Low latency is a critical requirement of beyond 5G services. Previously, the aspect of latency has been extensively analyzed in conventional and modern wireless networks. With the rapidly growing research interest in wireless-powered ambient backscatter communications, it has become ever more important to meet the delay constraints, while maximizing the achievable data rate. Therefore, to address the issue of latency in backscatter networks, this paper provides a deep Q-learning based framework for delay constrained ambient backscatter networks. To do so, a Q-learning model for ambient backscatter scenario has been developed. In addition, an algorithm has been proposed that employ deep neural networks to solve the complex Q-network. The simulation results show that the proposed approach not only improves the network performance but also meets the delay constraints for a dense backscatter network.


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    Title :

    Low Latency Ambient Backscatter Communications with Deep Q-Learning for Beyond 5G Applications


    Contributors:


    Publication date :

    2020-05-01


    Size :

    290920 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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