Hardware Distortion Modeling for Panel Selection in Large Intelligent Surfaces

TL;DR

This paper models hardware distortions in large intelligent surfaces, proposing a simplified exponential model for better analytical tractability, enabling efficient panel selection optimization with near-optimal performance.

Contribution

It introduces a novel exponential distortion model for LISs that simplifies analysis and optimization compared to traditional polynomial models.

Findings

Exponential model improves analytical tractability for SNDR optimization.

Closed-form solutions for panel selection are near-optimal.

Model reduces computational complexity in hardware distortion analysis.

Abstract

Hardware distortion in large intelligent surfaces (LISs) may limit their performance when scaling up such systems. It is of great importance to model the non-ideal effects in their transceivers to study the hardware distortions that can affect their performance. Therefore, we have focused on modeling and studying the effects of nonlinear RX-chains in LISs. We first derive expressions for SNDR of a LIS with a memory-less polynomial-based model at its RX-chains. Then we propose a simplified double-parameter exponential model for the distortion power and show that compared to the polynomial based model, the exponential model can improve the analytical tractability for SNDR optimization problems. In particular, we consider a panel selection optimization problems in a panel-based LIS scenario and show that the proposed model enables us to derive two closed-form sub-optimal solutions for panel selection, and can be a favorable alternative to high-order polynomial models in terms of computation complexity, especially for theoretical works on hardware distortion in MIMO and LIS systems. Numerical results show that the sub-optimal closed-form solutions have a near-optimal performance in terms of SNDR compared to the global optimum found by high-complexity heuristic search methods.