Deep Reinforcement Learning for Adaptive Resource Allocation in Secure Wireless Networks with Strategic Eavesdroppers
Keywords:
deep reinforcement learning, adaptive resource allocation, secure wireless networks, strategic eavesdroppers, physical layer security, adversarial robustness, network governanceAbstract
Secure wireless networks face a fundamental tension between resource efficiency and confidentiality because the broadcast nature of radio propagation creates opportunities for strategic eavesdroppers. Traditional resource allocation methods often assume passive, stochastic, or worst-case adversaries, which limits their ability to cope with intelligent and adaptive interception strategies. Deep reinforcement learning offers a promising framework for learning resource allocation policies directly from high-dimensional and dynamic wireless environments. This paper presents a system-level examination of deep reinforcement learning for adaptive resource allocation in secure wireless networks where eavesdroppers behave strategically. Rather than focusing on algorithmic derivations, the analysis emphasizes architectural choices, adversarial modeling, structural trade-offs, robustness, fairness, infrastructure requirements, sustainability, and governance. The discussion highlights that deep reinforcement learning can capture nonstationary adversarial behavior and support continuous decision-making across power control, beamforming, cooperative jamming, channel selection, and user scheduling. However, practical deployment requires careful consideration of training stability, exploration safety, energy consumption, interpretability, and regulatory accountability. The paper further argues that secure resource allocation cannot be treated solely as an optimization problem because strategic eavesdroppers introduce multi-agent dynamics and incentive misalignment. A layered governance architecture with continuous monitoring, human oversight, and standardized certification is necessary to align adaptive learning systems with broader security and societal goals.
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