In this paper, we investigate the problem of scene modeling and distributed channel selection optimization with non-orthogonal channels. Aiming at scene modeling, we propose a probabilistic line-of-sight (LoS) communication model, which overcomes the limitation of fixed communication distance in current research on non-orthogonal channels through taking into account the attenuation of the communication distance due to path loss. To deal with the optimization problem of channel selection, an improved Max-logit learning algorithm is proposed to optimize the distributed channel selection strategy, which reduces system interference and improves network throughput through a heterogeneous learning rate and appropriate the unaltered action probability. Experimental results validate that the proposed algorithm provides a significant reduction in cumulative interference, as well as improves spectrum utilization and network throughput. In addition, it can adapt to communication requirements more efficiently than traditional centralized and other distributed access algorithms, thus ensuring stable system performance in dense user environments.

In this paper, we study the problem of network reconfiguration when unmanned aerial vehicle (UAV) swarms suffer damage. Multiple UAVs are divided into several groups to perform various tasks. Each master UAV is connected to the ground control station and provides network services for small UAVs that perform various tasks, ensuring that the information of small UAVs can be transmitted back in a timely manner. When master UAVs are destroyed due to factors such as jamming or attacks, the associated small UAVs must select new master UAVs for network service and cooperate with other small UAVs to execute tasks. Based on the heterogeneity and relevance of tasks, we model and analyze the task relationship among different UAVs. Since both master UAVs and small UAVs have respective optimization objectives in the network reconfiguration process, we construct a many-to-one bilateral matching market to model the interaction between master UAVs and small UAVs. To realize an efficient solution for UAV network reconfiguration in complex environments, we propose a distributed matching algorithm and prove that the algorithm can converge to two-sided stable matching. Simulation results indicate that the proposed algorithm can significantly improve the task completion degree of the network compared with three other algorithms.

This paper investigates the anti-jamming problems for multi-user scenarios. On the one hand, users in the networks should coordinate their channel selection strategies to avoid spectrum conflicts among different users. On the other hand, because of the openness characteristic of wireless communications, malicious jammers can disrupt the legitimate communications of legitimate users by sending jamming signals, thus users also need to fully consider how to defend against malicious jamming attacks. To cope with the internal coordination and external confrontation problem, a context-aware dynamic spectrum coordinated anti-jamming approach is proposed. In detail, the multiuser anti-jamming scenario is modeled as an anti-jamming local altruistic game, and the existence of Nash Equilibrium (NE) is demonstrated. Besides, the proposed game model is proved to be an exact potential game. To obtain NEs, a multi-pattern stochastic learning algorithm (MSLA) is designed. Through local information exchange and distributed learning, users can achieve global optimization under the dynamic jamming environment.

This paper investigates the anti-jamming problems in wireless communication networks. In these networks consisted of multiple devices (users), there exist two critical problems. On the one hand, users with various transmission requirements should coordinate their channel selection strategies distributedly to avoid spectrum conflicts and satisfy transmission demands. On the other hand, they also need to fully consider how to eliminate the effects of malicious attacks. To cope with the internal coordination and external confrontation challenges and accommodate the dynamic changing jamming attacks, a context-aware distributed coordinated anti-jamming channel access mechanism is proposed, which means for different cases of jamming attacks, different access strategies are adopted. In detail, to reflect the heterogeneous communication demands of users, the transmission satisfaction function is firstly introduced. Then, the multi-user anti-jamming scenario is modeled as a context-aware multi-pattern dynamic anti-jamming game, which can be decomposed into two sub-games. Here, for the case that the control channel is available, a local altruistic sub-game is introduced. While for the case that the control channel has been jammed, an anti-jamming congestion sub-game is designed. Besides, the existence of Nash Equilibriums is demonstrated. To obtain NEs, a context-aware distributed channel access (CDCA) algorithm is designed. Through game-theoretic analysis and distributed learning, global transmission satisfaction can be improved under the dynamic jamming environment. Furthermore, the fairness of the network can also be guaranteed.

In this article, we study the unmanned aerial vehicle (UAV)-enabled opportunistic data collection in wireless sensor networks (WSNs). The UAV performing remote missions is expected to collect data from the WSN during the return flights. Due to the specified task and safety restrictions, flight trajectory and time of the UAV are strictly constrained, resulting in the limited coverage ability in the data collection process. Moreover, the unknown distribution of active sensors makes it difficult for ground sensors and the UAV to complete the offline optimization of flight mode and transmission. To tackle these problems, we develop an air-ground collaborative online planning method. On the one hand, ground sensors actively form terrestrial transmission clusters to improve the data upload efficiency. After analyzing the Line-of-Sight (LoS) reliability and transmission correlation, we construct a coalition formation game model for the clustering of ground sensors. We discuss the equilibrium property of the game model, which can be achieved by the proposed distributed coalition formation algorithm. On the other hand, to avoid conflicts during the data collection, a data upload protocol is designed. We further discuss various flight speed planning schemes based on different detection capabilities of the UAV. The simulation results show that the performance of ground coalition-based air-ground collaborative online optimization is much better than that of the unilateral data collection by the UAV. Moreover, UAV flight online planning can further improve data uploading efficiency.

In this paper, we investigate the problem of distributed ferrying transmission in UAV-assisted device-to-device (D2D) communication networks. When drones are performing tasks with given trajectories, terrestrial communication devices can select them for loading data opportunistically, and then drones will offload the data to corresponding receivers in the appropriate later time. For the dynamic multi-device network, there are composite optimization problems including competition of drone selection, time allocation of data loading and offloading, as well as limited channel access. Due to the distributed feature, devices share resources through independent perception and decision making. Therefore, a dynamic hierarchical game is designed for the problem of joint UAV allocation and channel access. Specifically, a predictable dynamic matching market is constructed to address the problem of UAV selection and time allocation, while the problem of channel access is studied by the congestion game. Based on the game model, a distributed hierarchical algorithm is proposed and the property of convergence is discussed. Simulation results confirm that the effective selection of data ferrying approach can improve the transmission performance significantly, while unreasonable optimization approaches may lead to the decline of the transmission performance.

In this study, the authors study the self‐organising user assignment problem for the multi‐user cooperation network. In the cellular network with user cooperation, idle users near the base station are seen as potential relay nodes, which can assist cell edge users to transmit data. Practically, the selfish nature of users is considered in the authors’ model. They design a score‐based system, where the strategies of relay assignment are affected by the historical behaviours of users. That means not only the transmission performance, but also the accumulative contributions of users are considered. The proposed score‐based system sufficiently encourages idle users to assist active users. Furthermore, the multi‐user assignment problem is formulated as a one‐to‐one matching game, in which idle users and active users rank one another individually based on their own preference. To address the issue, they propose a self‐organising mutually beneficial matching algorithm, which is proven to converge to a stable matching. Simulation results show that the proposed distributed algorithm yields well matching performance between source users and relay users, which is close to the optimal centralised results.