Directional antennas, with their ability to enhance signal strength and mitigate interference, are integral to modern wireless communication, especially in ground-air-space networks. However, the precise alignment requirement of directional antennas in communication pose challenges for efficient neighbor discovery (ND). Aiming at optimizing discovery time by dynamically adjusting sector selection probabilities, a novel Multi-Armed Bandit (MAB)-based algorithm is proposed in this paper. By analyzing the feedback that can accelerate the ND time and the theory of MAB, an important conclusion is drawn that MAB is only applicable to non-uniform distributions where there are significant differences in neighbor distributions. Based on analysis, an appropriate MAB-based ND algorithm is designed. Numerical simulations have demonstrated the correctness of the analysis and the performance advantages of the proposed algorithm both in uniform and non-uniform topologies.

Abstract In unmanned aerial vehicle ad‐hoc network (UANET), the node speed of unmanned aerial vehicles (UAVs) may reach up to 400 km/h. The fast or slow movement of UAV nodes leads to different speeds of topology change of the nodes. Traditional optimized link state routing (OLSR) protocol cannot adaptively adjust the routing update period when the network topology changes, which may lead to the nodes calculating incorrect routing tables. This increases the average end‐to‐end delay and packet loss rate for packet transmission. To enhance the adaptability of OLSR routing protocol to network topology changes, this paper proposes a multi‐agent independent deep deterministic policy gradient‐OLSR (MA‐IDDPG‐OLSR) routing protocol based on distributed multi‐agent reinforcement learning. The protocol deploys DDPG algorithm on each UAV node, and each UAV node adaptively adjusts the Hello and TC message sending intervals, according to the one‐hop neighbouring nodes as well as its own state. Simulation results show that the proposed protocol is able to improve the throughput and reduce the packet loss rate as compared to traditional AODV, GRP, OLSR, and distributed multiple‐agent independent proximal policy optimization‐OLSR (MA‐IPPO‐OLSR), distributed multiple‐agent independent twin delayed deep deterministic policy gradient‐OLSR (MA‐ITD3‐OLSR) routing protocols. Since MA‐IDDPG‐OLSR relies only on local information, there is a minor performance degradation in MA‐IDDPG‐OLSR compared to centralized single‐agent DQN‐OLSR routing protocol. But it is more suitable to a completely distributed UAV network without a centralized node.

This paper addresses the distributed task allocation problem for maximizing the total number of successfully executed tasks of multirobot systems. Due to the deadline time of tasks and fuel limits of robotic vehicles, not all tasks can be successfully executed sometimes. Based on the performance impact (PI) algorithm, an effective and efficient performance impact (EEPI) algorithm is proposed, its novelty lies in its cost function and task release procedure. The fundamental ideas of the proposed cost function are as follows. First, the traveling time from the initial position of each vehicle to the positions of its tasks is minimized, so that more time can be left for the vehicle to execute more tasks due to the limited fuel. Second, the start time of each task should be close enough to its deadline, so that tasks with earlier deadlines can be assigned earlier than those with later deadlines. To avoid invalid removal performance impacts (RPIs) and inclusion performance impacts (IPIs), the tasks assigned to a vehicle are all released if the number of tasks removed by the vehicle during the task removal phase is the most, which further increases the total number of successfully executed tasks. Both simulations and hardware-in-the-loop experiments suggest that compared with the state-of-the-art distributed task allocation algorithms, the proposed EEPI is not only effective in maximizing the number of successfully executed tasks but efficient in saving the number of iterations and time to converge.Note to Practitioners—This work was motivated by the limitations of the existing distributed task allocation algorithms for maximizing the total number of successfully executed tasks. The consensus-based bundle algorithm (CBBA) has been proven to guarantee convergence and 50% optimality under the diminishing marginal gain (DMG) assumption in previously published works. Based on CBBA, a performance impact (PI) algorithm was proposed, and simulations show that it can assign more tasks than CBBA when applied to time-critical scenarios with low task-to-vehicle ratios. Starting from the results of PI, a rescheduling method named PI for maximizing assignments (PI-maxAss) was proposed, which has been demonstrated to assign more tasks than PI with high task-to-vehicle ratios. However, much more iterations and time are required by PI-maxAss to converge to globally consistent assignments because of the rescheduling. Due to the above considerations, an effective and efficient performance impact (EEPI) algorithm is proposed in this paper to maximize the number of successfully executed tasks without any rescheduling. Both simulations and hardware-in-the-loop experiments suggest that compared with the algorithms mentioned above, the proposed EEPI is effective in maximizing the number of successfully executed tasks and efficient in saving the number of iterations and time to converge. In future work, the distributed task allocation problem in which several vehicles execute a task at the same time cooperatively or a vehicle executes several tasks simultaneously will be further addressed.

Abstract In this article, robust beamforming (BF) and artificial noise (AN) design method is proposed for multiuser broadcast secure transmission under secrecy outage probability (SOP) constraints. A special scenario is assumed that the transmitter cannot obtain the accurate channel state information (CSI) of the users' channel, and different users have different priorities and importance. This special scenario makes traditional BF algorithms and AN design methods unable to guarantee the effective and secure transmission of information. To deal with intractable non‐convex problems in this new design, a series of optimization methods like Bernstein‐type inequality (BTI) and S‐Procedure are employed to transform these problems into solvable functions. Moreover, a search algorithm is explored for simplifying the computational complexity of the optimization algorithm. After that, the new robust algorithm is compared with former methods in inaccurate CSI scenarios by numerical simulation, which shows the advantages of our algorithm.

Abstract In unmanned aerial vehicle ad‐hoc network (UANET), the network topology changes with time due to the movement of the unmanned aerial vehicles (UAVs), which brings great challenges to the design of the routing protocol. In traditional routing protocols, when UANET topology changes, nodes cannot dynamically update neighbour nodes and topology information, and routing table calculation cannot accurately reflect the actual transmission path. As a result, network cannot meet the quality of service (QoS) requirements such as low end‐to‐end delay, high throughput and low packet loss rate. This paper proposes a dynamically optimized link state routing (OLSR) protocol based on Deep Q‐Network algorithm (DQN‐OLSR). In this protocol, each node first adjusts the sending interval of Hello messages adaptively in real time, according to the position and speed information of its neighbour nodes. Then the protocol uses the DQN algorithm to dynamically adjust the flooding interval of topology control (TC) messages to improve the routing update capability of nodes. The simulation verifies that the UANETs under this protocol have higher throughput and less packet loss rate than ad‐hoc on‐demand distance vector (AODV), grid routing protocol (GRP) and OLSR protocols, at different movement speeds in random waypoint (RWP) and random walk mobile models. Under nomadic as well as pursue mobile models, DQN‐OLSR performs consistently with OLSR QoS performance, with the best performance among all four protocols. By further adding positioning errors to the nodes, it shows that the proposed protocol has good robustness, and the QoS performance degradation keeps within a low level.

In this study, the authors proposed the optimal beamforming (BF) design for multiuser broadcast secure transmission under secrecy outage probability constraint with artificial noise technique. Different from former research papers, their model assumes that different users have different priority and importance. Some users are regarded as main users with a stricter constraint, which make the normal BF algorithm not to gain the best performance. For this reason, they proposed a new BF design method for this specific system model. To deal with intractable non‐convex problems, they employed a series of optimisation algorithm like Bernstein‐type inequality and second‐order cone constraints method to transform these problems into solvable functions. After that, they show the feasibility of their new BF algorithm by Monte‐Carlo simulation method, and analyse the influence on transmission and security performance caused by different system parameters. Furthermore, they compare the security performance of their method with the normal BF method, which demonstrates the advantage of the new BF method.