A Worst-Case Performance Optimization Based Design Approach to Robust Symbol-Level Precoding for Downlink MU-MIMO
원문 초록
This paper addresses the optimization problem of symbol-level precoding (SLP) in the downlink of a multiuser multiple-input multiple-output (MU-MIMO) wireless system while the precoder's output is subject to partially-known distortions. In particular, we assume a linear distortion model with bounded additive noise. The original signal-to-interference-plus-noise ratio (SINR) -constrained SLP problem minimizing the total transmit power is first reformulated as a penalized unconstrained problem, which is referred to as the relaxed robust formulation. We then adopt a worst-case design approach to protect the users' intended symbols and the targeted constructive interference with a desired level of confidence. Due to the non-convexity of the relaxed robust formulation, we propose an iterative algorithm based on the block coordinate ascent-descent method. We show through simulation results that the proposed robust design is flexible in the sense that the CI constraints can be relaxed so as to keep a desirable balance between achievable rate and power consumption. Remarkably, the new formulation yields more energy-efficient solutions for appropriate choices of the penalty parameter, compared to the original problem.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
Stochastic Integration Based Estimator: Robust Design and Stone Soup Implementation
Robust Waveform Design for Integrated Sensing and Communication
GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty
Inverse deep learning methods and benchmarks for artificial electromagnetic material design
RID-Noise: Towards Robust Inverse Design under Noisy Environments
토론
이 논문과 연결된 포럼 글이 아직 없습니다.