Abstract
The development of the next-generation wireless networks are regarded as the essentials to embrace of Internet of Things (IoT) and edge computing in heterogeneous networks (HetNets). Due to the the spectrum scarcity problem and the large number of connectivity demand of IoT users, intelligent interference management for IoT is worthy of thorough investigation and should be well discussed with consideration on edge computing in heterogeneous networks (HetNets). Two crucial challenges in the context are: 1) placing edge cache based on dynamic request of IoT users, and 2) cache-enabled interference management with time-varying wireless channels. In this paper, we proposed smart edge caching-aided partial opportunistic interference alignment(POIA) with deep reinforcement learning for IoT downlink system in HetNets. Towards this end, the proposed scheme can update the base station (BS) cache dynamically, and then select the optimal cache-enabled POIA user group considering the time-varying user’s requests and time-varying wireless channels. To solve this problem efficiently, the reinforcement learning is exploited that can take advantage of a deep Q-learing to replace the system action. Extensive evaluations demonstrate that the proposed method is effectiveness according to sum rate and energy efficiency of IoT downlink transmission for HetNets.
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Acknowledgements
This work was supported in part by National Key R&D Program of China(2019YFC1521400), National Natural Science Foundation of China (61701400, 61672426 and 61902229), by Project Funded by China Postdoctoral Science Foundation (2017M613188), by Natural Science Basic Research Plan in Shaanxi Province of China (17JK0783 and 2019JQ-271).
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Zheng, J., Gao, L., Wang, H. et al. Smart Edge Caching-Aided Partial Opportunistic Interference Alignment in HetNets. Mobile Netw Appl 25, 1842–1850 (2020). https://doi.org/10.1007/s11036-020-01568-6
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DOI: https://doi.org/10.1007/s11036-020-01568-6