Superiorized Adaptive Projected Subgradient Method with Application to MIMO Detection

Abstract

In this paper, we show that the adaptive projected subgradient method (APSM) is bounded perturbation resilient. To illustrate a potential application of this result, we propose a set-theoretic framework for MIMO detection, and we devise algorithms based on a superiorized APSM. Various low-complexity MIMO detection algorithms achieve excellent performance on i.i.d. Gaussian channels, but they typically incur high performance loss if realistic channel models (e.g., correlated channels) are considered. Compared to existing low-complexity iterative detectors such as individually optimal large-MIMO approximate message passing (IO-LAMA), the proposed algorithms can achieve considerably lower symbol error ratios over correlated channels. At the same time, the proposed methods do not require matrix inverses, and their complexity is similar to IO-LAMA.

arXiv commentSubmitted to IEEE Transactions on Signal Processing for possible publication

References in corpus (9)

• Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs
• Learning to Detect
• Perturbation Resilience and Superiorization of Iterative Algorithms
• Convergence and Perturbation Resilience of Dynamic String-Averaging Projection Methods
• Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information
• Symbol Error Rate Performance of Box-relaxation Decoders in Massive MIMO
• Distributed Adaptive Learning with Multiple Kernels in Diffusion Networks
• Convergence Properties of Dynamic String Averaging Projection Methods in the Presence of Perturbations
• Multi-Group Multicast Beamforming by Superiorized Projections onto Convex Sets