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Deep Reinforcement Learning for Autonomous Communication Networks: Resource Allocation, Spectrum Management, and Control

D. A. Kumar Jakkula Rakshitha Madugula Pranush
Jul 2026 · International Scientific Journal of Engineering and Management · Vol 05, pp. 1-9 · 0 citations

TL;DR

This paper (or study) explores the application of reinforcement learning techniques to autonomous communication networks, including resource allocation, spectrum management, power control, routing, and congestion control.

Abstract

ABSTRACT Autonomous communication systems are evolving toward self-organizing, adaptive networks capable of optimizing performance under dynamic and uncertain environments. Traditional rule-based and model-driven optimization techniques struggle to cope with the complexity, scale, and non-stationarity of modern wireless and networked systems. Reinforcement learning (RL), a branch of machine learning where agents learn optimal policies through interaction with the environment, has emerged as a powerful paradigm for enabling autonomy in communication systems. This paper (or study) explores the application of reinforcement learning techniques to autonomous communication networks, including resource allocation, spectrum management, power control, routing, and congestion control. By formulating communication tasks as Markov Decision Processes (MDPs), RL agents can learn to maximize long-term performance metrics such as throughput, latency, energy efficiency, and quality of service without requiring explicit mathematical models of the environment. Deep reinforcement learning (DRL), which integrates deep neural networks with RL, further enhances scalability by handling high-dimensional state and action spaces typical in modern networks such as 5G, 6G, and Internet of Things (IoT) systems. Multi-agent reinforcement learning (MARL) is also increasingly relevant, enabling distributed decision-making among multiple network nodes with partial observability and limited coordination. Despite its promise, RL-based communication systems face challenges including sample inefficiency, convergence stability, safety constraints, and real-time deployment limitations. Ongoing research focuses on improving training efficiency, incorporating domain knowledge, ensuring reliability, and developing hybrid models that combine RL with optimization and control theory. Overall, reinforcement learning provides a foundational framework for next-generation autonomous communication systems, enabling adaptive, intelligent, and self-optimizing networks. Keywords: Reinforcement Learning, Autonomous Communication Systems, Deep Reinforcement Learning, Multi-Agent Systems, Wireless Networks, Resource Allocation, Spectrum Management, Markov Decision Process, 5G/6G Networks, Internet of Things (IoT), Network Optimization, Self-Organizing Networks, Policy Learning, Dynamic Systems Optimization

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