Near-capacity Multi-functional MIMO Systems: Sphere-packing, Iterative Detection and Cooperation: Near-capacity Sphere-packing, Multi-functional MIMOs ... Space-time Processing - Hardcover

Hanzo, Lajos; Alamri, Osamah; El-Hajjar, Mohammed; Wu, Nan

Hardcover

ISBN 10: 0470779659 ISBN 13: 9780470779651

Publisher: Wiley-IEEE Press, 2009

Synopsis

Providing an all-encompassing self-contained treatment of Near-Capacity Multi-Functional MIMO Systems , the book starts by categorizing the family of Multiple-Input Multiple-Output (MIMO) schemes as diversity techniques, multiplexing schemes, multiple access arrangements and beam-forming techniques. Sophisticated coherent and low-complexity non-coherent MIMO receivers dispensing with channel estimation are considered in both classic and cooperation-aided scenarios. It is demonstrated that in the presence of correlated shadow-fading, cooperation-assisted systems may be expected to outperform their non-cooperative counterparts. The book contains a 100-page chapter on the unified treatment of all block codes in the context of high-flexibility, cutting-edge irregular Linear Dispersion Codes (LDC), which approach the MIMO-capacity. The majority of the book’s solutions are in the optimum sphere-packing frame-work.

• Sophisticated amalgam of five year’s near-capacity MIMO research
• Detailed examination of wireless landscape, including the fields of channel coding, spacetime coding and turbo detection techniques
• Novel tool of Extrinsic Information Transfer Charts (EXIT) used to address recent developments
• Material presented logically, allowing advanced readers to turn directly to any specific chapter of interest
• One of the only books to cover these subjects, giving equal weighting to each

About this title

About the Author

Lajos Hanzo FREng, FIEEE, FIET, DSc received his degree in electronics in 1976 and his doctorate in 1983. During his 31-year career in telecommunications he has held various research and academic posts in Hungary, Germany and the UK. Since 1986 he has been with the School of Electronics and Computer Science, University of Southampton, UK, where he holds the chair in telecommunications. He has co-authored 17 books on mobile radio communications totaling in excess of 10 000 pages, published in excess of 800 research papers, acted as TPC Chair of several IEEE conferences, presented keynote lectures and been awarded a number of distinctions. Currently he is directing an academic research team, working on a range of research projects in the field of wireless multimedia communications sponsored by industry, the Engineering and Physical Sciences Research Council (EPSRC) UK, the European IST Programme and the Mobile Virtual Centre of Excellence (VCE), UK. He is an enthusiastic supporter of industrial and academic liaison and he offers a range of industrial courses. He is also an IEEE Distinguished Lecturer as well as a Governor of both the IEEE ComSoc and the VTS. He is the acting Editor-in-Chief of the IEEE Press. For further information on research in progress and associated publications please refer to http://www-mobile.ecs.soton.ac.uk.

Osamah Rashed Alamri received his BS degree with first class honours in electrical engineering from King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, in 1997, where he was ranked first with a 4.0 GPA. In 2002, he received his MS degree in electrical engineering from Stanford University, California, USA. He submitted his PhD thesis in October 2006 and published in excess of 20 research papers while working towards his PhD degree with the Communications Group, School of Electronics and Computer Science, University of Southampton, UK. His research interests include sphere packing modulation, space-time coding, turbo coding and detection, multi-dimensional mapping and MIMO systems. At the time of writing he is continuing his investigations as a post-doctoral researcher.

Mohammed El-Hajjar received his BEng degree (with distinction) in electrical engineering from the American University of Beirut (AUB), Lebanon, and his MSc degree (with distinction) in radio frequency communication systems from the University of Southampton, UK. Since October 2005, he has been working towards his PhD degree with the Communications Group, School of Electronics and Computer Science, University of Southampton, UK. He is the recipient of several academic awards from the AUB as well as the University of Southampton. His research interests include sphere packing modulation, space-time coding, differential space-time spreading, adaptive transceiver design and cooperative communications. In 2008 he completed his PhD thesis and joined Ensigma in Chepstow, Wales, UK, as a wireless system architect.

Nan Wu received his BEng in electronics engineering in 2003 from Dalian University of Technology, China. He then moved to the UK and received his MSc degree (with distinction) and PhD from the University of Southampton,UK in 2004 and 2008, respectively.His research interests are in the area of wireless communications, including space-time coding, channel coding and cooperative MIMO systems. In September 2008 he joined the National Institute of Standards and Technology (NIST) in the USA as a guest researcher working on cross-layer designs.

• Sophisticated amalgam of five year’s near-capacity MIMO research
• Detailed examination of wireless landscape, including the fields of channel coding, spacetime coding and turbo detection techniques
• Novel tool of Extrinsic Information Transfer Charts (EXIT) used to address recent developments
• Material presented logically, allowing advanced readers to turn directly to any specific chapter of interest
• One of the only books to cover these subjects, giving equal weighting to each

• Sophisticated amalgam of five year’s near-capacity MIMO research
• Detailed examination of wireless landscape, including the fields of channel coding, spacetime coding and turbo detection techniques
• Novel tool of Extrinsic Information Transfer Charts (EXIT) used to address recent developments
• Material presented logically, allowing advanced readers to turn directly to any specific chapter of interest
• One of the only books to cover these subjects, giving equal weighting to each

Excerpt. © Reprinted by permission. All rights reserved.

Near-Capacity Multi-Functional MIMO Systems

Sphere-Packing, Iterative Detection and CooperationBy Lajos Hanzo Osamah Alamri Mohammed El-Hajjar Nan Wu

John Wiley & Sons

Copyright © 2009 John Wiley & Sons, Ltd
All right reserved.
ISBN: 978-0-470-77965-1

Chapter One

Problem Formulation, Objectives and Benefits

The objective of this light-hearted introductory chapter is to provide a brief rudimentary exposure of the pivotal aspects of the book. Our treatment in this chapter is conceptual, rather than mathematically motivated, with the objective of characterizing the attainable diversity gains, multiplexing gains and beamforming gains. All issues touched upon in this chapter are revisited in a more rigorous mathematical approach in the remaining chapters.

Digital communication exploiting Multiple-Input Multiple-Output (MIMO) wireless channels has recently attracted considerable attention as one of the most significant technical breakthroughs in modern communications. Soon after its invention, the technology seems to have the potential to be part of large-scale standards-driven commercial wireless products and networks such as broadband wireless access systems, Wireless Local Area Networks (WLANs), third-generation (3G) networks and beyond. The 3G systems are expected to have the capability to support circuit and packet data at high bit rates. Rates of 144 kbit [s.sup.-1] or higher in high mobility (vehicular) traffic, 384 kbit [s.sup.-1] for pedestrian traffic and 2 Mbit [s.sup.-1] or higher for indoor traffic are targeted. Wireless systems that employ multiple antennas provide a promising platform for achieving such high rates because of the improved bit/symbol capacity compared with the Single-Input Single-Output (SISO) systems.

As shown in Figure 1.1, MIMO systems can be defined as wireless communication systems for which the transmitting end as well as the receiving end is equipped with multiple antenna elements. The basic concept of MIMO is that the transmitted signals from all transmit antennas are combined at each receive antenna element in such a way as to improve the Bit Error Rate (BER) performance or the data rate (bit [s.sup.-1]) of the transmission. Both the network's Quality of Service (QoS) and the operator's revenues can be increased significantly because of this advantage of MIMO systems. One can think of MIMO systems as an extension to smart antennas. However, the idea of using antenna arrays for improving the wireless transmission was introduced several decades ago.

Space-Time Processing (STP) is the core concept of MIMO systems. Time is the natural dimension of digital communication data. Space refers to the spatial dimension inherent in the use of multiple spatially distributed antennas. Most of the current interest in SpaceTime Coding (STC) is driven by discoveries in the late 1980s and early 1990s that multiple antennas can exploit a rich wireless scattering environment and benefit from the multipath fading nature of the wireless channel. Current research mostly focuses on channel modeling and measurement, and on the design of modulation and coding techniques that take into consideration the two-dimensional nature of STP (i.e. the space and time dimensions).

1.1 The Wireless Channel and the Concept of Diversity

The key characteristics of the mobile radio channel in contrast to the Gaussian channel are small-scale fading and multipath propagation. Small-scale fading, which is usually simply called fading, refers to the rapid fluctuation of signal strength over a short travel distance or period of time. Fading is primarily caused by multipath propagation of the transmitted signal, which creates replicas of the transmitted signal that arrive at the receiver with different delays. These versions of the transmitted signal combine either constructively or destructively at the receiver resulting in fluctuations in the amplitude and phase of the resultant signal. Other factors that have an influence on the small-scale fading include velocity of the mobile station, speed of the surrounding objects and the transmission bandwidth of the signal. During severe fading, the transmitted signal cannot be determined by the receiver unless some less-attenuated version of it is available. This usually can be achieved by introducing some sort of diversity in the transmitted signal. The three most common diversity techniques are as follows.

Temporal diversity. An example of temporal diversity is channel coding with time interleaving. The receiver is provided with several versions of the transmitted signal as redundancy in the temporal domain.

Frequency diversity. This type of diversity is based on the phenomenon that the structure of multipath propagation depends on the frequency of the transmitted wave. Thus, redundancy in the frequency domain provides the receiver with several replicas of the transmitted signal that experience different fading at any particular time instant.

Antenna or space diversity. In order to create space diversity, several spatially separated or differentially polarized antennas are employed. This generates redundancy of the transmitted signal in the spatial domain, where each replica undergoes a different propagation path. In this context, diversity order refers to the number of decorrelated spatial branches available at the transmitter or receiver, where the probability of losing a signal decreases exponentially with increasing diversity order.

It is always desirable to employ all forms of diversity in order to combat the adverse effects of the wireless channel. However, it is sometimes impractical to employ a particular type of diversity in a specific situation. For example, temporal diversity is ineffective in slow fading channels especially for delay-sensitive applications. In addition, antenna diversity at the mobile unit induces design impracticality. The most common systems that employ different types of diversity techniques for the sake of improving the performance of wireless transmission/reception are STC and MIMO schemes. Next, a brief historical overview on STC and MIMO systems is presented summarizing the main contributions in this field.

1.2 Diversity and Multiplexing Trade-offs in Multi-functional MIMO Systems

1.2.1 Classification of MIMO Systems

Again, our objective in this light-hearted section is to provide a brief conceptual overview of the material discussed in significantly more detail in Parts I and III of the book. More specifically, we briefly consider the design alternatives of different MIMO schemes, while considering the attainable diversity gains, multiplexing gains and beamforming gains. Our easy-reading conceptual treatment in this section aims to avoid the rigor of mathematics, which is left for the detailed approach of the remaining chapters.

Here we would like to commence with a brief classification of different MIMO schemes, which are categorized as diversity techniques, multiplexing schemes, multiple access arrangements and beamforming techniques. We then introduce two multi-functional MIMO families. These multi-functional MIMOs are capable of combining the benefits of several MIMO schemes and hence they attain an improved performance in terms of both their BER and their throughput. The first multi-functional MIMO family represents the recently proposed Layered Steered Space-Time Codes (LSSTCs), which combine the triple benefits of Space-Time Block Codes (STBCs), Vertical Bell Labs Layered Space-Time (V-BLAST) schemes and beamforming. The other multi-functional MIMO scheme is referred to as Layered Steered Space-Time Spreading (LSSTS) and combines the benefits of Space-Time Spreading (STS), V-BLAST and beamforming with those of the generalized Multicarrier Direct Sequence Code Division Multiple Access (MC DS-CDMA). We also compare the attainable diversity, multiplexing and beamforming gains of the different MIMO schemes in order to document the advantages of the multi-functional MIMOs over conventional MIMO schemes.

Recently, there has been a growing demand for flexible and bandwidth-efficient transceivers capable of supporting the explosive expansion of the Internet and the continued dramatic increase in demand for high-speed multimedia wireless services. Advances in channel coding made it feasible to approach Shannon's capacity limit in systems equipped with a single antenna, but fortunately these capacity limits can be further extended with the aid of multiple antennas. Recently, MIMO systems have attracted considerable research attention and are considered as one of the most significant technical breakthroughs in contemporary communications.

Explicitly, the MIMO schemes can be categorized as diversity techniques, multiplexing schemes, multiple access methods, beamforming as well as multi-functional MIMO arrange- ments, as shown in Figure 1.2. Spatial diversity can be attained by employing multiple antennas at the transmitter or the receiver. Multiple antennas can be used to transmit and receive appropriately encoded replicas of the same information sequence in order to achieve diversity and hence to obtain an improved BER performance. In the context of diversity techniques, the antennas are spaced as far apart as possible, so that the signals transmitted to or received by the different antennas experience independent fading and hence we attain the highest possible diversity gain.

A simple spatial diversity technique, which does not involve any loss of bandwidth, is constituted by the employment of multiple antennas at the receiver, where several techniques can be employed for combining the independently fading signal replicas, including Maximum Ratio Combining (MRC), Equal Gain Combining (EGC) and Selection Combining (SC), as shown in Figure 1.2. Several transmit, rather than receive, diversity techniques have also been proposed in the literature, as shown in Figure 1.2. In, Alamouti proposed a witty transmit diversity technique using two transmit antennas, the key advantage of which was the employment of low-complexity single-receive-antenna-based detection, which avoids the more complex joint detection of multiple symbols. The decoding algorithm proposed in can be generalized to an arbitrary number of receive antennas using MRC, EGC or SC. Alamouti's achievement inspired Tarokh et al. to generalize the concept of transmit diversity schemes to more than two transmit antennas, contriving the generalized concept of STBCs. The family of STBCs is capable of attaining the same diversity gain as Space-Time Trellis Codes (STTCs) at a lower decoding complexity, when employing the same number of transmit antennas. However, a disadvantage of STBCs when compared with STTCs is that they employ unsophisticated repetition-coding and hence provide no coding gain. Furthermore, inspired by the philosophy of STBCs, Hochwald et al. proposed the transmit diversity concept known as STS for the downlink of Wideband Code Division Multiple Access (WCDMA) that is capable of achieving the highest possible transmit diversity gain.

Regretfully, the STBC and STS designs of contrived for more than two transmit antennas result in a reduction of the achievable throughput per channel use. An alternative idea invoked for constructing full-rate STBCs for complex-valued modulation schemes and more than two antennas was suggested in. Here the strict constraint of perfect orthogonality was relaxed in favor of achieving a higher data rate. The resultant STBCs were referred to as quasi-orthogonal STBCs.

The STBC and STS designs offer, at best, the same data rate as an uncoded single-antenna system, but they provide an improved BER performance compared with the family of single-antenna-aided systems by providing diversity gains. In contrast to this, several high-rate space-time transmission schemes having a normalized rate higher than unity have been proposed in the literature. For example, high-rate space-time codes that are linear both in space and time, namely the family of the so-called Linear Dispersion Codes (LDCs), were proposed in. LDCs provide a flexible trade-off between emulating STC and/or spatial multiplexing.

STBCs and STTCs are capable of providing diversity gains for the sake of improving the achievable system performance. However, this BER performance improvement is often achieved at the expense of a rate loss, since STBCs and STTCs may result in a throughput loss compared with single-antenna-aided systems. As a design alternative, a specific class of MIMO systems was designed for improving the attainable spectral efficiency of the system by transmitting different signal streams independently over each of the transmit antennas, hence resulting in a multiplexing gain. This class of MIMOs subsumes the Bell Labs Layered Space-Time (BLAST) scheme and its relatives. The BLAST scheme aims to increase the system throughput in terms of the number of bits per symbol that can be transmitted in a given bandwidth at a given integrity.

In contrast to the family of BLAST schemes, where multiple antennas are activated by a single user to increase the user's throughput, Space Division Multiple Access (SDMA) employs multiple antennas for the sake of supporting multiple users. SDMA exploits the unique user-specific Channel Impulse Response (CIR) of the different users for separating their received signals. On the other hand, in beamforming arrangements [16], typically [lambda]/2-spaced antenna elements are used for the sake of creating a spatially selective transmitter/receiver beam, where [lambda] represents the carrier's wavelength. Beamforming is employed for providing a beamforming gain by mitigating the effects of various interfering signals, provided that they arrive from sufficiently different directions. In addition, beamforming is capable of suppressing the effects of co-channel interference, hence allowing the system to support multiple users by angularly separating them. Again, this angular separation becomes feasible only on condition that the corresponding users are separable in terms of the angle of arrival of their beams.

Finally, multi-functional MIMOs, as the terminology suggests, combine the benefits of several MIMO schemes including diversity gains and multiplexing gains as well as beamforming gains. As mentioned earlier, V-BLAST is capable of achieving the maximum attainable multiplexing gain, while STBC can achieve the full achievable antenna diversity gain facilitated by the number of independently fading diversity channels. Hence, it was proposed in to combine these two techniques in order to provide both antenna diversity and spectral efficiency gains. Furthermore, the combined array processing proposed in was improved in by optimizing the decoding order of the different antenna layers. An iterative decoding algorithm was proposed in that results in achieving the full receive diversity gain for the combined V-BLAST STBC system facilitated by the number of independently fading diversity channels. On the other hand, in the authors presented a transmission scheme referred to as Double Space-Time Transmit Diversity (D-STTD), which consists of two STBC layers at the transmitter, which is equipped with four transmit antennas, while the receiver is equipped with two antennas. Furthermore, in order to achieve additional performance gains, beamforming has been combined both with spatial diversity and with spatial multiplexing techniques. STBC has been combined with beamforming in order to attain an improved Signal-to-Noise Ratio (SNR) gain in addition to the diversity gain. This contribution provides a light-hearted perspective on further research advances in the field of multi-functional MIMO systems and demonstrates how diversity, multiplexing and beamforming gains are achieved by multi-functional MIMOs. More explicitly, in Section 1.2.2 we elaborate on the design of two novel multi-functional MIMOs, which are characterized by diversity gain and multiplexing gain as well as beamforming gain. In Section 1.2.3 we quantify the achievable performance of the different MIMO schemes. A comparison of the different MIMO schemes expressed in terms of their diversity, multiplexing and beamforming gains is presented in Section 1.2.4, followed by our brief conclusions.

(Continues...)

Excerpted from Near-Capacity Multi-Functional MIMO Systemsby Lajos Hanzo Osamah Alamri Mohammed El-Hajjar Nan Wu Copyright © 2009 by John Wiley & Sons, Ltd. Excerpted by permission.
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Edition details

• Publisher: Wiley-IEEE Press
• Publication date: 2009
• Language: English
• ISBN 10: 0470779659
• ISBN 13: 9780470779651
• Binding: Hardcover
• Edition number: 1
• Number of pages: 752