mlsanityData to 14 Oct 2026

Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks

arXiv 2502.01129 ↗First posted 3 Feb 2025cs.DC cs.AI cs.ETcited by 0

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

Abstract (arXiv)

This report investigates the application of deep reinforcement learning (DRL) algorithms for dynamic resource allocation in wireless communication systems. An environment that includes a base station, multiple antennas, and user equipment is created. Using the RLlib library, various DRL algorithms such as Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) are then applied. These algorithms are compared based on their ability to optimize resource allocation, focusing on the impact of different learning rates and scheduling policies. The findings demonstrate that the choice of algorithm and learning rate significantly influences system performance, with DRL providing more efficient resource allocation compared to traditional methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 7 references that are in the map. First posted on arXiv 3 Feb 2025.

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3 of 7 references.

References · 3

  1. 2020Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learningcited by 42
  2. 2017Implications of Decentralized Q-learning Resource Allocation in Wireless Networkscited by 6
  3. 2014Context-Aware Resource Allocation in Cellular Networkscited by 1

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  1. 2025Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models
  2. 2025Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning
  3. 2026From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks
  4. 2024ML Framework for Wireless MAC Protocol Design
  5. 2025Meta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks
  6. 2024Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications
  7. 2025(DEMO) Deep Reinforcement Learning Based Resource Allocation in Distributed IoT Systems
  8. 2024DRL Optimization Trajectory Generation via Wireless Network Intent-Guided Diffusion Models for Optimizing Resource Allocation