Offline and Distributional Reinforcement Learning for Wireless Communications

• URL: http://arxiv.org/abs/2504.03804v1
• Date: Fri, 04 Apr 2025 09:24:39 GMT
• Title: Offline and Distributional Reinforcement Learning for Wireless Communications
• Authors: Eslam Eldeeb, Hirley Alves,
• Abstract summary: Traditional online reinforcement learning (RL) and deep RL methods face limitations in real-time wireless networks.<n>We focus on offline and distributional RL, two advanced RL techniques that can overcome these challenges.<n>We introduce a novel framework that combines offline and distributional RL for wireless communication applications.
• Score: 5.771885923067511
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid growth of heterogeneous and massive wireless connectivity in 6G networks demands intelligent solutions to ensure scalability, reliability, privacy, ultra-low latency, and effective control. Although artificial intelligence (AI) and machine learning (ML) have demonstrated their potential in this domain, traditional online reinforcement learning (RL) and deep RL methods face limitations in real-time wireless networks. For instance, these methods rely on online interaction with the environment, which might be unfeasible, costly, or unsafe. In addition, they cannot handle the inherent uncertainties in real-time wireless applications. We focus on offline and distributional RL, two advanced RL techniques that can overcome these challenges by training on static datasets and accounting for network uncertainties. We introduce a novel framework that combines offline and distributional RL for wireless communication applications. Through case studies on unmanned aerial vehicle (UAV) trajectory optimization and radio resource management (RRM), we demonstrate that our proposed Conservative Quantile Regression (CQR) algorithm outperforms conventional RL approaches regarding convergence speed and risk management. Finally, we discuss open challenges and potential future directions for applying these techniques in 6G networks, paving the way for safer and more efficient real-time wireless systems.

Related papers

• DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey [27.386915138058416]
  Reinforcement learning (RL)-based large language models (LLMs) have gained significant attention.<n> Wireless networks require the empowerment of RL-based LLMs.<n> Wireless networks provide a vital infrastructure for the efficient training, deployment, and distributed inference of RL-based LLMs.
  arXiv  Detail & Related papers  (2025-03-13T01:59:11Z)
• Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning [5.771885923067511]
  This work proposes a novel, resilient, few-shot meta-offline RL algorithm combining offline RL and model-agnostic meta-learning. We show that the proposed few-shot meta-offline RL algorithm converges faster than baseline schemes. It is the only algorithm that can achieve optimal joint AoI and transmission power using an offline dataset.
  arXiv  Detail & Related papers  (2025-02-03T11:39:12Z)
• DRL Optimization Trajectory Generation via Wireless Network Intent-Guided Diffusion Models for Optimizing Resource Allocation [58.62766376631344]
  We propose a customized wireless network intent (WNI-G) model to address different state variations of wireless communication networks. Extensive simulation achieves greater stability in spectral efficiency and variations of traditional DRL models in dynamic communication systems.
  arXiv  Detail & Related papers  (2024-10-18T14:04:38Z)
• Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning [33.48496141312585]
  Reinforcement learning (RL) has been widely adopted for controlling and optimizing complex engineering systems such as next-generation wireless networks. An important challenge in adopting RL is the need for direct access to the physical environment. We propose an offline MARL scheme that integrates distributional RL and conservative Q-learning to address the environment's inherent aleatoric uncertainty.
  arXiv  Detail & Related papers  (2024-02-13T12:49:22Z)
• Advancing RAN Slicing with Offline Reinforcement Learning [15.259182716723496]
  This paper introduces offlineReinforcement Learning to solve the RAN slicing problem. We show how offline RL can effectively learn near-optimal policies from sub-optimal datasets. We also present empirical evidence of the efficacy of offline RL in adapting to various service-level requirements.
  arXiv  Detail & Related papers  (2023-12-16T22:09:50Z)
• Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces [56.35676570414731]
  The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments. RISs offer a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces.
  arXiv  Detail & Related papers  (2022-05-08T06:21:33Z)
• Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence [76.96698721128406]
  Mobile edge computing (MEC) considered a novel paradigm for computation and delay-sensitive tasks in fifth generation (5G) networks and beyond. This paper provides a comprehensive research review on free-enabled RL and offers insight for development.
  arXiv  Detail & Related papers  (2022-01-27T10:02:54Z)
• Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks [82.02891936174221]
  Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can coordinate over a wireless network is a promising approach. In this paper, a novel semantic-aware CDRL method is proposed to enable a group of untrained agents with semantically-linked DRL tasks to collaborate efficiently across a resource-constrained wireless cellular network.
  arXiv  Detail & Related papers  (2021-11-23T18:24:47Z)
• Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning [58.44609538048923]
  Radio Frequency powered Cognitive Radio Networks (RF-CRN) are likely to be the eyes and ears of upcoming modern networks such as Internet of Things (IoT) To be considered autonomous, the RF-powered network entities need to make decisions locally to maximize the network throughput under the uncertainty of any network environment. In this paper, deep reinforcement learning is proposed to overcome the shortcomings and allow a wireless gateway to derive an optimal policy to maximize network throughput.
  arXiv  Detail & Related papers  (2020-07-07T01:49:07Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.