SCIENCE CHINA Information Sciences, Volume 61, Issue 4: 042303(2018) https://doi.org/10.1007/s11432-016-9126-1

Robust MMSE precoding for massive MIMO transmission with hardware mismatch

• AcceptedMay 5, 2017
• PublishedNov 20, 2017
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Abstract

Due to hardware mismatch, the channel reciprocity of time-division duplex massive multiple-input multiple-output system is impaired.Under this condition, there exist several different approaches for base station (BS) to obtain downlink (DL) channel information based on the minimum mean-square-error (MMSE) estimation method.In this paper, we show that with the hardware mismatch parameters BS can obtain the same DL channel information via these different approaches.As the obtained DL channel information is usually imperfect,we propose a precoding technique based on the criterion that minimizes the mean-square-error (MSE) of signal detection at the user terminals (UTs).The proposed precoding is robust to the channel estimation error and significantly improves the system performance compared to the conventional regularized zero-forcing precoding.Furthermore, we derive an asymptotic approximation of the ergodic sum rate for the proposed precoding using the large dimensional random matrix theory,which is tight as the number of antennas both at the BS and UT approach infinity with a fixed non-zero and finite ratio.This approximation can provide a reliable sum rate prediction at a much lower computation cost than Monte Carlo simulations.Simulation results show that the approximation is accurate even for a realistic system dimension.

Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61320106003, 61471113, 61521061, 61631018), National High Technology Research and Development Program of China (863) (Grant Nos. 2015AA01A701, 2014AA01A704), National Science and Technology Major Project of China (Grant No. 2014ZX03003006-003), and Huawei Cooperation Project.

• Figure 1

(Color online) Comparison of the ergodic sum rates between the robust MMSE precoding and the RZF precoding, results are shown versus the SNR with $N~=~128$, $K~=~32$ and $\delta~=~0.4$.

• Figure 2

(Color online) Absolute error $|(R_{\rm~sum}-R^\circ_{\rm~sum})/R_{\rm~sum}|$ for fixed SNR $\rho_{\mathrm{d}}=10$ dB, result is versus $N$ with $N/K=4$.

• Figure 1

(Color online) Comparison of the ergodic sum rates between the robust MMSE precoding and the RZF precoding, results are shown versus the SNR with $N~=~128$, $K~=~32$ and $\delta~=~0.4$.

• Figure 2

(Color online) Comparison of the SER performances between the robust MMSE precoding and the RZF precoding under QPSK modulation, results are shown versus SNR with $N~=~128$, $K=32$ and $\delta~=~0.4$.

• Figure 3

(Color online) Absolute error $|(R_{\rm~sum}-R^\circ_{\rm~sum})/R_{\rm~sum}|$ for fixed SNR $\rho_{\mathrm{d}}=10$ dB, result is versus $N$ with $N/K=4$.

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