As a key technology of 6G, integrated sensing and communication (ISAC) has attracted much attention, and its security issues are becoming increasingly severe. In this letter, we enhance the physical layer security (PLS) of ISAC systems using rate-splitting multiple access (RSMA). To ensure specific security requirements, we formulate a joint optimization problem that involves the precoding matrix and the common rate. The goal is to maximize the communication-sensing weighted secure sum rate (SSR), thereby enhancing the overall system performance while maintaining security. To solve the non-convex optimization problem, we adopt the staged algorithm named successive convex approximation with penalty (SCA-P) algorithm. Firstly, the original non-convex problem is transformed into a series of more tractable convex problems. Then, penalty terms are introduced to deal with complex constraints. Finally, we gradually approach the optimal solution of the original problem by iteratively solving these convex problems. Simulation results show that the proposed scheme has better security performance than the state-of-the-art schemes.

This paper considers a downlink resource-allocation problem in distributed interference orthogonal frequency-division multiple access (OFDMA) systems under maximal power constraints. As the upcoming fifth-generation (5G) wireless networks are increasingly complex and heterogeneous, it is challenging for resource allocation tasks to optimize the system performance metrics and guarantee user service requests simultaneously. Because of the non-convex optimization problems, using existing approaches to find the optimal resource allocation is computationally expensive. Recently, model-free reinforcement learning (RL) techniques have become alternative approaches in wireless networks to solve non-convex and NP-hard optimization problems. In this paper, we study a deep Q-learning (DQL)-based approach to address the optimization of transmit power control for users in multi-cell interference networks. In particular, we have applied a DQL algorithm for resource allocation to maximize the overall system throughput subject to the maximum power and SINR constraints in a flat frequency channel. We first formulate the optimization problem as a non-cooperative game model, where the multiple BSs compete for spectral efficiencies by improving their achievable utility functions while ensuring the quality of service (QoS) requirements to the corresponding receivers. Then, we develop a DRL-based resource allocation model to maximize the system throughput while satisfying the power and spectral efficiency requirements. In this setting, we define the state-action spaces and the reward function to explore the possible actions and learning outcomes. The numerical simulations demonstrate that the proposed DQL-based scheme outperforms the traditional model-based solution.

Due to the vulnerability of wireless network control systems (WNCSs), data loss on unreliable channels severely degrades the remote state estimation performance in autonomous driving and connected vehicles. While redundant channels enhance successful transmission probability, they may lead to excessive consumption of limited vehicular network resources. To address this trade-off, this paper investigates optimal scheduling of redundant transmission in remote state estimation over dynamic vehicular links, modeled as Gilbert-Elliott channels. To minimize the long-term average estimation error in the remote estimation process, the redundant transmission scheduling problem is formulated as a Markov decision process (MDP), and deep reinforcement learning (DRL) is employed to solve the infinite-horizon reward optimization problem. To verify the stability of the established MDP model, a sufficient condition guaranteeing the boundedness of the average estimation error is derived. Furthermore, the existence of the optimal deterministic and stationary policy is established based on Bellman's optimality equation. Furthermore, considering the limitations of conventional reinforcement learning methods in processing high-dimensional vehicular communication data, the dueling double deep Q-network (D3QN) algorithm is developed to mitigate learning instability and value overestimation, enabling rapid convergence to the approximate optimal scheduling policy. In conclusion, the validity of the proposed method is corroborated through comprehensive comparisons with the periodic policy, greedy holding times policy, and the deep Q-network algorithm.

The proton exchange membrane fuel cell (PEMFC) is a promising power source, but the short lifespan and high maintenance cost restrict its development and widespread application. Performance degradation prediction is an effective technique to extend the lifespan and reduce the maintenance cost of PEMFC. This paper proposed a novel hybrid method for the performance degradation prediction of PEMFC. Firstly, considering the randomness of PEMFC degradation, a Wiener process model is established to describe the degradation of the aging factor. Secondly, the unscented Kalman filter algorithm is used to estimate the degradation state of the aging factor from monitoring voltage. Then, in order to predict the degradation state of PEMFC, the transformer structure is used to capture the data characteristics and fluctuations of the aging factor. To quantify the uncertainty of the predicted results, we also add the Monte Carlo dropout technology to the transformer to obtain the confidence interval of the predicted result. Finally, the effectiveness and superiority of the proposed method are verified on the experimental datasets.

This paper addressed the optimal policy selection problem of attacker and sensor in cyber-physical systems (CPSs) under denial of service (DoS) attacks. Since the sensor and the attacker have opposite goals, a two-player zero-sum game is introduced to describe the game between the sensor and the attacker, and the Nash equilibrium strategies are studied to obtain the optimal actions. In order to effectively evaluate and quantify the gains, a reinforcement learning algorithm is proposed to dynamically adjust the corresponding strategies. Furthermore, security state estimation is introduced to evaluate the impact of offensive and defensive strategies on CPSs. In the algorithm, the $\epsilon$-greedy policy is improved to make optimal choices based on sufficient learning, achieving a balance of exploration and exploitation. It is worth noting that the channel reliability factor is considered in order to study CPSs with multiple reasons for packet loss. The reinforcement learning algorithm is designed in two scenarios: reliable channel (that is, the reason for packet loss is only DoS attacks) and unreliable channel (the reason for packet loss is not entirely from DoS attacks). The simulation results of the two scenarios show that the proposed reinforcement learning algorithm can quickly converge to the Nash equilibrium policies of both sides, proving the availability and effectiveness of the algorithm.