论文标题
通过决策辅助的强化学习，在物联网中延迟限制缓冲区的接力赛选择
Delay Constrained Buffer-Aided Relay Selection in the Internet of Things with Decision-Assisted Reinforcement Learning
论文作者
论文摘要
本文调查了在延迟约束缓冲区网络中接力选择的加强学习。缓冲区的继电器选择显着提高了中断性能，但通常以更高的延迟价格提高。另一方面，诸如物联网之类的现代通信系统通常对潜伏期有严格的要求。因此，有必要找到继电器选择策略，以在缓冲辅助继电器网络中实现良好的吞吐量性能，同时分层延迟约束。随着继电器上使用的缓冲区并延迟对数据传输施加的延迟约束，获得最佳的继电器选择成为一个复杂的高维问题，这使得强化学习很难收敛。在本文中，我们提出了新的决策辅助深入的强化学习，以改善融合。这是通过探索缓冲区中继系统的A-Priori信息来实现的。提出的方法可以实现高通量对延迟约束的影响。提供广泛的仿真结果以验证提出的算法。
This paper investigates the reinforcement learning for the relay selection in the delay-constrained buffer-aided networks. The buffer-aided relay selection significantly improves the outage performance but often at the price of higher latency. On the other hand, modern communication systems such as the Internet of Things often have strict requirement on the latency. It is thus necessary to find relay selection policies to achieve good throughput performance in the buffer-aided relay network while stratifying the delay constraint. With the buffers employed at the relays and delay constraints imposed on the data transmission, obtaining the best relay selection becomes a complicated high-dimensional problem, making it hard for the reinforcement learning to converge. In this paper, we propose the novel decision-assisted deep reinforcement learning to improve the convergence. This is achieved by exploring the a-priori information from the buffer-aided relay system. The proposed approaches can achieve high throughput subject to delay constraints. Extensive simulation results are provided to verify the proposed algorithms.
