This paper considers a multiple-input multipleoutput (MIMO) receiver with very low-precision analog-todigital convertors (ADCs) with the goal of developing massive MIMO antenna systems that require minimal cost and power. Previous studies demonstrated that the training duration should be relatively long to obtain acceptable channel state information. To address this requirement, we adopt a joint channel-and-data (JCD) estimation method based on Bayes-optimal inference. This method yields minimal mean square errors with respect to the channels and payload data. We develop a Bayes-optimal JCD estimator using a recent technique based on approximate message passing. We then present an analytical framework to study the theoretical performance of the estimator in the large-system limit. Simulation results confirm our analytical results, which allow the efficient evaluation of the performance of quantized massive MIMO systems and provide insights into effective system design.

Massive multiuser multiple-input multiple-output (MU-MIMO) systems are expected to be the core technology in fifth-generation wireless systems because they significantly improve spectral efficiency. However, the requirement for a large number of radio frequency (RF) chains results in high hardware costs and power consumption, which obstruct the commercial deployment of massive MIMO systems. A potential solution is to use low-resolution digital-to-analog converters (DAC)/analog-todigital converters for each antenna and RF chain. However, using low-resolution DACs at the transmit side directly limits the degree of freedom of output signals and thus poses a challenge to the precoding design. In this study, we develop efficient and universal algorithms for a downlink massive MU-MIMO system with finitealphabet precodings. Our algorithms are developed based on the alternating direction method of multipliers (ADMM) framework. The original ADMM does not converge in a nonlinear discrete optimization problem. The primary cause of this problem is that the alternating (update) directions in ADMM on one side are biased, and those on the other side are unbiased. By making the two updates consistent in an unbiased manner, we develop two algorithms called iterative discrete estimation (IDE) and IDE2: IDE demonstrates excellent performance and IDE2 possesses a significantly low computational complexity. Compared with state-of-the-art techniques, the proposed precoding algorithms present significant advantages in performance and computational complexity.

A practical challenge in the precoding design for massive multiuser multiple-input multiple-output (MIMO) systems is to facilitate hardware-friendly implementation. To achieve this, we propose a low peak-to-average power ratio (PAPR) precoding based on approximate message passing (AMP) algorithm to minimize multiuser interference (MUI) in massive multiuser MIMO systems. The proposed approach exhibits fast convergence and low complexity characteristics. Compared with conventional constant envelope precoding and annulus-constrained precoding, simulation results demonstrate that the proposed AMP precoding is superior both in terms of computational complexity and the average running time. In addition, the proposed AMP precoding exhibits a much desirable tradeoff between MUI suppression and PAPR reduction. These findings indicate that the proposed AMP precoding is a suitable candidate for hardware implementation, which is very appealing for massive MIMO systems. Index TermsMassive MIMO, message passing, PAPR.Recently, constant envelope (CE) precoding was proposed by Mohammed and Larsson [3-5] to reduce the PAPR of the transmit signal, thereby enabling the use of cheap and highly power-efficient power amplifiers

No abstract