Two-dimensional Anti-jamming Mobile Communication Based on Reinforcement Learning
By using smart radio devices, a jammer can dynamically change its jamming policy based on opposing security mechanisms; it can even induce the mobile device to enter a specific communication mode and then launch the jamming policy accordingly. On the other hand, mobile devices can exploit spread spectrum and user mobility to address both jamming and interference. In this paper, a two-dimensional anti-jamming mobile communication scheme is proposed in which a mobile device leaves a heavily jammed/interfered-with frequency or area. It is shown that, by applying reinforcement learning techniques, a mobile device can achieve an optimal communication policy without the need to know the jamming and interference model and the radio channel model in a dynamic game framework. More specifically, a hotbooting deep Q-network based two-dimensional mobile communication scheme is proposed that exploits experiences in similar scenarios to reduce the exploration time at the beginning of the game, and applies deep convolutional neural network and macro-action techniques to accelerate the learning speed in dynamic situations. Several real-world scenarios are simulated to evaluate the proposed method. These simulation results show that our proposed scheme can improve both the signal-to-interference-plus-noise ratio of the signals and the utility of the mobile devices against cooperative jamming compared with benchmark schemes.
Code (0)
등록된 구현이 없습니다.
Tasks
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Vocal Bursts Valence PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Delay Minimization for Movable Antennas-Enabled Anti-Jamming Communications With Mobile Edge Computing
In future 6G networks, anti-jamming will become a critical challenge, particularly with the development of intelligent jammers that can initiate malicious interference, posing a significant security threat to communicati…
Edge-computingPositionMobile Jamming Mitigation in 5G Networks: A MUSIC-Based Adaptive Beamforming Approach
Mobile jammers pose a critical threat to 5G networks, particularly in military communications. We propose an intelligent anti-jamming framework that integrates Multiple Signal Classification (MUSIC) for high-resolution D…
Computational EfficiencyIntelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning
Malicious jamming launched by smart jammer, which attacks legitimate transmissions has been regarded as one of the critical security challenges in wireless communications. Thus, this paper exploits intelligent reflecting…
reinforcement-learningReinforcement Learning (RL)QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning
The problem of quality of service (QoS) and jamming-aware communications is considered in an adversarial wireless network subject to external eavesdropping and jamming attacks. To ensure robust communication against jamm…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Detection and blind channel estimation for UAV-aided wireless sensor networks in smart cities under mobile jamming attack
Unmanned aerial vehicles (UAVs) can be integrated into wireless sensor networks (WSNs) for smart city applications in several ways. Among them, a UAV can be employed as a relay in a "store-carry and forward" fashion by u…