Multi-Agent Deep Reinforcement Learning for Mobile Wireless Systems: From Distributed Power Allocation to Auction-Based RIS Access
Year: 2025
Type: article
Abstract: Wireless systems are becoming increasingly complex, with a growing number of parameters to tune, a rising variety and heterogeneity of devices and equipment, and continuously evolving, diverse quality-of-service requirements. While centralized optimization may be theoretically optimal, it is often impractical in real-world deployments. This creates a need for methods that support distributed optimization and coordination among the goals of individual agents (e.g., users, operators, applications), while maintaining or improving network efficiency with manageable computational effort. In this talk, we explore the principles behind using deep reinforcement learning (DRL) as a promising approach for optimizing distributed multi-agent wireless systems. We illustrate its application to cell-free MIMO power allocation and the assignment of reconfigurable intelligent surfaces (RISs) in multi-operator scenarios, highlighting both the potential benefits and the challenges introduced by non-stationary multi-agent environments.
Source: reposiTUm (TU Wien)
DOI: 10.34726/11739
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7124754006
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