Low-Complexity Precoding Design for Massive Multiuser MIMO Systems Using Approximate Message Passing

A practical challenge in the precoding design of 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 an 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 a conventional constant-envelope precoding and an annulus-constrained precoding, simulation results demonstrate that the proposed AMP precoding is superior both in terms of computational complexity and 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

Precoding via Approximate Message Passing with Instantaneous Signal Constraints

This paper proposes a low complexity precoding algorithm based on the recently proposed Generalized Least Square Error (GLSE) scheme with generic penalty and support. The algorithm iteratively constructs the transmit vector via Approximate Message Passing (AMP). Using the asymptotic decoupling property of GLSE precoders, we derive closed form fixed point equations to tune the parameters in the proposed algorithm for a general set of instantaneous signal constraints. The tuning strategy is then utilized to construct transmit vectors with restricted peak-to-average power ratios and to efficiently select a subset of transmit antennas. The numerical investigations show that the proposed algorithm tracks the large-system performance of GLSE precoders even for a moderate number of antennas.Comment: 2018 International Zurich Seminar on Information and Communication (IZS) 5 pages and 2 figure

A First-Order Primal-Dual Method for Saddle Point Optimization of PAPR Problem in MU-MIMO-OFDM Systems

This paper investigates the use of a particular splitting-based optimization technique for constrained l∞-norm based peak-to-average power ratio (PAPR) reduction problem in multiuser orthogonal frequency-division multiplexing (OFDM) based multiple-input multi-output (MIMO) systems. PAPR reduction and multi-user interference (MUI) cancelation are considered in a saddle-point formulation on the downlink of a multi-user MIMO-OFDM system and an efficient primal-dual hybrid gradient (PDHG) inspired algorithm with easy-to-evaluate proximal operators is developed. The proposed algorithm converges significantly faster to satisfactory solutions with much improved asymptotical convergence rate than existing methods. Numerical results illustrate the superior performance of the proposed algorithm over existing methods in terms of PAPR reduction for different MIMO configurations