Heuristic Algorithms for RIS-assisted Wireless Networks: Exploring Heuristic-aided Machine Learning

2023/06/26 by Hao Zhou, Melike Erol‐Kantarci, Zhou, Hao +5 · 4 citations
Engineering · #Advanced Wireless Communication Technologies #Antenna Design and Analysis #FOS: Computer and information sciences #FOS: Electrical engineering #Networking and Internet Architecture (cs.NI) #Satellite Communication Systems #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2307.01205

openalex publication_date 2023/06/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

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, namely 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 (DQN). This work provides a new perspective for optimizing RIS-aided wireless networks by taking advantage of heuristic algorithms and ML.

Cited by

• Robust Design of Beyond-Diagonal Reconfigurable Intelligent Surface Empowered RSMA-SWIPT System Under Channel Estimation Errors
• Reconfigurable Intelligent Surfaces for 6G and Beyond: A Comprehensive Survey from Theory to Deployment
• Mobile Traffic Prediction using LLMs with Efficient In-context Demonstration Selection
• A Heuristic-Integrated DRL Approach for Phase Optimization in Large-Scale RISs

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