With the proliferation of various mobile smart devices, edge computing and computational offloading technologies have emerged as pivotal support mechanisms, enhancing the service quality of these devices. To facilitate the learning of task offloading strategies across diverse and complex scenarios, this study introduces a federated learning strategy algorithm based on multi-agent deep reinforcement learning. This algorithm aims to aggregate the training strategies of multiple edge computing devices, thereby enabling the synthesis of superior task offloading strategies tailored to a wide array of environments while also providing safeguards against malicious nodes. This paper specifically examines the reward inversion attack, illustrating the algorithm’s capability to identify and counteract such malicious threats effectively. Experimental results validate that the proposed algorithm not only robustly defends against these attacks but also adeptly learns the task offloading strategies pertinent to each node.
Due to the open characteristics of the electromagnetic channel, there are malicious nodes that jam the normal data flow, prevent the legitimate receiver from obtaining information, and then intercept and tamper with the data, which makes the research of communication anti-jamming becomes more and more important. Traditional anti-jamming methods employ a single anti-jamming approach and cannot adaptively adjust their anti-jamming strategy based on the environment, often can’t achieve a good anti-jamming effect in the complex communication environment. In order to solve these challenges, this paper studies the anti-jamming communication model based on deep reinforcement learning (DRL), and builds a simulation system to realize intelligent anti-jamming decision-making by using DRL algorithm. The simulation results show that the proposed intelligent anti-jamming decision can choose the best anti-jamming scheme according to the complex environment and effectively improve the communication quality.
HF diversity communication network adopts multiple frequencies to support user communication to improve the reliability of receiving.In the case of multi-user concurrent access,genetic algorithm can be used to plan the frequency of users,so as to save frequency resources under the condition of meeting the needs of given users.However,the planning process takes a long time and the optimization effect is not good.To address these problems,a new improved genetic algorithm is proposed to plan frequency resources of HF diversity communication network.The simulation results indicate that the improved algorithm has shorter planning time and better optimization effect than the original genetic algorithm,which provides a basis for the practical application of HF diversity communication network.
Due to its high flexibility and other advantages,aerial drone base stations can expand the communication support range for ground users and solve the mobile coverage problem of ground mobile users in battlefield environments.Considering the impact of high-level terrain features on radio signals in the battlefield environment,the differentiated communication needs of users,and the energy consumption issues of UAV(Unmanned Aerial Vehicle)base stations,a reinforcement learning communication coverage algorithm based on heterogeneous mobile users(ABS-RL)is proposed to model the channel capacity of ground users,the energy consumption of UAV,and the dynamic deployment of UAV base stations,aiming to provide high-quality communication services for ground users.The simulation results demonstrate that the algorithm offers significant advantages in enhancing the channel capacity for ground users and reducing the total energy consumption of UAV base stations.
In order to achieve the long-term work of the wireless sensor network in the border area,a network combining UAV and NOMA(Non-Orthogonal Multiple Access)technology is used to collect data from sensors distributed along the narrow border,so as to effectively reduce the energy consumption of WSNs(Wireless Sensor Networks).The sensor user group uploads data to UAV in a multi-carrier NOMA mode to minimize the total energy consumption of all sensors by optimizing sensor user scheduling and grouping,transmit power and UAV trajectory under the constraints of maximum transmit power,user QoS(Quality of Service)and UAV mobility.The optimization problem is a mixed integer nonlinear optimization problem,which is decomposed into three subproblems to solve.By using exhaustive search,Lagrange duality decomposition and SCA(Successive Convex Approximation),the subproblems are solved successively,and the minimum energy consumption is achieved.Simulation results indicate that the proposed scheme can effectively reduce the total energy consumption of the WSNs.
