期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2018
卷号：9
期号：8
DOI：10.14569/IJACSA.2018.090877
出版社：Science and Information Society (SAI)
摘要：The promising massive level MIMO (multiple-input-multiple-output) systems based on extremely huge antenna collections have turned into a sizzling theme of wireless com-munication systems. This paper assesses the performance of the quasi optimal MIMO detection approach based on semi-definite programming (SDP). This study also investigates the gain obtained when using SDP detector by comparing Bit Error Rate (BER) performance with linear detectors. The near optimal Zero Forcing Maximum Likelihood (ZFML) is also implemented and the comparison is evaluated. The ZFML detector reduces exhaustive ML searching using multi-step reduced constellation (MSRC) detection technique. The detector efficiently combines linear processing with local ML search. The complexity is bounded by maintaining small search areas, while performance is maximized by relaxing this constraint and increasing the cardinality of the search space. The near optimality of SDP is analyzed through BER performance with different antenna configurations using 16-QAM signal constellation operating in a flat fading channel. Simulation results indicate that the SDP detector acquired better BER performance, in addition to a significant decrease in computational complexity using different system/antenna configurations.
关键词：Multiple input multiple output antennas; MIMO detection approaches; performance analysis; semi-definite program-ming; zero forcing maximum likelihood
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