Deep Reinforcement Learning MAC for Backscatter Communications Relying on Wi-Fi Architecture

In this paper, we propose a distributed deep reinforcement learning (DRL) based medium access control (MAC) protocol, termed DRL-MAC, which is used to assist the backscatter communications for Internet-of-Things (IoT) networks. By leveraging the current Wi-Fi infrastructure, the backscatter communic...

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Bibliographic Details
Main Authors: Cao, Xuelin, Song, Zuxun, Yang, Bo, Du, Xunsheng, Qian, Lijun, Han, Zhu
Format: Conference Proceeding
Language:English
Subjects:
Backscatter
Machine learning
Media Access Protocol
Optimization
Smart devices
Wireless fidelity
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Summary:In this paper, we propose a distributed deep reinforcement learning (DRL) based medium access control (MAC) protocol, termed DRL-MAC, which is used to assist the backscatter communications for Internet-of-Things (IoT) networks. By leveraging the current Wi-Fi infrastructure, the backscatter communications can be reserved in advance to avoid the interference from Wi-Fi communications. In the proposed MAC protocol, the deep reinforcement learning is further introduced to learn the reserved information and make decisions such as 1) which backscatter device (TAG) will be serviced, and 2) the reservation step for the serviced TAG. In addition, the utility function is defined and the optimization problem is formulated to balance the backscatter communications and Wi-Fi communications. Moreover, a DRL algorithm is proposed to obtain the optimal strategy. The numerical results show the effectiveness of the proposed MAC for backscatter communications.
ISSN:2576-6813
DOI:10.1109/GLOBECOM38437.2019.9013445