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- W2909591731 abstract "Both caching and interference alignment (IA) are promising techniques for next generation wireless networks. Nevertheless, most existing works on cache-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 (FSMC). The complexity of the system is very high when we consider realistic FSMC models. Therefore, in this chapter, we propose a novel deep reinforcement learning approach, which is an advanced reinforcement learning algorithm that uses deep Q network to approximate the Q value-action function. We use Google TensorFlow to implement deep reinforcement learning in this chapter to obtain the optimal IA user selection policy in cache-enabled opportunistic IA wireless networks. Simulation results are presented to show that the performance of cache-enabled opportunistic IA networks in terms of the network’s sum rate and energy efficiency can be significantly improved by using the proposed approach." @default.
- W2909591731 created "2019-01-25" @default.
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- W2909591731 date "2019-01-01" @default.
- W2909591731 modified "2023-09-24" @default.
- W2909591731 title "Deep Reinforcement Learning for Interference Alignment Wireless Networks" @default.
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- W2909591731 doi "https://doi.org/10.1007/978-3-030-10546-4_3" @default.
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