TEL AVIV UNIVERSITY Gaddi Blumrosen

More documents

Recommendations

Info

$L' enclouage centro-médullaire des fractures du fémur guidé par ...$

: Table 2: The benefits of smart antennas techniques ([12]) Feature Benefit Signal gain—Inputs from multiple antennas are combined to optimize available power required to establish given level of coverage. Interference rejection—Antenna pattern can be generated toward cochannel interference sources, improving the signal-to-interference ratio of the received signals. Spatial diversity—Composite information from the array is used to minimize fading and other undesirable effects of multi-path propagation. power efficiency—combines the inputs to multiple elements to optimize available processing gain in the downlink (toward the user) 3.2 Beamforming 3.2.1 Introduction Better range/coverage—Focusing the energy sent out into the cell increases base station range and coverage. Lower power requirements also enable a greater battery life and smaller/lighter handset size. Increased capacity—Precise control of signal nulls quality and mitigation of interference combine to frequency reuse reduce distance (or cluster size), improving capacity. Certain adaptive technologies (such as space division multiple access) support the reuse of frequencies within the same cell. Multi-path rejection—can reduce the effective delay spread of the channel, allowing higher bit rates to be supported without the use of an equalizer reduced expense—Lower amplifier costs, power consumption, and higher reliability will result. Beamforming (BF) utilizes transmitted and/or received signals phase and amplitude induced in each antenna in an antenna array, to form beams in different desired directions. BF can be seen as moving from antenna space to beam space by Fourier transform. There are two main ways for BF. The first one is with fixed beam patterns - the beams are fixed and predetermined and the transmitter can switch from one to another and is called Switched-Beam BF. The other way, non-fixed beam patterns, is obtained by continuous updating of antenna weights according to CSI knowledge and subscriber spatial distribution. By this way optimal BF can be achieved. When the beamforming weights are changed adaptively, it is called adaptive BF. We will start in 3.2.2 with a description of SVD based BF. On 3.2.3, we introduce MMSE based BF. BF methods performance is briefly discussed in 3.2.4. In 3.2.5, a physical perspective of BF is shown.
3.2.2 SVD based BF. BF can be referred to also as an SVD based techniques. The ST system can be transformed into a set of r parallel scalar channels, where r is the channel rank. The available transmit power may then be optimally allocated (in maximum capacity sense) to the individual channels. For this technique, a certain value of channel realization and statistics is a must in the transmitter and receiver for spatial pre-coding before transmission and the spatial post-coding in reception. Pre-coding and Postcoding can be used adaptively to track channel changes. In the case of MISO, the term Beam Forming (BF) in transmission is often used, while in SIMO, the term Spatial Filtering in the receiver is often used. The subspaces spanned by RT and RR (2.3.4), can be separated to two different subspaces in the transmission and reception- Signal subspace and noise subspace respectively [13]. Signal subspace is referred to as signal reflections from large reflectors (different signals or users), each one has a related eigenvalue and an eigenvector. Signal subspace can be also obtained by forming a base out of the steering vectors in different location, and thus refer only to the dominant signal reflectors (sources). Noise subspace is referring to noise spatial sources each one has related eigenvalue and eigenvector. Noise subspace can be also obtained by forming a base from steering vectors in the direction of the spatial noise sources. Thus a good BF, will try to focus its energy in the direction of the signal corresponding eigenvectors and will try to avoid transmission in the direction of noise eigenvectors. If we want to match the weights to the channel statistically, in order to compensate as much as possible on channel fading, we have to demand in the transmission and H H reception, RT WT WT and RR WRWR , respectively. By using (2.22), we obtain: H 1/ 2 1/ 2 H H RT V TV V T ( VT ) WT WT (3.1) and the transmission weight matrix is: 1/ 2 1/ 2 H H 1/ 2 WT RT ( T V ) VT (3.2) similarly, for reception weights, we obtain: 1/ 2 1/ 2 WR RR U R (3.3) To examine the effect of the SVD weights on the transmitted signal, and how the signal is matched to the channel, we have to substitute (3.2) into, (2.45) for example, in transmit diversity ( RR 1) correlated Rayleigh fading: H H 1/ 2 H 1/ 2 H 1/ 2 H 1/ 2 T T T T T T Y HW X N G V V X N V V X N X N (3.4) (3.4) enable the transmitter to adjust to the channel by transmitting the input code in r ( NT if R T full rank) Gaussian scalar sub-channels at the direction of transmit v v , , v correlation eigenvectors, 1, 2 N . The transmitted power can also be T optimized (maximize the total capacity) by using algorithms like water pouring for forming a set of new eigenvalue, , and the transmitted signal, becomes: (3.5) ˆ W X V X v X v X v X T T N N H ˆ1 / 2 T ˆ T ˆ ˆ 1 1 2 2
Page 1 and 2: TEL AVIV UNIVERSITY The IBY and Ald
Page 3 and 4: Abstract A wireless channel, due to
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: 3. ST processing techniques Overvie
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
Page 55 and 56: 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
Page 65 and 66: 5.3.4 Algorithm sensitivity As for
Page 67 and 68: NT Figure 28: The Largest eigenvalu
Page 69 and 70: 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
Page 77 and 78: (maximum SNR approaches) and also t
Page 79 and 80:
APPENDIX A: ML optimal antenna weig
Page 81 and 82:
APPENDIX B: BF Iterative techniques
Page 83 and 84:
APPENDIX C: Simulation environment
Page 85 and 86:
find_opt_ab_Rice.m (Find MMSE optim
Page 87 and 88:
diversity ) , םע עדימ יקל
Page 89:
ביבא - לת תטיסרבינו
show all

TEL AVIV UNIVERSITY Gaddi Blumrosen

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?