The sum rate performance of nonlinier quantized precoding using Gibbs sampling are evaluated in a massive multiuser multiple-input multiple-output (MU-MIMO) system in this paper. Massive MU-MIMO is a key technology to handle the growth of data traffic. In a full digital massive MU-MIMO system, however, the resolution of digital-to-analogue converters (DACs) in transmit antenna branches have to be low to yield acceptable power consumption. Thus, a combinational optimization problem is solved for the nonlinier quantized precoding to determine transmit signals from finite alphabets output from low resolution DACs. A conventional optimization criterion minimizes errors between desired signals and received signals at user equipments (UEs). However, the system sum rate may decrease as it increases the transmit power. This paper proposes two optimization criteria that take the transmit power into account in order to maximize the sum rate. Mixed Gibbs sampling is applied to obtain the suboptimal solution of the nonlinear optimization problem. Numerical results obtained through computer simulations show that the two proposed criteria achieve higher sum rates than the conventional criterion. On the other hand, the sum rate criterion achieves the largest sum rate while it leads to less throughputs than the MMSE criterion on approximately 60% of subcarriers.

Massive multiple-input and multiple-output (MIMO) is a key technology to meet the increasing capacity demands that must be satisfied by next generation wireless systems. However, it is expensive to use linear power amplifiers when implementing a massive MIMO system as it will have hundreds of antennas. In this paper, considering that low peak-to-average power ratio (PAPR) of transmit signals can facilitate hardware-friendly equipment with nonlinear but power-efficient amplifiers, we first formulate the precoding scheme as a PAPR minimization problem. Then, in order to obtain the optimal solution with low complexity, the precoding problem is recast into a Bayesian estimation problem by leveraging belief propagation algorithm. Eventually, we propose a low-PAPR approximate message passing (LP-AMP) algorithm based on belief propagation to ensure the good transmission performance and minimize the PAPR to realize practical deployments. Simulation results reveal that the proposed method can get PAPR reduction and adequate transmission performance, simultaneously, with low computational complexity. Moreover, the results further indicate that the proposed method is suitable for practical implementation, which is appealing for massive multiuser MIMO (MU-MIMO) systems.