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Approximate Iteration Detection and Precoding in Massive MIMO

China Communications 2018年第5期15卷 183-196页

作者： Chuan Tang Yerong Tao Yancang Chen Cang Liu Luechao Yuan Zuocheng Xing Luoyang Electronic Equipment Test Center LuoYang 471000China National Laboratory for Parallel and Distributed Processing National University of Defense TechnologyChangsha 410073China

Massive multiple-input multiple-output provides improved energy efficiency and spectral efficiency in 5 G. However it requires large-scale matrix computation with tremendous complexity, especially for data detection and precoding. Recently, many detection and precoding methods were proposed using approximate iteration methods, which meet the demand of precision with low complexity. In this paper, we compare these approximate iteration methods in precision and complexity, and then improve these methods with iteration refinement at the cost of little complexity and no extra hardware resource. By derivation, our proposal is a combination of three approximate iteration methods in essence and provides remarkable precision improvement on desired vectors. The results show that our proposal provides 27%-83% normalized mean-squared error improvement of the detection symbol vector and precoding symbol vector. Moreover, we find the bit-error rate is mainly controlled by soft-input soft-output viterbi decoding when using approximate iteration methods. Further, only considering the effect on soft-input soft-output viterbi decoding, the simulation results show that using a rough estimation for the filter matrix of minimum mean square error detection to calculating log-likelihood ratio could provideenough good bit-error rate performance, especially when the ratio of base station antennas number and the users number is not too large.

关键词： massive MIMO detection and precoding matrix inversion iteration refinement soft viterbi decoding

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