In this paper, we study the distributed relay assignment problem in multi-channel multi-radio unmanned aerial vehicle (UAV) communication networks. Multi-UAVs are driven by the overall task and fly in certain formation, where drones with different tasks have various transmission requirements. Source UAVs equipped with multi-radio can select more than one relay radios to achieve high data rate, and each relay radio can be shared by multiple source UAVs. We construct distributed game models to promote the global transmission performance by self-organizing coordination among UAVs. Specifically, the channel competition relationship between relay UAVs is modeled as a congestion game model, while the task-driven relay selection among UAVs is modeled as a many-to-many matching market without substitutability. With the proposed game models, the optimizing of local optimized process will lead to the improvement of global transmission results. After that, we design algorithms for the stable and changeable topology structures, respectively. Based on the given formation shape of UAVs, a learning matching algorithm is proposed to reach the optimum result with a large probability. A fast potential matching algorithm is propose to deal with the topological change of UAV networks. We prove that two proposed algorithms can both achieve the stable matching results. Simulation results show that the proposed relay assignment approaches yield good performances in terms of the global transmission satisfaction and fairness. Particularly, the result of the learning algorithm is close to the global optimum and the fast potential matching approach is robust to the perturbation of UAV networks.

In this paper, we investigate the problem of opportunistic UAV transmission in D2D communication networks. UAVs are supposed to help transmissions of D2D users when they are employed to perform flying missions with given trajectories. On one hand, users can select appropriate UAVs as real-time relays according to the topology in the sky at different moments. On the other hand, due to flight characteristics, UAVs can receive the uploading data when they are approaching transmitters, and then offload the data to corresponding receivers in the appropriate later time. Users need to select and adjust transmission modes dynamically, including multi-UAV selection, time allocation of data loading and offloading, as well as the competition of channel access. We design a hierarchical game model to analyze the complicated relationship among devices. Specifically, a predictable dynamic matching market is constructed to address the issue of UAV selection and time allocation, while the problem of channel access is studied by the congestion game. After that, distributed algorithms are proposed and the properties of convergence are discussed. Simulation results confirm that the effective opportunistic UAV transmission approach can improve the global network significantly, while unreasonable optimization approaches may lead to the decline of the transmission performance.

In this paper, we study joint relay selection and the power control optimization problem in an anti-jamming relay communication system. Considering the hierarchical competitive relationship between a user and jammer, we formulate the anti-jamming problem as a Stackelberg game. From the perspective of game, the user selects relay and power strategy firstly which acts as the leader, while the jammer chooses power strategy then that acts as follower. Moreover, we prove the existence of Stackelberg equilibrium. Based on the Q-learning algorithm and multi-armed bandit method, a hierarchical joint optimization algorithm is proposed. Simulation results show the user’s strategy selection probability and the jammer’s regret. We compare the user’s and jammer’s utility under the proposed algorithm with a random selection algorithm to verify the algorithm’s superiority. Moreover, the influence of feedback error and eavesdropping error on utility is analyzed.

As a potential alternative for low-altitude surveillance, the LTE-based passive radar (LTEPR) has received worldwide attention due to the wide availability and large bandwidth of LTE. The MIMO and OFDMA techniques of LTE mean that the radio resource is randomly allocated and multiple signals are transmitted via multiple antennas. The received reference signal of LTEPR is the mixture of multiple signals and discontinous over time. Target SNR loss easily happens if this reference signal is used in matched filtering. However, open publications rarely consider to fractionate reference signal, that is, separating and refining multiple transmitting signals meanwhile dealing with MIMO and OFDMA. This paper proposes a complete procedure of reference signal fractionation for LTEPR, in which a heuristic channel estimator is proposed to solve estimation error caused by virtual subcarriers and a supervised strategy is proposed to identify the active radio resource from noise. The MIMO technique is decoded after channel equalization. The numerical simulations prove that the heuristic channel estimator is unbiased and the supervised strategy largely reduces the false alarm and missed alarm caused by OFDMA. The real data tests prove that the proposed procedure is feasible and practical to recover multiple transmitting signals.

Random access is one of the most competitive multiple access schemes for future space-based Internet of Things (S-IoT) due to its support for massive connections and grant-free transmission, as well as its ease of implementation. However, firstly, existing random access schemes are highly sensitive to load: once the load exceeds a certain critical value, the throughput will drop sharply due to the increased probability of data collision. Moreover, due to variable satellite coverage and bursty traffic, the network load of S-IoT changes dynamically; therefore, when existing random access schemes are applied directly to the S-IoT environment, the actual throughput is far below the theoretical maximum. Accordingly, this paper proposes an intelligent load control-based random access scheme based on CRDSA++, which is an enhanced version of the contention resolution diversity slotted ALOHA (CRDSA) and extends the CRDSA concept to more than two replicas. The proposed scheme is dubbed load control-based three-replica contention resolution diversity slotted ALOHA (LC-CRDSA3). LC-CRDSA3 actively controls network load. When the load threatens to exceed the critical value, only certain nodes are allowed to send data, and the load is controlled to be near the critical value, thereby effectively improving the throughput. In order to accurately carry out load control, we innovatively propose a maximum likelihood estimation (MLE)-based load estimation algorithm, which estimates the load value of each received frame by making full use of the number of time slots in different states. On this basis, LC-CRDSA3 adopts computational intelligence-based time series forecasting technology to predict the load values of future frames using the historical load values. We evaluated the performance of LC-CRDSA3 through a series of simulation experiments and compared it with CRDSA++. Our experimental results demonstrate that in S-IoT contexts where the load changes dynamically, LC-CRDSA3 can obtain network throughput that is close to the theoretical maximum across a wide load range through accurate load control.