Joint Linear Precoding and DFT Beamforming Design for Massive MIMO Satellite Communication

TL;DR

This paper proposes a joint linear precoding and DFT-based beamforming design for massive MIMO satellite communication, optimizing throughput with efficient algorithms and benchmarking methods.

Contribution

It introduces a novel joint design framework for LP and DFT beamforming in satellite mMIMO systems, addressing a complex MINP problem with new algorithms.

Findings

Proposed algorithms outperform benchmarks in throughput.

Efficient solutions with reduced computational complexity.

Monte Carlo simulations validate effectiveness.

Abstract

This paper jointly designs linear precoding (LP) and codebook-based beamforming implemented in a satellite with massive multiple-input multiple-output (mMIMO) antenna technology. The codebook of beamforming weights is built using the columns of the discrete Fourier transform (DFT) matrix, and the resulting joint design maximizes the achievable throughput under limited transmission power. The corresponding optimization problem is first formulated as a mixed integer non-linear programming (MINP). To adequately address this challenging problem, an efficient LP and DFT-based beamforming algorithm are developed by utilizing several optimization tools, such as the weighted minimum mean square error transformation, duality method, and Hungarian algorithm. In addition, a greedy algorithm is proposed for benchmarking. A complexity analysis of these solutions is provided along with a comprehensive set of Monte Carlo simulations demonstrating the efficiency of our proposed algorithms.