데이터 적시성(適時性)을 고려한 내쉬협상-심층강화학습 기반 순차적 스펙트럼 할당 기법

초록

This study proposes a novel wireless spectrum allocation method aimed at ensuring smooth sharing of limited spectrum resources,minimizing data latency, and enabling efficient data transmission. The Nash Bargaining Solution (NBS) is a mathematical approach thatfairly distributes limited resources while maximizing the benefits for all negotiating parties. Additionally, reinforcement learning providesa method to efficiently allocate spectrum by learning network conditions and demand variations in dynamic environments, therebyminimizing resource waste and optimizing network performance. To address this issue, the proposed method employs a two-stage controlmechanism that leverages the concepts of the Nash Bargaining Solution and the Double Deep Q-Network (DDQN) reinforcement learningalgorithm. In the first stage, spectrum pricing is dynamically determined, and spectrum allocation is performed based on the NashBargaining Solution. In the second stage, individual IoE devices select their spectrum requests through deep reinforcement learning. Through sequential interactions among intelligent IoE devices, the proposed two-stage control approach explores synergies to optimizethe spectrum allocation process. Finally, simulation results demonstrate that the jointly designed control method effectively guides individualdevices to select cooperative strategies beneficial to overall system efficiency. The method outperforms existing protocols in terms ofservice delay, network throughput, and fairness among devices.

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