Titlebook: Deep Reinforcement Learning for Wireless Networks; F. Richard Yu,Ying He Book 2019 The Author(s), under exclusive license to Springer Natu

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Book 2019t learning approach is used in cache-enabled opportunistic interference alignment wireless?networks and mobile social networks. Simulation results with different network parameters are?presented to show the effectiveness of the proposed scheme...?There is a phenomenal burst of research activities in

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Deep Reinforcement Learning for Interference Alignment Wireless Networks,e-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this chapter, we consider realistic time-varying channels. Specifically, the channel is formulated as a finite-state Markov channel (FS

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