Channel Estimation and Performance Analysis of One-Bit Massive MIMO Systems

Abstract

This paper considers channel estimation and system performance for the uplink of a single-cell massive multiple-input multiple-output (MIMO) system. Each receive antenna of the base station (BS) is assumed to be equipped with a pair of onebit analog-to-digital converters (ADCs) to quantize the real and imaginary part of the received signal. We first propose an approach for channel estimation that is applicable for both flat and frequency-selective fading, based on the Bussgang decomposition that reformulates the nonlinear quantizer as a linear function with identical first-and second-order statistics. The resulting channel estimator outperforms previously proposed approaches across all SNRs. We then derive closed-form expressions for the achievable rate in flat fading channels assuming low SNR and a large number of users for the maximal ratio and zero forcing receivers that takes channel estimation error due to both noise and one-bit quantization into account. The closed-form expressions in turn allow us to obtain insight into important system design issues such as optimal resource allocation, maximal sum spectral efficiency, overall energy efficiency, and number of antennas. Numerical results are presented to verify our analytical results and demonstrate the benefit of optimizing system performance accordingly.

Performance analysis of uplink massive MIMO systems with variable‐resolution ADCs using MMSE and MRC detection

“…In this study, we assume that b m can uniformly select its value between b low and b up ( b low ≤ b up ). Moreover, if all ADCs use the same resolution, it follows that Σ α = α I M , which is similar with A d = α d I M of Li et al(eq. 36)…”

“…23 Only using one-bit ADCs in massive MIMO systems, channel estimation and performance analysis were offered by some researchers. 24,25 For relaying networks with low-resolution ADCs, it is validated that the performance loss caused by low-resolution ADCs can be compensated with increasing the number of antennas in a logarithmical scaling law. 12 Assuming that low-resolution ADCs are equipped at the relay station, Dong et al 26 derived closed-form sum rate expressions for both perfect and imperfect CSI and showed that low-resolution ADCs only result in limited sum rate loss when the number of antennas at the BS and relay station is relatively large compared to the number of users.…”

“…For MRC receiver under energy constraint, the SE of mixed‐ADC massive MIMO was investigated . Only using one‐bit ADCs in massive MIMO systems, channel estimation and performance analysis were offered by some researchers …”

Prior CSIT estimation for FDD massive MIMO system with 1‐bit feedback per dimension

“…The 1-bit signal recovery problem is also considered in the uplink channel estimation problem. 28,29 In particular, in the work of Stöckle et al, 28 to address the direction of arrival estimation issue in massive MIMO system, an efficient complex-valued BIHT (CBIHT) algorithm was proposed. In the work of Li et al, 29 a linear minimum mean-square-error (LMMSE) channel estimator was proposed by rewriting the nonlinear 1-bit quantization using a linear expression.…”

“…28,29 In particular, in the work of Stöckle et al, 28 to address the direction of arrival estimation issue in massive MIMO system, an efficient complex-valued BIHT (CBIHT) algorithm was proposed. In the work of Li et al, 29 a linear minimum mean-square-error (LMMSE) channel estimator was proposed by rewriting the nonlinear 1-bit quantization using a linear expression. However, the quantization-based channel estimation and feedback fails to consider the prior support information of sparse channel between the consecutive frames, where the channel support of the previous frame can be a good predictor for the CSIT estimation in current frame.…”