y 1 = 槡 P 1 1h T 1 1f 1 s 1 + 槡 P 1 2g T 1 2 interference 2f 2 s y 2 = 槡 P 2 2h T 2 2f 2 s 2 + 槡 P 2 1g T 2 1 interference 1f 1 s + n n

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月拦狄
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1 37 1 Vol 37 No Journal of Jiangxi Normal UniversityNatural Science Jan MISO 郭荣新, 袁继昌 RVQ 2 MISO 3 TN A 0 MIMO 2 MISO 3 MIMOnetwork MIMO 3GPP LTE-A 2 CoMP MIMO 1-3 DPC 4-6 DPC X x X T X H X I N 6 N N x x MISO 1 SLNR 1 M J Z ZD01GO1 Full-CSI MIMO

2 y 1 = 槡 P 1 1h T 1 1f 1 s 1 + 槡 P 1 2g T 1 2 interference 2f 2 s y 2 = 槡 P 2 2h T 2 2f 2 s 2 + 槡 P 2 1g T 2 1 interference 1f 1 s + n n 2 2 P i j i j P i j = P 0 D 0 /d i j α α P 0 D 0 d i j 6 i j D 0 P f i C M 1 i i f i = 1 n i SLNR i = P i i f T i h i n i ~ CN0 1 s i i 1 s i 2 = i = 1 2 f i BF i 1 MISO 1 1 f i = h i i h i i 6 1 h C M g 1 2 C M 1 2 ZF h i i g i j i = 1 2j = 1 2i j g T j if i 2 = CN0 1 0 f i = 1 U i = h T i ig T j iw i = MISO 2 U H i U i -1 U H i f i = w i w i 7 w i W i 1 g T j iw i = SLNR i h T i i f i P j i f T i g j i g T j i f i SLNR P f opt i i f T i hi ih T i i f i i arg max ii = 1 2 f i C M 1 P j i f T i g j i g T j i f i SINR i = P i i f T i hi ih T i i f i P i j f T j g i j g T i j f j f opt P i = λ j i max g R i = log SINR i 4 P j i g T -1 {( j I i i i P i i ) i h T i i } MISO 10 λ max A A R = 2 R i 5

3 1 MISO 39 3 CDI FDD i h i i g i j B Q B G h i i g i j B Q + B G = B h i i g i j 2 F Q = f 1 f 2 f 2 B Q F G = f 1 f 2 f 2 B G M h i i = arg max h槇 H i i f 2 f F Q 11 gi j = arg max g槇 H i j f 2 f F G 12 2 BF 22 h槇 i i = h i i / h i i g槇 2 i j = g i j / g i j 2 Matlab ZF SLNR SLNR D 0 = M = 2 M > N r = 1 α = B = 6 BF SINR BF 2 ZF BF SLNR SNR SNR 2 D 0 = 1 P 0 = 15 db M = 2 N r = 1 α = 3 7 B = 6 B = 8 BF SLNR SLNRBF SLNR

4 B = 6 4 B = P 0 = 5 db D 0 = 1 M = 2 N r = 2 BF BF 1 α = 3 7 B = 6 BF 2 BF BF SLNR BF SLNR BF SLNR 4 SLNR 2 P 0 = 20 db 2 SLNR SLNR 3 P 0 = 10 db2

5 1 MISO Theory SLNR SLNR 2 3Yu Wei Lan Tian Transmitter optimization for the multiantenna downlink with per-antenna power constraints J 2 BF IEEE Transactions on Signal Processing Costa M Writing on dirty paper J IEEE Transactions on Information Theory Weingarten H Steinberg Y Shaimai S The capacity region SLNR of the Gaussian multiple-input multiple-output broadcast channel J IEEE Transactions on Information Theory Zhang Jun Chen Runhua Andrews J G et al Networked MIMO with clustered linear precoding J IEEE Transactions on Wireless Communications MISO Nakagami-m J Sadek M Tarighat A Sayed A A leakage-based precoding scheme for downlink multi-user MIMO channels J IEEE Transactions on Wireless Communications Bhagavatula R Heath Jr R W Adaptive limited feedback for sumrate maximizing beamforming in cooperative multicell systems J IEEE Transactions on Signal Processing Zhang Jun Andrews J G Adaptive spatial intercell interference cancellation in multicell wireless networks J 7 IEEE Journal on Selected Areas in Communications Boccardi F Huang H Zero-forcing precoding for the MIMO broadcast channel under per-antenna power constraints C CannesIEEE 7th Workshop on Signal Processing Advances in Wireless Communications Karakayali K Yates RFoschini G et al Optimum zeroforcing beamforming with per-antenna power constraints C NiceIEEE International Symposium on Information 11Shi Fengfeng Xu Wei Zhao Cunming A Limited Feedback Strategy for Cooperative Multicell MISO Systems C NanjingInternational Conference on Wireless Communications and Signal Processing Santipach W Honig M Signature optimization for CDMA with limited feedback J IEEE Transactions on Information Theory The Study of Interference Cancellation Schemes in Multi-Cell MISO Communication Systems GUO Rong-xin YUAN Ji-chang School of Information Science and Engineering Huaqiao University Xiamen Fujian China AbstractThe inter-cell interference cancellation schemes were investigated with partial channel state information The sum-rate performance of three kinds of beamforming transmission strategy i e eigen-beamforming zero-forcing beamforming and signal to leakage and noise ratio beamformingby exploiting two independent random vector quantization codebooks to quantize the desired channel and the interference channel respectively is analyzed The optimal transmission strategy choice of the base station according to user locations and average cell-edge signal to noise ratio is studied Simulation results demonstrate that when the user is noise-limited the neighbor base station will choose eigen-beamforming transmission strategy and when the user is interference-limited the neighbor base station will choose signal to leakage and noise ratio beamforming transmission strategy Key wordsinter-cell interferencebeamforminglimited feedbacksum-rate

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08_toukei03.dvi 2013 61 1 123 134 c 2013 2012 6 28 8 28 9 5 LDPC Low-Density Parity-Check LDPC MIMO 1. 2002 Feldman LP Feldman, 2003; Feldman et al., 2005 Feldman Vontobel and Koetter 2006 Koetter and Vontobel 2003 Burshtein