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

TL;DR

This paper explores the use of heuristic algorithms combined with machine learning to optimize RIS-assisted wireless networks, demonstrating improved data rates and convergence in a case study.

Contribution

It introduces three novel heuristic-aided ML algorithms for RIS network optimization, integrating heuristics with deep reinforcement learning, supervised learning, and hierarchical learning.

Findings

Heuristic DRL outperforms conventional DQN in data rate and convergence.

The proposed algorithms effectively manage the complexity of RIS-assisted networks.

The work offers a new perspective on combining heuristics and ML for wireless network optimization.

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.