An Improved Multicell MMSE Channel Estimation in a Massive MIMO System

Abstract

Massive MIMO is a promising technology to improve both the spectrum efficiency and the energy efficiency. The key problem that impacts the throughput of a massive MIMO system is the pilot contamination due to the nonorthogonality of the pilot sequences in different cells. Conventional channel estimation schemes cannot mitigate this problem effectively, and the computational complexity is increasingly becoming larger in views of the large number of antennas employed in a massive MIMO system. Furthermore, the channel estimation is always carried out with some ideal assumptions such as the complete knowledge of large-scale fading. In this paper, a new channel estimation scheme is proposed by utilizing interference cancellation and joint processing. Highly interfering users in neighboring cells are identified based on the estimation of large-scale fading and then included in the joint channel processing; this achieves a compromise between the effectiveness and efficiency of the channel estimation at a reasonable computational cost, and leads to an improvement in the overall system performance. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.

Rateless space–time block code for mitigating pilot contamination effects in multi‐cell massive MIMO system with lossy links

“…The large‐scale fading (LSF) has significant impacts on the performance of uplink data decoding as well as downlink transmission, therefore, it has to be taken into consideration for more accurate performance or estimations in massive MIMO systems [11]. In [12], an improved multi‐cell MMSE joint channel estimation scheme based on the LSF estimation was introduced. However, because of joint processing, the computational complexity grows sharply as the number of antennas increases.…”

Channel estimation for massive MIMO TDD systems assuming pilot contamination and flat fading

“…Due to numerical issues, the closed-form MSE expression presented in [21] does not produce values for M > 85. During our simulations, comparing the closed-form expression given by equation (15) and the approximated MSE expression given by (14), we noticed that the (2M) function in the numerator of equation (16) finite floating-point number represented by the IEEE double precision format, i.e., 1.7977e+308 [30], for values of M greater than 85. A double precision variable goes to +Inf after the largest possible number [30].…”

“…The approximate MSE in (14) for the proposed estimator decreases with increasing transmitting power q, increasing M or decreasing β iik , which means smaller interference level from other cells, i.e., smaller pilot contamination.…”

“…Additionally, the estimation error of the large-scale fading coefficients impacts significantly on the performance of uplink data decoding and downlink transmission (e.g., precoding and beamforming) [14][15][16]. Approaches on how to estimate the large-scale fading coefficients are presented in the following pieces of work [10,14,17].…”

Comparative study of channel estimators for massive MIMO 5G NR systems

“…In [9], the LS and MMSE estimators were compared in terms of the bit error rate (BER) performance, but again, a MIMO combining operation is not specified. An improved MMSE estimation applied to a zero‐forcing (ZF) receiver was analysed in [12]. However, the results are limited to the MSE of the channel estimation, not the BER or the error vector magnitude (EVM) that can be achieved with the ZF technique combined with the MMSE estimation.…”