Low Complexity Detection Algorithm Based on Two-diagonal Matrix Decomposition in Massive MIMO Systems

CAO Haiyan; YANG Jingwei; FANG Xin; XU Fangmin

doi:10.11999/JEIT170498

Volume 40 Issue 2

Feb. 2018

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CAO Haiyan, YANG Jingwei, FANG Xin, XU Fangmin. Low Complexity Detection Algorithm Based on Two-diagonal Matrix Decomposition in Massive MIMO Systems[J]. Journal of Electronics & Information Technology, 2018, 40(2): 416-420. doi: 10.11999/JEIT170498

Citation:

CAO Haiyan, YANG Jingwei, FANG Xin, XU Fangmin. Low Complexity Detection Algorithm Based on Two-diagonal Matrix Decomposition in Massive MIMO Systems[J]. Journal of Electronics & Information Technology, 2018, 40(2): 416-420. doi: 10.11999/JEIT170498

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Low Complexity Detection Algorithm Based on Two-diagonal Matrix Decomposition in Massive MIMO Systems

doi: 10.11999/JEIT170498

Funds:

The National Natural Science Foundation of China (61501158, 61379027), The Natural Science Foundation of Zhejiang Province (LY14F010019, LQ15F01004)

Received Date: 2017-05-24
Rev Recd Date: 2017-10-24
Publish Date: 2018-02-19

Abstract

Abstract

Minimum Mean Square Error (MMSE) algorithm is near-optimal for uplink massive MIMO systems, but it involves high-complexity matrix inversion. Recently, the proposed detection algorithm based on Neumann series approximation reduces the complexity with some performance losses. In order to reduce the complexity while approaching the performance of MMSE algorithm, the Neumann series approximation based on two-diagonal matrix decomposition is proposed in this paper, that is, the large matrix is decomposed into the sum of the two elements of the main diagonal and the hollow matrix. The theoretical analysis and simulation results show that the detection performance of the proposed algorithm is close to the MMSE detection algorithm while its computational complexity is reduced from O(K3)toO(K2), where K is the number of users.
- Massive MIMO,
- Neumann series,
- Two-diagonal matrix,
- Low complexity

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References

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