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

Hao Zhou, Melike Erol-Kantarci, Yuanwei Liu, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

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: 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.

Original languageEnglish (US)
Pages (from-to)1-9
Number of pages9
JournalIEEE Wireless Communications
DOIs
StateAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Greedy algorithms
  • Heuristic algorithms
  • Machine learning algorithms
  • Metaheuristics
  • Signal processing algorithms
  • Training
  • Wireless networks

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