Anti-Jamming RIS Communications Using DQN-Based Algorithm

Abstract

Wireless sensor networks (WSNs) are widely applied in battlefield surveillance, where the data collection (e.g. target tracking or contention zone observation) employed by local sensor nodes needs to send to military bases for tactical decisions. Since data transmissions are susceptible to malicious attacks, jammers of adversaries can successfully block their victim's communications by transmitting interfering signals to legitimate transmissions. Nowadays, owing to the ability to reconfigure the wireless propagation medium, reconfigurable intelligent surface (RIS) is regarded as an effective tool to enhance transmission performance, especially in the jamming context. This paper considers the anti-jamming communication tactical scenario of a solar-powered RIS network, in which the RIS is used to improve the uplink transmission performance between a wireless device (WD) and a base station (BS). We investigate the long-term anti-jamming communications of the WD powered by a solar energy harvester. Our objective is to jointly assign the optimal amount of transmission energy and the RIS phase shifts to maximize the data rate of the system in the long run. To this end, we formulate an anti-jamming communication optimization problem as a Markov decision process (MDP) framework and then design a deep Q network (DQN)-based algorithm to generate an optimal policy. As a result, the optimal resource allocation is achieved through trial-and-error interactions with environment by observing the predefined rewards and the network state transition. The Python simulation results conducted by averaging 10 4 time slots show that the proposed algorithm is not only able to learn from environment, but also yields better performance than baseline schemes under network changes. Moreover, the performance of RIS communication schemes is verified to be superior to that of without-RIS communication schemes in the jamming context.INDEX TERMS reconfigurable intelligent surface, deep reinforcement learning, markov decision process, solar-powered network, jamming attack, battlefield surveillance.

A Novel Intelligent Anti-Jamming Algorithm Based on Deep Reinforcement Learning Assisted by Meta-Learning for Wireless Communication Systems

“…In the simulations, the proposed Meta-PPO algorithm implemented its learning processes for 1000 episodes and 10 iterations for parameter updates in each episode. Figure 9 shows the curves of normalized network throughput changing with the episodes when the proposed Meta-PPO algorithm was used, which was compared with PPO [31], DQN [32], Double DQN [33], and Dueling DQN [34] algorithms. Normalized throughput is a widely accepted criterion for measuring the performance of an agent's strategy [35].…”

RIS-Assisted Robust Beamforming for UAV Anti-Jamming and Eavesdropping Communications: A Deep Reinforcement Learning Approach

“…As stated in Section 2, the imperfect CSI results in infinite non-convexity in both the objective function and constraints. With this as the focus, according to the works [28][29][30], the equivalent worst-case CSI of the illegitimate channel that can be obtained through utilizing the discretization method is given, respectively, by…”

“…Store the experience transitions (s t , a t , r t , s t+1 ). Sample mini-batch transitions based on PER using (30), and then, update the priority of the selected transition based on its TD error.…”

Controllable Multiple Active Reconfigurable Intelligent Surfaces Assisted Anti-Jamming Communication

“…An anti-jamming method was proposed in [2], in which the transmitting beam and RIS phase shift were joint optimized. Furthermore, the performance of RIS-assisted wireless communication under the conditions of simultaneous jamming and eavesdropping attacks was studied in [5,6]. However, the above papers assume that the BS accurately knows about prior information such as jamming power or jamming channel, which often fail in practical situations.…”

“…However, the above papers assume that the BS accurately knows about prior information such as jamming power or jamming channel, which often fail in practical situations. In [5,7], the optimization and regulation methods of RIS parameters were studied under the conditions of jamming perception error and uncertainty in the direction of jamming. In [8], the impact of the correlation between interference and communication signals on the performance of communication systems was studied.…”