Pattern-Aware Intelligent Anti-Jamming Communication: A Sequential Deep Reinforcement Learning Approach

Abstract

This paper investigates the problem of anti-jamming communication in dynamic and intelligent jamming environment. A sequential deep reinforcement learning algorithm (SDRLA) without prior information is proposed, and raw spectrum information is used as the input of SDRLA. The proposed SDRLA algorithm mainly contains two parts: Firstly, deep learning and sliding window principle are introduced to identify jamming patterns; Secondly, reinforcement learning is carried out to make on-line channel selection based on recognized jamming patterns. In addition, channel switching cost is introduced for the purpose of formulating the trade-off relationship between throughput and overhead. Taking advantage of both deep learning and reinforcement learning, this method can realize rapid and effective anti-jamming channel selection with no need for modeling the jammer's characteristics. Simulation results show the convergence and effectiveness of the proposed SDRLA algorithm. Compared with single-mode reinforcement learning, our approach can reach better convergence performance and higher utility. INDEX TERMS Pattern-aware, reinforcement learning (RL), deep learning (DL), anti-jamming communication, channel switching cost.

Deep Multi-Task Conditional and Sequential Learning for Anti-Jamming

“…This work focuses on learningbased anti-jamming for spectral retreat strategy against both jamming attacks and interference among nodes. Antijamming learning-based systems have been extensively studied in different approaches which, recently, focus on improving the learning algorithm's performance by solving jamming in multi-dimensional or multi-layers [12]- [15], handling the lack of meaningful or clear jammer's information [16]- [18], or improving on the slowness of the algorithms [19]- [22].…”

An Intelligent Anti-Jamming Scheme for Cognitive Radio Based on Deep Reinforcement Learning

“…Bi et al [24] designed a multi-user anti-jamming strategy based on deep Q learning to achieve global optimization for multi-user system. A sequential deep reinforcement learning algorithm is studied in [25] to confront with multiple jammers. [26] proposed a fast DQN-based anti-jamming mobile communication scheme to cope with jamming attacks.…”

“…In our simulation, the transmitter-receiver pair and the jammer combat with each other. Following existing works [11], [25], they combat with each other in a frequency band of 20MHz. Specifically, we uniformly select 5 (N C =5) center frequencies (f ={53, 57, 61, 65, 68}MHz) as candidate frequency channels.…”

Intelligent Reflecting Surface Assisted Anti-Jamming Communications: A Fast Reinforcement Learning Approach

“…In [13], a policy hill climbing (PHC)-based Q-Learning approach was studied to improve the communication performance against jamming without knowing the jamming model. In [14] and [15], the authors adopted deep reinforcement learning (DRL) algorithms that enable transmitters to quickly obtain an optimal policy to guarantee security performance against jamming.…”

“…However, despite the effectiveness of the above mentioned anti-jamming schemes [3]- [15], employing a number of active relays incurs an excessive hardware cost, and anti-jamming beamforming and power control in communication systems is generally energy-consuming. To tackle these shortcomings, a new paradigm, called intelligent reflecting surface (IRS) [16], [17], has been recently proposed as a promising technique to enhance the secrecy performance.…”