Heuristic Algorithms for RIS-Assisted Wireless Networks: Exploring Heuristic-Aided Machine Learning

被引:0
|
作者
Zhou, Hao [1 ]
Erol-Kantarci, Melike [1 ]
Liu, Yuanwei [2 ]
Poor, H. Vincent [3 ]
机构
[1] Univ Ottawa, Ottawa, ON, Canada
[2] Queen Mary Univ London, London, England
[3] Princeton Univ, Princeton, NJ USA
基金
美国国家科学基金会;
关键词
Heuristic algorithms; Wireless networks; Machine learning algorithms; Metaheuristics; Signal processing algorithms; Greedy algorithms; Training;
D O I
10.1109/MWC.010.2300321
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Reconfigurable intelligent surfaces (RISs) are a promising technology to enable smart radio environments. However, integrating RISs into wireless networks also leads to substantial complexity for network management. This work investigates heuristic algorithms and applications to optimize RIS-aided wireless networks, including greedy algorithms, meta-heuristic algorithms, and matching theory. Moreover, we combine heuristic algorithms with machine learning (ML), and propose three heuristic-aided ML algorithms: heuristic deep reinforcement learning (DRL), heuristic-aided supervised learning, and heuristic hierarchical learning. Finally, a case study shows that heuristic DRL can achieve higher data rates and faster convergence than conventional deep Q-networks (DQNs). This work provides a new perspective for optimizing RIS-aided wireless networks by taking advantage of heuristic algorithms and ML.
引用
收藏
页码:106 / 114
页数:9
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