ARESEARCH PROPOSALOnMIMOWireless System Technology

Submitted toCultural & Public Relations DepartmentEmbassy of Japan

Submitted byDeep Raj BhujelBachelors Degree in EngineeringElectronics and Communication Engineering

30-May-2013ABSTRACT

Dead spots are everywhere. They're those areas of your home or office where, no matter how you position your router or how you point the antenna, you just can't get a Wi-Fi signal. Almost any Wi-Fi connection, even a weak one, is sufficient to surf the Internet or transfer data. But if distance and obstacles sap too much bandwidth from a network, video images will start to stutter and break up. Video is what's causing this problem on range and higher speed. The cure for the problem, is an innovation called MIMO, short for multiple input, multiple output .The new technology, uses a number of antennas to send multiple signals as a way to significantly increase the speed and range of a wireless network. In tests, it is found that MIMO nearly doubled the speed and provided superior range. Multiple-Input / Multiple Output (MIMO) technology has emerged in the last decade as a powerful means of increasing the throughput and performance of wireless communication systems. Research on this relatively new technology has penetrated in a substantial way many fields, ranging from signal processing to information / communication theory to wireless propagation. Equally importantly, MIMO technology has made its way into current and next generation communication standards and systems. In this paper, I will provide an overview of MIMO systems, starting with the fundamentals of capacity, random channels, basic transceiver architectures, diversity, space-time coding and channel estimation. I will then review some more recent results in the areas of diversity versus multiplexing trade-offs, input optimization / precoding, fundamental limits of coherent operation and multi-user MIMO, including systems with interference. Finally we will cover certain applications of MIMO techniques in current wireless systems.

TABLE OF CONTENTS

ABSTRACT ....iTABLE OF CONTENTS ...iiLIST OF FIGURES ..iiiLIST OF TABLES iiiLIST OF SYMBOLS AND ABBREVIATIONS iv1. MOTIVATION AND OBJECTIVES ....11.1 Motivation .....11.2 Objectives ..12. LITERATURE REVIEW ...32.1 Introduction ...32.2 Concept of MIMO .....52.3 Principle .....72.4 How It Works 82.5 Channel Capacity ...92.6 Antenna Selection ..92.7 Outage Capacity ...103. MIMO APPLICATIONS IN 3G WIRELESS SYSTEMS AND BEYOND 113.1 Background ...113.2 MIMO in 3G Wireless Systems and Beyond ....114. ACTIVITIES ...134.1 MIMO testing ....135. OUTCOMES ..155.1 Applications of MIMO .156. CONCLUSIONS AND FUTURE TRENDS .167. REFERENCES ...17

LIST OF FIGURESFigure 1 : A Multi-channel Network 3Figure 2 : Understanding of SISO, SIMO, MISO and MIMO ...6Figure 3 : Working of MIMO ..8Figure 4 : MIMO channel model 13

LIST OF TABLESTable 1 : Peak Data Rates of Various MIMO Architectures ..11

LIST OF SYMBOLS AND ABBREVIATIONSMIMO .Multiple Input Multiple OutputSISO ...Single Input Single OutputSIMO .Single Input Multiple OutputMISO .Multiple Input Single OutputQoS Quality of ServiceWLAN ...Wireless Local Area NetworkIEE .Institute of Electrical and Electronics EngineersWiMAX .Worldwide Interoperability for Microwave AccessHSDPA...High-Speed Digital Packet AccessHSDPA+.High-Speed Digital Packet Access plusMEAs..Multiple-Element Antenna systemsARQ ...Automatic Repeat Request3GPP ..3rd Generation Partnership ProjectOFDM.Orthogonal Frequency Division MultiplexingOFDMA..Orthogonal Frequency Division Multiple AccessRRC.Radio Resource ControlCSI..Channel State InformationITU..International Telecommunication UnionBLAST Basic Local Alignment Search ToolLTE .Long Term EvolutionPARPeak-to-Average RatioVSGVector Signal GeneratorVSAVector Signal AnalyzerTX ..TransmitterRX ..ReceiverIST-MASCOT ...Information Society Technologies Multiple Access Space-Time Coding Testbed

1. MOTIVATION AND OBJECTIVES1.1 MotivationComprehensive broadband, integrated mobile communication will step into all mobile 4G service and communication. The 4G will be the migration from the other generation of mobile services to overcome the limitation of boundary and achieve the integration. The 4G of mobile services aims to total wireless.

The 4G will be developed to provide high speed transmission, next generation internet support, seamless integrated services and coverage, utilization of higher frequency, lower system cost, seamless personal mobility, mobile multimedia, sufficient spectrum use, quality of service (QoS), reconfigurable network and end-to-end IP systems.

In conventional wireless communication, a single antenna is used at the source and another antenna is used at destination. In many cases, it gives rise to problem with multipath fading, making difficult to meet promises aim by the 4G.

The solution to multipath fading can be solved using MIMO technology. The following paper will outline the concept of MIMO technology and why its superior to present the present day technology. That is the reason I want my research on MIMO technology exploration and advancement to make the wireless service hassle-free and easier.

1.2 ObjectivesMIMO technology promises higher data rate, higher quality of service and better reliability by exploiting antenna array at both the transmitter and the receiver. Signals at both sides (transmitter and receiver) are mixed such that they either generate multiple parallel, spatial bit pipes and/or add diversity to decrease the bit-error rate.

Diversity helps in selecting the clearest signal out of many signals, resulting in lower bit-error rate. Multiple bit pipes effectively increase the data rate (quantitative improvement), whereas the reduced bit-error rate improve the quality of service, throughput and reliability (qualitative improvement).

The fundamental gain in MIMO is increased data rate. Why not use more bandwidth or complex modulation scheme to increase the data rate? The use of more bandwidth depends upon the availability of spectrum and again the use may be difficult to meet the spectral efficiency. All wireless devices use a particular part of radio spectrum. Air traffic radar, for example, operates between 960 and 1215 megahertz and cellphone between 824 to 849 megahertz. As growing number of wireless devices enter the consumer market, the spectrum becomes congested every year. MIMO has potential to expand radio capacity and relieve the burden on existing bandwidth.

By spreading the transmitted signal over the multiple paths, the MIMO technology increases the chances of signal reception at receiver. It also increases the range of operation.

Multipath fading causes the distortion by scrambling the copy of the signals reaching the receiver via multiple paths on bouncing of the objects. Then how does the multipath signals work in MIMO? Proper algorithms are used at both the transmitter and receiver to analyses the signal received from different path and different antenna of array.

Proper spacing of antenna and signal analysis via a matrix manipulation technology that cross-correlate the signals are the requirement of MIMO technology.

2. LITERATURE REVIEW2.1 IntroductionMultiple Input Multiple Output (MIMO) is a smart antenna technique that increases speed, range, reliability and spectral efficiency for wireless systems. Given the demands thatapplicationsare placing on WLANs, MIMO chipsets will figure prominently in new access points and network interface cards. MIMO is one technology being considered a standard for next-generation that boosts throughput to 100 Mbit/sec. In the meantime, proprietary MIMO technology improves performance of existingnetworks.

A conventional radio (or telephony) uses one antenna to transmit a DataStream as shown in figure 1. A typical smart antenna radio, on the other hand, uses multiple antennas. This design helps combat distortion and interference. Examples of multiple-antenna techniques include switched antenna diversity selection, radio-frequency beam forming, digital beam forming and adaptive diversity combining.Figure 1 : A Multi-channel NetworkThese smart antenna techniques are one-dimensional, whereas MIMO is multi-dimensional. It builds on one-dimensional smart antenna technology by simultaneously transmitting multiple data streams through the same channel, which increases wireless capacity.

You can think of conventional radio (or telephony) transmission as traveling on a one-lane highway. The speed limit governs the maximum allowable flow of traffic through that lane. Compared with conventional radios, one-dimensional smart antenna systems help move traffic through that lane faster and more reliably so that it travels at a rate closer to the speed limit. MIMO helps traffic move at the speed limit and opens more lanes. The number of lanes that are opened as shown in figure 1 multiplies the rate of traffic flow.

A characteristic of radio transmission called multipath, which had previously been considered an impairment to radio transmission, is actually a gift of nature. Multipath occurs when signals sent from a transmitter reflect off objects in the environment and take multiple paths to the receiver. The researchers showed that multipath could be exploited to multiplicatively increase the capacity of a radio system.

If each multipath route could be treated as a separate channel, it would be as if each route were a separate virtual wire. A channel with multipath then would be like a bundle of virtual wires.

To exploit the benefits the virtual wires offer, MIMO uses multiple, spatially separated antennas. MIMO encodes a high-speed DataStream across multiple antennas. Each antenna carries a separate, lower-speed stream. Multipath virtual wires are utilized to send the lower-speed streams simultaneously.

But wireless is not as well behaved as a bundle of wires. Each signal transmitted in a multipath environment travels multiple routes. This makes a wireless system act like a bundle of wires with a great deal of leakage between them, causing transmitted signals to jumble together. The MIMO receiver uses mathematical algorithms to unravel and recover the transmitted signals.

2.2 Concept of MIMOIn radio, MIMO (commonly pronounced my-moh or me-moh), is the use of multiple antennas at both the transmitter and receiver to improve communication performance. It is one of several forms of smart antenna technology. Note that the terms input and output refer to the radio channel carrying the signal, not to the devices having antennas.

MIMO technology has attracted attention in wireless communications, because it offers significant increases in data throughput and link range without additional bandwidth or increased transmit power. It achieves this goal by spreading the same total transmit power over the antennas to achieve an array gain that improves the spectral efficiency (more bits per second per hertz of bandwidth) and/or to achieve a diversity gain that improves the link reliability (reduced fading). Because of these properties, MIMO is an important part of modern wireless communication standards such as IEEE 802.11n (Wi-Fi), 4G, 3GPP Long Term Evolution, WiMAX and HSPA+.

Wireless channels input and output modulated signals. For the purpose of modulation, the two basic things are considered are frequency and time. The frequency plan and time plan use bits per hertz and bits per second as measures for data rate transportation.

A new dimension to upgrade the data transportation rate is spatial dimension. This is the concept behind MIMO technology.

MIMO technology may be seen as an upgrade of SIMO and MISO. All three technologies namely SIMO, MISO and MIMO use multipaths for increasing data rate, throughput and reliability. Multiple paths are used by multiple transmit antenna and multiple receiver antenna.

Multiple antennas at one end either at transmitter or at the receiver were in use long ago. The then use of multiple antennas aimed at beam forming and spatial diversity, which are mainly used to increase the signal to noise ratio. The improved signal to noise ratio decreases the bit-error rate.

The use of multiple antennas adds the new dimension to digital communication technology which forms the basis of 3G and 4G. The natural dimension of digital technology is time. Added with that, MIMO offers a new space time axis to digital technology. MIMO is therefore termed as space time wirelesses or smart antenna.Digital MIMO is called volume to volume wireless links as it offers parallel bit pipes between transmitter and the receiver.

Figure 2 : Understanding of SISO, SIMO, MISO and MIMO (note that the terms input and output refer to the radio channel carrying the signal, not to the devices having antennas)

2.3 PrincipleThe increase in spectral efficiency offered by MIMO systems is based on the utilization of space (or antenna) diversity at both the transmitter and the receiver. Due to the utilization of space diversity, MIMO systems are also referred to as MEAs. With a MIMO system, the data stream from a single user is de-multiplexed into nT separate sub-streams. The number nT equals the number of transmit antennas. Each sub-stream is then encoded into channel symbols. It is common to impose the same data rate on all transmitters, but adaptive modulation rate can also be utilized on each of the sub-streams. The signals are received by nR receive antennas. With this transmission scheme, there is a linear increase in spectral efficiency compared to a logarithmic increase in more traditional systems utilizing receive diversity or no diversity. The high spectral efficiencies attained by a MIMO system are enabled by the fact that in a rich scattering environment, the signals from each individual transmitter appear highly uncorrelated at each of the receive antennas. When the signals are conveyed through uncorrelated channels between the transmitter and receiver, the signals corresponding to each of the individual transmit antennas have attained different spatial signatures. The receiver can use these differences in spatial signature to simultaneously and at the same frequency separate the signals that originated from different transmit antennas.

Figure 3 : Working of MIMO2.4 How It WorksThe MIMO system uses multiple antennas to simultaneously transmit data, in small pieces to the receiver, which can process the data flows and put them back together. This process, called spatial multiplexing, proportionally boosts the data-transmission speed by a factor equal to the number of transmitting antennas. In addition, since all data is transmitted both in the same frequency band and with separate spatial signatures, this technique utilizes spectrum very efficiently (Refer to figure 3)

2.5 Channel CapacityAt the input of a communication system, discrete source symbols are mapped into a sequence of channel symbols. The channel symbols are then transmitted/ conveyed through a wireless channel that by nature is random. In addition, random noise is added to the channel symbols. In general, it is possible that two different input sequences may give rise to the same output sequence, causing different input sequences to be confusable at the output. To avoid this situation, a non-confusable subset of input sequences must be chosen so that with a high probability, there is only one input sequence causing a particular output. It is then possible to reconstruct all the input sequences at the output with negligible probability of error. A measure of how much information that can be transmitted and received with a negligible probability of error is called the channel capacity.

2.6 Antenna SelectionThe MIMO channel capacity has so far been optimized based on the assumption that all transmit and receive antennas are used at the same time. Recently, several authors have presented papers on MIMO systems with either transmit or receive antenna selection. The capacity of the MIMO channel reduces with a rank deficient channel matrix. A rank deficient channel matrix means that some columns in the channel matrix are linearly dependent. When they are linearly dependent, they can be expressed as a linear combination of the other Columns in the matrix. The information within these columns is then in some way redundant and is not contributing to the capacity of the channel. The idea of transmit antenna selection is to improve the capacity by not using the transmit antennas that correspond to the linearly dependent columns, but instead redistributing the power among the other antennas. Since the total number of parallel sub channels is equal to the rank of the channel matrix, the optimal choice is to distribute the transmit power on a subset of k transmit antennas that maximizes the channel capacity. The optimal choice of k transmits antennas that maximize the channel capacity results in a channel matrix that is full rank. In, a computationally efficient, near-optimal search technique for the optimal subset based on classical water pouring is described.

2.7 Outage CapacityIn this paper, the ergodic (mean) capacity has been used as a measure for the spectral efficiency of the MIMO channel. The capacity under channel ergodicity is defined as the average of the maximal value of the mutual information between the transmitted and the received signal, where the maximization was carried out with respect to all possible transmitter statistical distributions. Another measure of channel capacity that is frequently used is outage capacity. With outage capacity, the channel capacity is associated to an outage probability. Capacity is treated as a random variable, which depends on the channel instantaneous response and remains constant during the transmission of a .nite-length coded block of information. If the channel capacity falls below the outage capacity, there is no possibility that the transmitted block of information can be decoded with no errors, whichever coding scheme is employed. The probability that the capacity is less than the outage capacity denoted by Coutage is q. This can be expressed in mathematical terms by

Prob {C = Coutage} = q.

In this case, represents an upper bound due to fact that there is a .nite probability q that the channel capacity is less than the outage capacity. It can also be written as a lower bound, representing the case where there is a .nite probability (1 - q) that the channel capacity is higher than Coutage, i.e., Prob {C > Coutage} = 1- q.

3. MIMO APPLICATIONS IN 3G WIRELESS SYSTEMS AND BEYOND3.1 BackgroundWith MIMO-related research entering a maturing stage and with recent measurement campaign results further demonstrating the benefits of MIMO channels, the standardization of MIMO solutions in third generation wireless systems (and beyond) has recently begun. Several techniques, seen as complementary to MIMO in improving throughput, performance and spectrum efficiency are drawing interest, especially as enhancements to present 3G mobile systems, e.g., HSDPA. These include adaptive modulation and coding, hybrid ARQ, fast cell selection, transmit diversity.

Table 1 : Peak Data Rates of Various MIMO Architectures

3.2 MIMO in 3G Wireless Systems and BeyondThere is little commercial implementation of MIMO in cellular systems as yet and none is currently being deployed for 3G outside pure transmit diversity solutions for MISO. Current MIMO examples include the Lucents BLAST chip and proprietary systems intended for specific markets such as Iospan Wireless Airburst system for fixed wireless access. The earliest lab trials of MIMO have been demonstrated by Lucent Technologies several years ago. In the case of 3GPP, some MIMO results are presented here.

Based on link level simulations of a combination of V-Blast and spreading code reuse. Table 1 gives the peak data rates achieved by the down link shared channel using MIMO techniques in the 2-GHz band with a 5-MHz carrier spacing under conditions of flat fading. The gains in throughput that MIMO offer are for ideal conditions and are known to be sensitive to channel conditions. In particular, the conditions in urban channels that give rise to uncorrelated fading amongst antenna elements are known to be suitable for MIMO. The gains of MIMO come at the expense of increased receiver complexity both in the base station and in the handsets. Also various factors such as incorrect channel estimation, presence of correlation amongst antenna elements, higher Doppler frequencies, etc., will tend to degrade the ideal system performance.

4. ACTIVITIES

Figure 4 : MIMO channel model4.1 MIMO testing MIMO signal testing focuses first on the transmitter/receiver system. The random phases of the sub-carrier signals can produce instantaneous power levels that cause the amplifier to compress, momentarily causing distortion and ultimately symbol errors. Signals with a high PAR can cause amplifiers to compress unpredictably during transmission. OFDM signals are very dynamic and compression problems can be hard to detect because of their noise-like nature. Knowing the quality of the signal channel is also critical. A channel emulator can simulate how a device performs at the cell edge, can add noise or can simulate what the channel looks like at speed. To fully qualify the performance of a receiver, a calibrated transmitter, such as a VSG, and channel emulator can be used to test the receiver under a variety of different conditions. Conversely, the transmitter's performance under a number of different conditions can be verified using a channel emulator and a calibrated receiver, such as a VSA.

Understanding the channel allows for manipulation of the phase and amplitude of each transmitter in order to form a beam. To correctly form a beam, the transmitter needs to understand the characteristics of the channel. This process is called channel sounding or channel estimation. A known signal is sent to the mobile device that enables it to build a picture of the channel environment. The mobile device sends back the channel characteristics to the transmitter. The transmitter can then apply the correct phase and amplitude adjustments to form a beam directed at the mobile device. This is called a closed-loop MIMO system. For beamforming, it is required to adjust the phases and amplitude of each transmitter. In a beamformer optimized for spatial diversity or spatial multiplexing, each antenna element simultaneously transmits a weighted combination of two data symbols.

5. OUTCOMES5.1 Applications of MIMO Spatial multiplexing techniques make the receivers very complex, and therefore they are typically combined with OFDM or with OFDMA modulation, where the problems created by a multi-path channel are handled efficiently. The IEEE 802.16e standard incorporates MIMO-OFDMA. The IEEE 802.11n standard, released in October 2009, recommends MIMO-OFDM.

MIMO is also planned to be used in Mobile radio telephone standards such as recent 3GPP and 3GPP2. In 3GPP, HSPA+ and LTE standards take MIMO into account. Moreover, to fully support cellular environments, MIMO research consortia including IST-MASCOT propose to develop advanced MIMO techniques, e.g., multi-user MIMO (MU-MIMO).

MIMO technology can be used in non-wireless communications systems. One example is the home networking standard ITU-T G.9963, which defines a powerline communications system that uses MIMO techniques to transmit multiple signals over multiple AC wires (phase, neutral and ground).

6. CONCLUSIONS AND FUTURE TRENDSThis paper reviews the major features of MIMO links for use in future wireless networks. It is clear that the success of MIMO integration into commercial standards such as 3G, WLAN, and beyond will rely on a fine compromise between rate maximization (BLAST type) and diversity (spacetime coding) solutions, also including the ability to adapt to the time changing nature of the wireless channel using some form of (at least partial) feedback. To this end more progress in modeling, not only the MIMO channel but also its specific dynamics, will be required. As new and more specific channel models are being proposed, it will be useful to see how those can affect the performance tradeoffs between existing transmissions and whether new, tailored to specific models, can be developed. Finally, upcoming trials and performance measurements in specific deployment conditions will be key to evaluate precisely the overall benefits of MIMO systems in real-world wireless scenarios.

7. REFERENCESi. http://en.wikipedia.org/wiki/MIMOii. http://www.nari.ee.ethz.ch/wireless/research/projects.htmliii. http://www.howstuffworks.com/iv. www.comsoc.comv. http://www.networkworld.com/topics/wireless.htmlvi. http://www.youtube.com/watch?v=VLAgYUQCgD8vii. George V. Tsoulos, MIMO System Technology for Wireless Communications, CRC Press, Taylor & Francis Group, 2006viii. http://www.swatijaininst.com/