Partial-band noise jamming is an important countermeasure against frequency-hopping spread spectrum technology in wireless sensor networks, and the jamming resource allocation (JRA) problem involved is a high-dimensional combinatorial optimization and also an NP-hard problem. Moreover, the users can dynamically alter their communication status, such as changing the channel spectrum distributions of hopping sets, posing additional difficulties for JRA. This paper develops two methods to address above challenges. First, a deep reinforcement learning (DRL)-based method is proposed for efficient JRA optimization, with the jamming scheme of each jamming node decided sequentially by the policy neural network, and its parameters are updated through trust region policy optimization (TRPO) within a trust region to ensure stable and fast convergence. Second, we propose a meta-TRPO based method to improve the generalization capability of the policy network. After the meta-training process, it can update the meta policy network with just a few fine-tuning steps and quickly obtain a task-specific policy for the fresh task. Extensive simulation results show the proposed DRL-based method converges faster than other DRL methods. Additionally, the proposed meta-TRPO based method can rapidly adapt to unseen jamming tasks with only a small quantity of training trajectories.

Due to the nonconvexity feature of optimal controlling such as jamming link selection and jamming power allocation issues, obtaining the optimal resource allocation strategy in communication countermeasures scenarios is challenging. Thus, we propose a novel decentralized jamming resource allocation algorithm based on multiagent deep reinforcement learning (MADRL) to improve the efficiency of jamming resource allocation in battlefield communication countermeasures. We first model the communication jamming resource allocation problem as a fully cooperative multiagent task, considering the cooperative interrelationship of jamming equipment (JE). Then, to alleviate the nonstationarity feature and high decision dimensions in the multiagent system, we introduce a centralized training with decentralized execution framework (CTDE), which means all JEs are trained with global information and rely on their local observations only while making decisions. Each JE obtains a decentralized policy after the training process. Subsequently, we develop the multiagent soft actor-critic (MASAC) algorithm to enhance the exploration capability of agents and accelerate the learning of cooperative policies among agents by leveraging the maximum policy entropy criterion. Finally, the simulation results are presented to demonstrate that the proposed MASAC algorithm outperforms the existing centralized allocation benchmark algorithms.

Jamming against frequency-hopping spread spectrum (FHSS) in wireless sensor networks (WSNs) has been primarily investigated with the follower jamming mode. However, implementing follower jamming in practical applications encounters manifold challenges, such as stringent requirements on hardware performance, difficulties in attaining accurate synchronization with signals. Diverging from existing works, in this paper, we propose a novel partial-band noise jamming (PBNJ) decision-making algorithm based on asynchronous deep reinforcement learning, which can allocate central jamming frequency and bandwidth more efficiently in FHSS jamming. Firstly, we model the problem of allocating jamming resource of PBNJ to disrupt the FHSS communication in WSNs as a Markov decision process (MDP). Next, considering the interrelationship among decisions made by different jamming nodes (JNs), we construct a multi-step decision framework in a time-division manner, and the Long Short-Term Memory (LSTM) network is leveraged to fully extract decision features from historical data, capturing correlations between jamming strategies of the deployed JNs, and guides future jamming decisions and enhances collaboration among different JNs in jamming resources allocation. Furthermore, to accelerate the convergence, we adopt the asynchronous advantage actor-critic (A3C) algorithm to optimize the allocation of central jamming frequency and bandwidth of JNs, utilizing the architecture of multi-threaded parallel training, and update the actor network and critic network in an asynchronous gradient descent manner. Simulation results show that the proposed LSTM-A3C algorithm converges fast and outperforms various baselines in terms of the convergence speed, jamming success rate and the total reward.

This letter studies the problem of dynamic jamming power allocation (JPA) with incomplete sensing information in dynamic and unknown environments. Most existing studies assume that jammers have perfect sensing information, without the consideration of the reliability of sensors, leading to poor performance when facing incomplete sensing information. In response, we propose a robust intelligent JPA scheme and adopt a two-stage algorithm namely “offline training and online deploy" to guide the training and deployment process. The approach utilizes the observed values from spatially distributed sensor units as inputs and employs deep reinforcement learning (DRL) for jamming power decision-making. To handle the missing observation data, we designed a data completion module based on the generative adversarial networks (GAN) framework. In addition, we introduce the priority experience replay mechanism (PERM) and opponent modeling (OM) in the decision model to enhance the learning efficiency and decision accuracy of the network. Simulation results show that the proposed approach can achieve efficient jamming with incomplete information, and outperform conventional DRL-based approaches.

Reinforcement learning (RL)’s powerful optimization capabilities have been extensively applied in the field of wireless communication jamming decision-making. However, the generalization of jamming policies has rarely been explored, and most existing studies rely on task-customized reward functions, which are often intractable to design. To address these issues, we propose a meta RL method for frequency-hopping spread spectrum (FHSS) jamming decision-making, aided by inexpert demonstrations. Firstly, the policy network is meta-trained with multiple diverse tasks to obtain initial network parameters with good generalization. Subsequently, we combine RL and behavioral cloning (BC) to extract useful information from demonstrations, along with learning rate adaptation to achieve efficient policy exploration without the task-customized jamming reward. Simulations confirm that our proposed method not only adapts to unseen jamming tasks with just a few fine-tuning steps under general binary rewards condition, but also achieves higher accumulated jamming rewards and results in lower normalized throughput for users, outperforming state-of-the-art methods.