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

arXiv: https://arxiv.org/abs/1903.03600 · HTML: https://mlsanity.com/paper/1903.03600/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2019.

## Built on by (most cited first, 1 of 1)

- [When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges](https://mlsanity.com/paper/2201.04677/) (2022)

## Abstract (arXiv)

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%.