TEL AVIV UNIVERSITY Gaddi Blumrosen

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TEL AVIV UNIVERSITY The IBY and Alder Fleischman Faculty of Engineering Combining Orthogonal Space Time Codes and adaptive beamforming A thesis submitted toward the degree of Master of Science in Electrical Engineering by Gaddi Blumrosen This research was carried out in the department of Electrical Engineering - Systems Under the supervision of Dr. Avraham Freedman February 2005
Abstract A wireless channel, due to the radio wave propagation in a changing environment, causes the transmissions to vary in space, time and frequency. A channel, which causes the transmission to vary at different spatial locations, e.g. different antennas in antenna array, is called a space-selective fading channel. Multiple transmit and or receive antennas can be utilized in wireless systems to enhance coverage and capacity of the wireless channel by exploiting space-selective fading channel either by diversity or by coherent combining techniques. A special case, which will be investigated in this work, is a system with multiple transmit antennas and single receive antenna, an arrangement very common in the downlink of a cellular system, for example, where the base station can accommodate a number of antennas whereas the mobile station is limited in that respect. . In this case, diversity can be achieved by Space Time Codes (STC) techniques while coherent combining is achieved by Beam-forming (BF) techniques with or without exploitation of Channel State Information (CSI) available at the transmitter. In BF, the system is being transformed into a set of parallel scalar channels by Single Value Decomposition (SVD) and the available transmit power can then be optimally allocated to each individual scalar channel, called also sub-channel. For this technique, a certain value of CSI (channel realization) is necessary at the transmitter and receiver for spatial pre-coding before transmission and spatial post-coding in reception. Thus, BF is mainly a close- loop method. Adaptive BF techniques are widely used for tracking the channel fluctuations. In STC spatial encoding at transmitter and spatial decoding at the receiver are used. Full CSI knowledge is not assumed but just a general statistical model of the channel, mostly uncorrelated channel due to rich scattering, is assumed. Thus, STC is an open loop method. STC suffer from lack of CSI exploitation and is optimized only in a rich scattering environment while BF, though it exploits CSI, suffers from CSI quality degradation and is typically more computation demanding. A lot of research was done recently trying to combine these two families of techniques, STC and BF, in order to gain the benefits of both families. In this work we examine common ways for combining ST processing, mainly by beamforming techniques and STC. In particular we focus on combining Orthogonal Space Time Block Codes (OSTBC) and BF through adaptive antenna weight (which is a linear transformation) in ML approach in different channel scenarios. Later we derive a simple approximation for the weights which has been shown to be very close to the ML optimal solution performance in simpler manner (computation gain). We start this thesis with a describing of recent space-time system models. Then we explore different Space Time Codes (STC) techniques and BF techniques, mainly focusing in this work on the case of Multiple transmit antennas and single receive antenna with imperfect channel estimation at the transmitter and perfect channel estimation at receiver with transmission of orthogonal STC. As a first step, the ways of combining these two families of techniques were surveyed. We have chosen to concentrate on the ML optimal combination OSTBC and BF, which was described in [25] and denoted in this thesis as the JSO algorithm according to the initials of the writers, G. Jöngren, M. Skoglund, B. Ottersten.
Page 1: TEL AVIV UNIVERSITY The IBY and Ald
Page 5 and 6: 1. Introduction A wireless channel,
Page 7 and 8: dedicated to examine ways of adapta
Page 9 and 10: 2.2 Antenna array modeling 2.2.1 In
Page 11 and 12: 2.3.2 Spatial wireless channel mode
Page 13 and 14: 1 inversely proportional to Doppler
Page 15 and 16: Table 1: Relations between channel
Page 17 and 18: Where is the distance between the i
Page 19 and 20: For simplicity, let use the common
Page 21 and 22: Where the transmit and receive matr
Page 23 and 24: 2.4 Received and transmitted Signal
Page 25 and 26: Eb MRT, the received SNR becomes ,
Page 27 and 28: 3. ST processing techniques Overvie
Page 29 and 30: 3.2.2 SVD based BF. BF can be refer
Page 31 and 32: The physical interpretation for WR
Page 33 and 34: SVD Eigenvector physical perspectiv
Page 35 and 36: 3.3.2 STBC Design Principles. As in
Page 37 and 38: It is shown that space-time block c
Page 39 and 40: OSTBC. It is seen that the antenna
Page 41 and 42: epresent for example a Rician chann
Page 43 and 44: By minimization the performance cri
Page 45 and 46: Figure 14: Real-time adaptive STC c
Page 47 and 48: techniques, but differ mainly in th
Page 49 and 50: Case of diagonal conditional covari
Page 51 and 52: 4.4.5 OSTBC with linear antenna wei
Page 53 and 54:
5. ML optimal weights for transmit
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1. 1 N as T NT est0 or 0. Equal p
Page 57 and 58:
5.2.5 An approximation function The
Page 59 and 60:
5.2.6 Optimal weight sensitivity to
Page 61 and 62:
algorithm follows closely the bette
Page 63 and 64:
An alternative, more intuitive way
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5.3.4 Algorithm sensitivity As for
Page 67 and 68:
NT Figure 28: The Largest eigenvalu
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estimated. Both OSTBC and WOSTC per
Page 71 and 72:
5.4 ML optimal antenna weights with
Page 73 and 74:
5.4.3 Deriving ML Optimal antenna w
Page 75 and 76:
Rician channel with non-diagonal co
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(maximum SNR approaches) and also t
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APPENDIX A: ML optimal antenna weig
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APPENDIX B: BF Iterative techniques
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APPENDIX C: Simulation environment
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find_opt_ab_Rice.m (Find MMSE optim
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diversity ) , םע עדימ יקל
Page 89:
ביבא - לת תטיסרבינו
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TEL AVIV UNIVERSITY Gaddi Blumrosen

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