mlsanityData to 14 Oct 2026

Auction-Based RIS Allocation With DRL: Controlling the Cost-Performance Trade-Off

arXiv 2603.04433 ↗First posted 18 Feb 2026cs.NI cs.LG cs.MAcited by 1

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

We study the allocation of reconfigurable intelligent surfaces (RISs) in a multi-cell wireless network, where base stations compete for control of shared RIS units deployed at the cell edges. These RISs, provided by an independent operator, are dynamically leased to the highest bidder using a simultaneously ascending auction format. Each base station estimates the utility of acquiring additional RISs based on macroscopic channel parameters, enabling a scalable and low-overhead allocation mechanism. To optimize the bidding behavior, we integrate deep reinforcement learning (DRL) agents that learn to maximize performance while adhering to budget constraints. Through simulations in clustered cell-edge environments, we demonstrate that reinforcement learning (RL)-based bidding significantly outperforms heuristic strategies, achieving optimal trade-offs between cost and spectral efficiency. Furthermore, we introduce a tunable parameter that governs the bidding aggressiveness of RL agents, enabling a flexible control of the trade-off between network performance and expenditure. Our results highlight the potential of combining auction-based allocation with adaptive RL mechanisms for efficient and fair utilization of RISs in next-generation wireless networks.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 16 references that are in the map. First posted on arXiv 18 Feb 2026.

Builds on

5 of 16 references.

References · 5

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 220
  3. 2020SuperSuit: Simple Microwrappers for Reinforcement Learning Environmentscited by 10
  4. 2020Energy Efficiency Maximization in RIS-Aided Cell-Free Network with Limited Backhaulcited by 4
  5. 2021System Performance Insights into Design of RIS-assisted Smart Radio Environments for 6Gcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Cooperative Deep Reinforcement Learning for Fair RIS Allocationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024A Hierarchical DRL Approach for Resource Optimization in Multi-RIS Multi-Operator Networks
  2. 2025Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks
  3. 2025Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models
  4. 2024An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models
  5. 2026Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers
  6. 2026Parametrized Sharing for Multi-Agent Hybrid DRL for Multiple Multi-Functional RISs-Aided Downlink NOMA Networks
  7. 2025Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning
  8. 2024Multi-Agent Deep Reinforcement Learning for Energy Efficient Multi-Hop STAR-RIS-Assisted Transmissions