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.

翻译：可重构智能表面（RIS）是构建智能无线电环境的一项有前景的技术。然而，将RIS集成到无线网络中也会显著增加网络管理的复杂性。本文研究了用于优化RIS辅助无线网络的启发式算法及其应用，包括贪婪算法、元启发式算法和匹配理论。此外，我们将启发式算法与机器学习（ML）相结合，提出了三种启发式增强的机器学习算法，即启发式深度强化学习（DRL）、启发式增强监督学习和启发式分层学习。最后，通过案例研究表明，与传统深度Q网络（DQN）相比，启发式DRL能够实现更高的数据速率和更快的收敛速度。本研究为利用启发式算法和机器学习优化RIS辅助无线网络提供了新的视角。