Low‐complexity channel estimation for time division duplex massive multi‐user multi‐input multi‐output systems

Abstract

This paper addresses the problem of minimum mean square error channel estimator for time division duplex massive multi-user multi-input multi-output systems. It is noteworthy that, the minimum mean square error has been previously proposed for multi-cell massive multi-user multi-input multi-output channel estimation. However, the minimum mean square error estimator suffers from high computational complexity due to the large dimension of the covariance matrix inversion. In this study, low-complexity channel estimator for time division duplex massive multi-user multi-input multi-output networks is designed by using the low-rank matrix approximation techniques. The proposed estimator is referred to as an approximate minimum mean square error estimator. Furthermore, the computational complexity of the proposed approximate minimum mean square error estimator is analysed and compared to the minimum mean square error and least square estimators. The normalised mean square error and the uplink achievable sum-rate performance criteria are used to evaluate the performance of the proposed estimator. Finally, the simulation results show the effectiveness of the proposed estimator under two different scenarios: noise-limited and pilot contamination. These simulation results are compared to the conventional minimum mean square error and least square estimators.

Outdoor-to-Indoor mmWave Relaying with Massive MIMO: Impact of Imperfect Channel Estimation

“…and denoting by E ≜ H eq − H eq the error estimation matrix, the variance of the elements of E is β e − ζ 2 e , and 0 ≤ µ e ≜ ζ 2 e β e ≤ 1 denotes the channel estimation reliability. The computational complexity of channel estimation was studied and approximated for several channel estimation methods in [27]. Referring to it, we approximate the MMSEbased channel estimation complexity as ON 3 B τ 3 p .…”