Mitigating Jamming Attack in 5G Heterogeneous Networks: A Federated Deep Reinforcement Learning Approach

Sharma, Himanshu; Kumar, Neeraj; Tekchandani, Rajkumar

doi:10.1109/tvt.2022.3212966

Cited by 28 publications

(10 citation statements)

References 48 publications

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“…However, in the case of intentional interference, this is usually insufficient. Hence, novel jamming detection [39]- [42] and mitigation [43], [44] techniques are also being researched and developed in relation to the 5G-NR standard. In [41], the author proposed a new metric for jamming detection in OFDM-based systems which may be used in both the time and frequency domains, and be implemented separately in each physical resource block.…”

Section: A Related Workmentioning

confidence: 99%

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Practical Trial for Low-Energy Effective Jamming on Private Networks With 5G-NR and NB-IoT Radio Interfaces

Skokowski,

Malon,

Kryk

et al. 2024

IEEE Access

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under project no. UGB/22-863/2023/WAT on "Modern technologies of wireless communication and emitter localization in various system applications."ABSTRACT Fourth-generation (4G) mobile networks are successively replaced by fifth-generation (5G) ones, based on the new releases of the 3rd Generation Partnership Project (3GPP) standard. 5G generation is dedicated to civilian users and the conducted analytical work shows that it has numerous technological gaps that prevent its direct implementation in military communications systems. However, the recent armed world conflicts showed that closed or public mobile networks are willingly used by soldiers for both private and business communications, and to conduct defensive and offensive operations as well. From the military operation viewpoint, jamming both civil and military systems is one of the essential elements of electronic warfare. This paper focuses on the practical trial of low-energy and smart jamming on a 5G private network using narrowband signals, which facilitates the reduction of the available throughput, e.g. in the time division duplex -uplink (TDD-UL) by 99%, or by 82% in the frequency division duplex -downlink (FDD-DL). This type of jamming also allows for reaching up to 25 dB of energy gain comparing to barrage jamming. The authors moreover investigated jamming the Narrowband IoT radio interface using synchronized, selective jamming. The goal was to propose energy efficient methods that will allow the jammers to work longer and be mounted on a small unmanned aerial vehicle (UAV) that can operate near the gNB. The generation of low-power jamming signals in the gNB vicinity successfully hinders detecting the jammer by the enemy's electronic reconnaissance systems. The proposed solutions are compared with the test results for other types of jamming methods.INDEX TERMS Electronic warfare, low-energy jamming, narrowband jamming, NB-IoT, 5G private network, smart jamming.

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Section: A Related Workmentioning

confidence: 99%

“…These methods can also be adopted in interference mitigation techniques. An example is [44], where a federated deep reinforcement learning (DRL)-based anti-jamming technique for twotier 5G heterogeneous networks (HetNets) has been proposed.…”

Section: A Related Workmentioning

confidence: 99%

Practical Trial for Low-Energy Effective Jamming on Private Networks With 5G-NR and NB-IoT Radio Interfaces

Skokowski,

Malon,

Kryk

et al. 2024

IEEE Access

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“…Many ECCMs can suppress the same jamming mode, and one ECCM can also weaken multiple forms of jamming [31]. Therefore, in a complex electromagnetic environment, the scale of the radar anti-jamming knowledge base is becoming larger and larger, resulting in a large action space and a long convergence time in the optimization process of ECCMs [32]. There are many other RL algorithms.…”

Section: Introductionmentioning

confidence: 99%

Radar Anti-Jamming Decision-Making Method Based on DDPG-MADDPG Algorithm

Wei

Yan

et al. 2023

Remote Sensing

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In the face of smart and varied jamming, intelligent radar anti-jamming technologies are urgently needed. Due to the variety of radar electronic counter-countermeasures (ECCMs), it is necessary to efficiently optimize ECCMs in the high-dimensional knowledge base to ensure that the radar achieves the optimal anti-jamming effect. Therefore, an intelligent radar anti-jamming decision-making method based on the deep deterministic policy gradient (DDPG) and the multi-agent deep deterministic policy gradient (MADDPG) (DDPG-MADDPG) algorithm is proposed. Firstly, by establishing a typical working scenario of radar and jamming, we designed the intelligent radar anti-jamming decision-making model, and the anti-jamming decision-making process was formulated. Then, aiming at different jamming modes, we designed the anti-jamming improvement factor and the correlation matrix of jamming and ECCM. They were used to evaluate the jamming suppression performance of ECCMs and to provide feedback for the decision-making algorithm. The decision-making constraints and four different decision-making objectives were designed to verify the performance of the decision-making algorithm. Finally, we designed a DDPG-MADDPG algorithm to generate the anti-jamming strategy. The simulation results showed that the proposed method has excellent robustness and generalization performance. At the same time, it has a shorter convergence time and higher anti-jamming decision making accuracy.

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“…To restore normal communication quickly and stably when the wireless communication system is faced with jamming, researchers use the method based on automatic control [10,11] to prevent jamming. To address the issue of local data secrecy during multi-agent anti-jamming communication, researchers employ federated learning (FL) [12][13][14] for anti-jamming purposes. To enable agents to learn how to learn during anti-jamming communication, researchers have utilized meta-learning [15][16][17] for anti-jamming.…”

Section: Introductionmentioning

confidence: 99%

“…Building upon this foundation, scholars worldwide have proposed improved methods such as multi-agent layered Qlearning (MALQL) and UCB-DQN [23]. Historical experience is underutilized [11] IoT Automated Measurement [12] 5G FDRL FL Performance is sacrificed for security [13] Secrecy-driven FL FL [14] FANET AFRL [15] Beamforming Meta Learning Meta-L High computing power requirements [16] Image Classification MGML [17] Jamming Recognition Meta Learning [18] Wireless sensor networks JMAA RL Curse of dimensionality; Unable to cope with continuous-state Spaces [19] UAV Anti-Jamming Communication CMRL [20] Deceiving-based anti-jamming methods Reinforcement Learning [21] Frequency Selection of HF The anti-jamming method based on automatic control can quickly and effectively deal with the current jamming and make appropriate anti-jamming actions, but this method cannot accumulate historical experience and cannot effectively predict the state change before anti-jamming. The intelligent anti-jamming communication methods are based on FL sacrifice performance for the sake of data security, and the performance is limited by the communication efficiency between agents.…”

Section: Introductionmentioning

confidence: 99%

Anti-Jamming Communication Using Imitation Learning

Zhou,

Niu,

Wan

et al. 2023

Entropy

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The communication reliability of wireless communication systems is threatened by malicious jammers. Aiming at the problem of reliable communication under malicious jamming, a large number of schemes have been proposed to mitigate the effects of malicious jamming by avoiding the blocking interference of jammers. However, the existing anti-jamming schemes, such as fixed strategy, Reinforcement learning (RL), and deep Q network (DQN) have limited use of historical data, and most of them only pay attention to the current state changes and cannot gain experience from historical samples. In view of this, this manuscript proposes anti-jamming communication using imitation learning. Specifically, this manuscript addresses the problem of anti-jamming decisions for wireless communication in scenarios with malicious jamming and proposes an algorithm that consists of three steps: First, the heuristic-based Expert Trajectory Generation Algorithm is proposed as the expert strategy, which enables us to obtain the expert trajectory from historical samples. The trajectory mentioned in this algorithm represents the sequence of actions undertaken by the expert in various situations. Then obtaining a user strategy by imitating the expert strategy using an imitation learning neural network. Finally, adopting a functional user strategy for efficient and sequential anti-jamming decisions. Simulation results indicate that the proposed method outperforms the RL-based anti-jamming method and DQN-based anti-jamming method regarding solving continuous-state spectrum anti-jamming problems without causing “curse of dimensionality” and providing greater robustness against channel fading and noise as well as when the jamming pattern changes.

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Mitigating Jamming Attack in 5G Heterogeneous Networks: A Federated Deep Reinforcement Learning Approach

Cited by 28 publications

References 48 publications

Practical Trial for Low-Energy Effective Jamming on Private Networks With 5G-NR and NB-IoT Radio Interfaces

Practical Trial for Low-Energy Effective Jamming on Private Networks With 5G-NR and NB-IoT Radio Interfaces

Radar Anti-Jamming Decision-Making Method Based on DDPG-MADDPG Algorithm

Anti-Jamming Communication Using Imitation Learning

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