Anti-Jamming Communications in UAV Swarms: A Reinforcement Learning Approach

Jinlin Peng,Bo Zhang,Qinhao Wu,Zixuan Zhang

doi:10.1109/access.2019.2958328

Jinlin Peng, Bo Zhang + Show 2 more

Open Access

https://doi.org/10.1109/access.2019.2958328

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Journal: IEEE Access	Publication Date: Jan 1, 2019
Citations: 37	License type: CC BY 4.0

Affiliation: National University of Defense Technology

Abstract

Intelligent unmanned aerial vehicle (UAV) swarm may accomplish complex tasks through cooperation, relying on inter-UAV communications. This paper aims to improve the communication performance of intelligent UAV swarm system in the presence of jamming, by multi-parameter programming and reinforcement learning. This paper considers a communication system, where the communication between a UAV swarm and the base station is jammed by multiple interferers. Compared with the existing work, the UAVs in the system can exploit degree-of-freedom in frequency, motion and antenna spatial domain to optimize the communication quality in the receiving area. This paper proposes a modified Q-Learning algorithm based on multi-parameter programming, where a cost is introduced to strike a balance between the motion and communication performance of the UAVs. The simulation results show the effectiveness of the algorithm.

Highlights

With the rapid development of artificial intelligence, intelligent unmanned aerial vehicle (UAV) is widely used in daily life [1], [2]
This paper focuses on the multi-dimensional programming of complex UAV communication networking
A framework of UAV intelligent communication system based on ‘‘frequency-motion-antenna’’ is established, and various parameters are set for each agent to programme

Summary

INTRODUCTION

With the rapid development of artificial intelligence, intelligent unmanned aerial vehicle (UAV) is widely used in daily life [1], [2]. Under the condition of multiple UAV receivers, the algorithm tunes the antenna beam for improving the overall communication quality of the receiving UAVs. This paper is organized as follows: In Section 2, we review the recent work related to networked UAV communication and anti-jamming based on reinforcement learning. Reference [20] extended the parameters of communication system to joint frequency-motion programming based on a hotbooting deep Q-network It accelerated the iteration process and improved the ability of the agent to resist jamming. Most research of UAV ad-hoc network considered power allocation, adaptive coding and modulation according to mitigate jamming from the environments, which achieve better communication quality and data transmission rate. That is, when the antenna mainlobe is wider than 30◦, the maximum value of the pattern is less than 1.0, otherwise it is greater than 1.0

PROBLEM FORMULATION

MULTI-DIMENSIONAL ANTI-JAMMING REINFORCEMENT LEARNING ALGORITHM

CONCLUSION