved and resources consumed, but also the programmability of the filter should be considered during the design of filter. For system based on different hardware, the feasibility, complexity and programmability of the filter are different. Finite-impulse response (FIR) filters are employed as channel filters because of its linear-phase property and guaranteed stability. Low power high-speed and sharp transition-band FIR filters are required in the channelizer to meet the stringent wireless communication specifications. The complexity of filtering operation can be mainly attributed to the number of adders used to realize the coefficient

multiplication operation. Several new approaches have been proposed for reducing the complexity of FIR filters. In a frequency response masking (FRM) technique was proposed for the synthesis of sharp transition-band FIR filters.

II. FRM TECHNIQUE The advantage of FRM technique is that, the bandwidth of the filters are not altered and the resulting filter will have many sparse coefficients resulting in less complex filters. Common Sub expression Elimination (CSE) technique can be employed to remove redundant additions in FIR filters and thus to reduce its complexity. It composes the overall sharp transition-band filter using several wide transition band sub filters [2].

The flowchart of simulation of FRM technique is explained in Fig.1, after applying FRM technique we will get more sharp range of multiple waveforms of the same. Here we can see in the Fig. 2 that the slope has decreased after applying FRM technique. This means that it has inclined towards ideal filter and thus we will get the more sharp range of frequency, resulting in to better result. Here fp is passband filter and fs is stopband filter. Ha(z) is a given prototype of odd length Na and Hc(z) is the complementary filter. HMa(Z) is masking filter [3] . Z Transform converts the signal from time domain to frequency domain. fap and fas are the passband and stopband edges of the modal filter. fmap and fmas are the stopband and passband of the masking filter [4].

III. FORMULAE USED

( 1 ) /

ap p

as s

map p

mas as

f f M m

f f M m

f f

f m f M

= −

= −

=

= + −

( ) /mcp ap

mcs s

f m f M

f f

= −

= modal(( 1) )

2delaysN M

N−

=

Following equations are used to get the desired result.

pm f M= ⎢ ⎥⎣ ⎦

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1 1 1 1 2 2 2 2p p p pf M f M f M f M− = −⎢ ⎥ ⎢ ⎥⎣ ⎦ ⎣ ⎦

⎣ ⎦ ⎣ ⎦1 1 1 1 2 2 2 2s s s sf M f M f M f M− = −

The simulation is done by using MATLAB 7.1 and the graph is obtained which shows fruitful result as shown in Fig. 2.

Figure 1 Flowchart of FRM technique.

IV. OFDM Multiple transmit and receive antennas can be used with orthogonal frequency division multiplexing to improve performance and transmission rate. The basic model of OFDM as shown in Fig. 3(a) and Fig.3(b) based multiplexing is fit for high data rate transmission and if combined with other technique like space time coding can provide required diversity and spatial multiplexing gain. As gain is one of the important attribute of any antenna. By increasing gain naval structural antenna system for broadband HF communication can be enhanced [5, 6].

Figure 2 Simulation of FRM approach.

Algorithm for simulation of OFDM 1. Generate text Data, apply QPSK modulation. 2. Apply inverse fast Fourier transform (IFFT). 3. Guard time interval is inserted. 4. AWGN is added guard time interval is removed. 5. Apply fast Fourier transforms (FFT) then demodulates

parallel data is converted in to serial data. 6. Finally calculate the BER. OFDM has been proposed and discussed in order to mitigate the performance degradation due to multipath interference. The information in channel is encoded and modulated. Guard time is inserted after IFFT. Finally these data is transmitted from one or more antenna. In a typical OFDM receiver, the guard interval (GI) is first removed, and then the information is serial to parallel converted. The converted data is put in to FFT and is afterward demodulated [7, 8]. It is robust to multipath fading because it mitigate multipath fading by transforming a high rate data stream in to low data stream and mapping them on narrow band subcarriers. Bandwidth of each subcarrier is sufficiently narrow within the coherence bandwidth of the channel. This allows a receiver which does not implement multipath canceller or equalizer for multipath mitigation.

V. OFDM BASED MIMO SYSTEM WITH FULL DIVERSITY GAIN

High data rate transmission is combined with other like space time coding can provide desired diversity and spatial multiplexing gain [9]. In OFDM-SM where each antenna transmits different symbol, full diversity gain is not achieved. But in STBC-OFDM by adding space time block code full diversity gain can be achieved at the expense of lower transmission rate [10]. In STBC-OFDM with transmit diversity, a block of input stream, is demuxed to sub stream and modulation the output of each sub stream is then mapped to a space time block coder. In broadband environment combined OFDM and space time processing is an effective method to combat fading and achieve high data rate

Set the values of passband and stopband edges of modal filter and number of delay

Calculate the passband and stopband of overall filter

Calculate the passband and stopband edges of masking filter

Set the length of modal filter

Calculate the number of complementary delays

Display the calculated values

Graph taking number of delays and values for modal filter

Stop

Start

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communication. In OFDM, using IFFT at the transmitter and FFT at the receiver, transforms the broadband frequency selective channel into a set of ISI-free narrow-band sub channels, so that each sub channel can be easily equalized at the receiver.

(a)

(b)

Figure 3 Block diagram space time coded OFDM a) Transmitter b) Receiver.

Figure 4 Plot of fp, fs, fmas and fmcp and ndelay.

Fig. 4 shows the comparison among different parameters of filter. To avoid inter-symbol interference a cyclic prefix is added at the transmitter and removed at the receiver prior to detection. The main problem is that if a subcarrier falls in a deep fade or channel null, all the information carried by that subcarrier is affected and results in an error burst that greatly degrades the system performance. In order to deal with bad sub-carriers, techniques such as channel coding or changing subcarrier positions at the transmitter (which requires commercial system integration (CSI) at the transmitter) can be used. Multicarrier space-time processing allows multi-transmit and multi-receive communication through multiple parallel

sub-channels at high data rate that can provide diversity gain and spatial multiplexing gain. Space times block codes with two transmit antennas or orthogonal codes for arbitrary number of transmit antenna achieve full diversity gain and linear low complexity decoding at the expense of lower transmission rate. Diversity gain is achieved by sending the same information through different paths, whereas spatial multiplexing gain is achieved when each transmit antenna, transmits different information that results in higher transmission rate. In a MIMO OFDM system, both diversity and spatial multiplexing gains can be achieved.

CONCLUSION After applying FRM and OFDM technique which is fit for high data rate transmission we get more efficient and increased diversity gain of the antenna. The future work includes applying amplifier like common base amplifier, a circuit with low input impedance mainly used in high frequency application in multifeed naval structure antennae (NSA) so that we can make even more efficient antenna than what we have at present.

REFERENCES

[1] J. Mitola III, “Technical challenges in the globalization of software radio,” IEEE Comm. Mag., pp. 84-89, Feb. 1999.

[2] R. Mahesh and A. P. Vinod “FRM reconfigurable channel filter for software radio receivers,” Proc. IEEE Int., pp. 4515-45188, May 2007.

[3] A. P. Vinod and E. M.-K. Lai, “On the implementation of efficient channel filters for wideband receivers by optimizing common sub expression elimination methods,” IEEE Trans. Computer-Aided Design Integer Circuits Syst., vol. 24, no. 2, pp. 295–304, Feb. 2005.

[4] Y. C. Lim, “Frequency-response masking approach for the synthesis of sharp linear phase digital filters,” IEEE Trans. Circuits. Syst., vol. 33, no. 4, pp. 357–364, Apr. 1986.

[5] Richard Van Nee and R. Prasad, “OFDM for wireless multimedia communications,” Artech house Boston London. 2000.

[6] L. J. Cimini, Jr., “Analysis and simulation of a digital mobile channel using orthogonal frequency division multiplexing,” IEEE Trans. Comm., COM-33, no. 7, pp. 665–675, 1985.

[7] G. J. Foschini, “Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas,” Bell Labs Tech. J., vol. 1, no. 2, pp. 41–59, 1996.

[8] P. W. Wolniansky, G. J. Foschini, G. D. Golden and R. A. Valenzuela, “V-BLAST: An architecture for realizing very high data rates over the Rich-scattering wireless channel,” Presented at the ISSSE, Sept. 1988.

[9] Y. Li, N. Seshadri, and S. Ariyavisitakul, “Channel estimation for OFDM systems with transmitter diversity in mobile wireless channels,” IEEE. Conf., vol. 17, pp. 461-471, Mar. 1999.

[10] V. Tarokh, H. Jafarkhani and A. R. Calderbank, “Space time block coding for wireless communications: performance results,” IEEE J. Selected Areas in Comm., vol. 17, pp. 451-460, Mar. 1999.

Space Time Coder

IFFT

IFFT

Add Guard

Add Guard

Input Signal

Space Time

DeCoder

FFT

FFT

Remove Guard

Remove Guard

Output Signal

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