# A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks

arXiv: https://arxiv.org/abs/2303.14320 · HTML: https://mlsanity.com/paper/2303.14320/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2023.

## Built on by (most cited first, 15 of 15)

- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [ORAN-Bench-13K: An Open Source Benchmark for Assessing LLMs in Open Radio Access Networks](https://mlsanity.com/paper/2407.06245/) (2024)
- [LLM-OptiRA: LLM-Driven Optimization of Resource Allocation for Non-Convex Problems in Wireless Communications](https://mlsanity.com/paper/2505.02091/) (2025)
- [Dual-Mind World Models: A General Framework for Learning in Dynamic Wireless Networks](https://mlsanity.com/paper/2510.24546/) (2025)
- [Multi-Agent Reinforcement Learning with Action Masking for UAV-enabled Mobile Communications](https://mlsanity.com/paper/2303.16737/) (2023)
- [Non-iterative Optimization of Trajectory and Radio Resource for Aerial Network](https://mlsanity.com/paper/2405.01314/) (2024)
- [Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks](https://mlsanity.com/paper/2604.01238/) (2026)
- [An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models](https://mlsanity.com/paper/2405.17439/) (2024)
- [A Hierarchical DRL Approach for Resource Optimization in Multi-RIS Multi-Operator Networks](https://mlsanity.com/paper/2410.12320/) (2024)
- [Generative AI-Driven Phase Control for RIS-Aided Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2602.11226/) (2026)
- [Fairness-Driven Optimization of RIS-Augmented 5G Networks for Seamless 3D UAV Connectivity Using DRL Algorithms](https://mlsanity.com/paper/2312.09420/) (2023)
- [Multi-beam Beamforming in RIS-aided MIMO Subject to Reradiation Mask Constraints -- Optimization and Machine Learning Design](https://mlsanity.com/paper/2507.15367/) (2025)
- [Sum Rate Maximization in STAR-RIS-UAV-Assisted Networks: A CA-DDPG Approach for Joint Optimization](https://mlsanity.com/paper/2512.01202/) (2025)
- [When Critics Disagree: Adaptive Reward Poisoning Attacks in RIS-Aided Wireless Control System](https://mlsanity.com/paper/2605.20037/) (2026)
- [Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Models](https://mlsanity.com/paper/2606.31334/) (2026)

## Abstract (arXiv)

Reconfigurable intelligent surfaces (RISs) have received considerable attention as a key enabler for envisioned 6G networks, for the purpose of improving the network capacity, coverage, efficiency, and security with low energy consumption and low hardware cost. However, integrating RISs into the existing infrastructure greatly increases the network management complexity, especially for controlling a significant number of RIS elements. To unleash the full potential of RISs, efficient optimization approaches are of great importance. This work provides a comprehensive survey on optimization techniques for RIS-aided wireless communications, including model-based, heuristic, and machine learning (ML) algorithms. In particular, we first summarize the problem formulations in the literature with diverse objectives and constraints, e.g., sum-rate maximization, power minimization, and imperfect channel state information constraints. Then, we introduce model-based algorithms that have been used in the literature, such as alternating optimization, the majorization-minimization method, and successive convex approximation. Next, heuristic optimization is discussed, which applies heuristic rules for obtaining low-complexity solutions. Moreover, we present state-of-the-art ML algorithms and applications towards RISs, i.e., supervised and unsupervised learning, reinforcement learning, federated learning, graph learning, transfer learning, and hierarchical learning-based approaches. Model-based, heuristic, and ML approaches are compared in terms of stability, robustness, optimality and so on, providing a systematic understanding of these techniques. Finally, we highlight RIS-aided applications towards 6G networks and identify future challenges.