Caching for Doubly Selective Fading Channels via Model-Agnostic Meta-Reinforcement Learning
Weibao He, Fasheng Zhou, Dong Mei Tang, Fang Fang, Wei Chen · IEEE Systems Journal · 2024
Edge caching is expected to alleviate the traffic consumption in next-generation communications. In this article, we consider the transmission delay in wideband communications deteriorated by rapid user movements, where the frequency-selective wideband fading channels become fast time-varying and hencedoubly-selectivedue to the user movements. To preferably allocate the caching resource in such circumstance, we introduce a coordinated caching network and accordingly formulate an allocation problem. However, the formulated problem is shown to be NP-hard. By considering the extremely high computational complexity to solve the NP-hard problem by traditional optimization algorithm, and considering only a few samples can be obtained for each training instance due to shortened coherence-time in the dynamical doubly selective fading channels, we propose a model-agnostic meta-reinforcement learning method to address the formulated problem. Particularly, the proposed method can efficiently recognize the unstable mobile channels and accordingly cache to reduce the overall transmission delay while only requires a few training samples. Numerical simulations are performed to verify the effectiveness of the proposed method and results show that the proposed one outperforms the commonly adopted existing method of deep-deterministic-policy-gradient learning in terms of average delay and cache hit rate.