Hardware Distortion Modeling for Panel Selection in Large Intelligent Surfaces

2025-01-22

AI Summary

To address the challenge of hardware-induced nonlinear distortion in large intelligent surfaces (LIS), which is difficult to model and optimize due to physical deformation, this paper proposes a novel two-parameter exponential power distortion model—replacing conventional high-order polynomial models—to significantly enhance tractability and computational efficiency in signal-to-noise-and-distortion ratio (SNDR) analysis. Based on this model, we derive, for the first time, two closed-form suboptimal solutions to the panel selection problem, achieving an unprecedented balance between theoretical accuracy and computational complexity. The method integrates memoryless modeling, exponential approximation fitting, and analytical SNDR derivation. The resulting closed-form solutions closely approach the SNDR performance of heuristic global optimization while drastically reducing computational overhead. This work establishes an efficient new paradigm for hardware distortion modeling and optimization in MIMO/LIS systems.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Optimization
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• hardware deformation
• signal reception performance
• large-scale intelligent panels

Innovation

• Simplified Method
• Hardware Impaired Performance
• Large Intelligent Surfaces