Capacity Optimization of Large Intelligent Surface With Hardware Impairment Based on Meta-Deep Learning

Autor: Yifan Mao, Xiaoyu Xiao, Zhirun Hu
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IEEE Access, Vol 12, Pp 69359-69370 (2024)
Druh dokumentu: article
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2024.3401454
Popis: This work proposes a sub-optimal method based on a two-layer structured meta-deep reinforcement learning (MDRL) approach to address the hardware impairment (HWI) optimization issue in large intelligent surface (LIS) systems. This method, designed for distributed LIS systems with reflection matrices, effectively enhances the system capacity and performance despite HWIs. Building upon existing techniques of dividing large-area LIS systems into multiple small-area subsystems, the simulated results demonstrate that sub-optimal LIS performance can be achieved with fewer samples in diverse dynamic wireless environments. This innovative approach enhances the adaptability of distributed LIS systems and offers an effective HWI management strategy, paving the way for future LIS system optimization.
Databáze: Directory of Open Access Journals