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Optimization-Driven Deep Reinforcement Learning for Wireless Networks

Optimization-Driven Deep Reinforcement Learning for Wireless Networks

Hardcover

Series: Wireless Networks

Technology & EngineeringNetworkingProbability & Statistics

ISBN10: 3032229960
ISBN13: 9783032229960
Publisher: Springer
Published: May 28 2026
Pages: 209
Weight: 1.11
Height: 0.56 Width: 6.14 Depth: 9.21
Language: English
This book explores the integration and interplay of model-based optimization and model-free deep reinforcement learning (DRL). It addresses the growing complexity of future wireless networks. This book begins with a concise overview of foundational DRL algorithms and then delves into advanced frameworks, including optimization-driven DRL, hierarchical DRL, multi-agent DRL, Bayesian-enhanced DRL, and Lyapunov-guided DRL. Each framework is illustrated through case studies in emerging scenarios such as intelligent reflecting surface (IRS)-assisted wireless communications, UAV-assisted wireless networks, backscatter-assisted relay communications, and mobile edge computing.

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