Reinforcement Learning for Interference Avoidance Game in RF-Powered Backscatter Communications

Abstract

RF-powered backscatter communication is a promising new technology that can be deployed for battery-free applications such as internet of things (IoT) and wireless sensor networks (WSN). However, since this kind of communication is based on the ambient RF signals and battery-free devices, they are vulnerable to interference and jamming. In this paper, we model the interaction between the user and a smart interferer in an ambient backscatter communication network as a game. We design the utility functions of both the user and interferer in which the backscattering time is taken into the account. The convexity of both sub-game optimization problems is proved and the closed-form expression for the equilibrium of the Stackelberg game is obtained. Due to lack of information about the system SNR and transmission strategy of the interferer, the optimal strategy is obtained using the Q-learning algorithm in a dynamic iterative manner. We further introduce hotbooting Q-learning as an effective approach to expedite the convergence of the traditional Q-learning. Simulation results show that our approach can obtain considerable performance improvement in comparison to random and fixed backscattering time transmission strategies and improves the convergence speed of Q-Learning by about 31%.

Learning-Based Resource Allocation for Backscatter-Aided Vehicular Networks

“…There exist a few studies on backscatter communication that employ machine learning techniques [40], [41]. For instance, the authors of [42] used a supervised machine learning technique (support vector machine) to detect the signal from a backscatter tag by transforming the tag detection into a classification task.…”

Reinforcement Learning for Scalable and Reliable Power Allocation in SDN-based Backscatter Heterogeneous Network

“…In [15], the problem of vehicle-to-vehicle (V2V) transmission of the message was considered. However, there exist a few studies on backscatter communication that employ machine learning techniques [16], [17]. For instance, the authors of [18] used a supervised machine learning technique (support vector machine) to detect the signal from a backscatter tag by transforming the tag detection into a classification task.…”

Prioritized Uplink Resource Allocation in Smart Grid Backscatter Communication Networks via Deep Reinforcement Learning

“…In the future, we will study more flexible backscatter communication systems combined with D2D. More factors will be considered, such as smart jamming attack [20], concurrent transmission, channel conflict [28], signal power, reflection coefficient and energy conversion efficiency. As for solution methods, communication systems are matched with RL algorithms because of their interactive characteristics; therefore, we will give priority to DRL algorithms that have a lot of potential in communication scenarios.…”

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