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

TL;DR

This paper proposes an auction-based RIS allocation method in multi-cell networks, utilizing deep reinforcement learning to optimize bidding strategies for cost-effective performance improvements.

Contribution

It introduces a novel DRL-based bidding mechanism for RIS allocation, enabling scalable, adaptive, and cost-efficient control in wireless networks.

Findings

RL-based bidding outperforms heuristic strategies

Tunable parameter controls cost-performance trade-off

Achieves efficient RIS utilization in simulations

Abstract

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.