Cooperative communication is a key technology to improve the stability of UAV swarm communication. To reduce the energy consumption in cooperative communication, this study proposes a method for selecting relay nodes based on distance and energy constraints. First, a two-stage communication model was designed to explain the signal forwarding communication process. Subsequently, cluster head nodes were filtered through a competition mechanism to be used for forwarding source signals. The minimum communication capacity of the link contained the distance parameter of the relay link. Lastly, under energy and distance constraints, the optimal solution of the minimum interrupt function for the cluster head was found, and was defined as the best relay node. The experimental results show that the two parameters of network coverage and residual energy of the improved method were higher than those of other mainstream algorithms. Furthermore, the actual UAV networking test results show that the proposed relay selection method had the most residual energy. It is concluded that the relay selection method proposed in this study has good practicality and advancement in UAV cluster communication.

Device-to-device(D2D) communication combined with non-orthogonal multiple access(NOMA) can further improve the quality of service for future communications systems and increase spectrum utilization and connection density in cellular networks. In this paper, an effective power allocation scheme is designed to optimize the performance of a D2D communication system based on NOMA for imperfect Successive Interference Cancellation(SIC) decoding. To solve the non-convexity of the problem due to integer constraints and coupling variables, an alternate optimization algorithm was designed to obtain the optimal solution of each subproblem by Lagrange duality analysis and the sub-gradient descent method. Finally, the numerical simulation results demonstrate the performance advantages of the proposed joint optimization algorithm for channel resource allocation and power control in energy efficiency.

A distributed GM-CPHD filter based on parallel inverse covariance crossover is designed to attenuate the local filtering and uncertain time-varying noise affecting the accuracy of sensor signals. First, the GM-CPHD filter is identified as the module for subsystem filtering and estimation due to its high stability under Gaussian distribution. Second, the signals of each subsystem are fused by invoking the inverse covariance cross-fusion algorithm, and the convex optimization problem with high-dimensional weight coefficients is solved. At the same time, the algorithm reduces the burden of data computation, and data fusion time is saved. Finally, the GM-CPHD filter is added to the conventional ICI structure, and the generalization capability of the parallel inverse covariance intersection Gaussian mixture cardinalized probability hypothesis density (PICI-GM-CPHD) algorithm reduces the nonlinear complexity of the system. An experiment on the stability of Gaussian fusion models is organized and linear and nonlinear signals are compared by simulating the metrics of different algorithms, and the results show that the improved algorithm has a smaller metric OSPA error than other mainstream algorithms. Compared with other algorithms, the improved algorithm improves the signal processing accuracy and reduces the running time. The improved algorithm is practical and advanced in terms of multisensor data processing.

To solve the power allocation problem of multiple relays in cooperative communication, a sparrow improvement algorithm based on small habitats is proposed. First, the small-habitat rule increases the diversity of the energy classification of communication nodes. The problem of the premature convergence of the algorithm is solved. Subsequently, the joint jump rule is designed to replace the local search rule, and the local search rule of the sparrow algorithm is modified to avoid the local wandering phenomenon. The improved search method solves the local solution problem of the most available function. Lastly, a validation experiment of the performance of the algorithm is carried out according to the IEEE CEC 2017 benchmark document set. The simulation verifies the practicality of various algorithms. The maximum evaluation number (max FE) of the objective function is calculated to compare the performance of various algorithms. The experimental results show that the improved algorithm can increase the diversity of species. The multi-point search capability and global merit search capability are improved. Additionally, the convergence speed and computational accuracy of the algorithm are improved. The results show that the improved method is effective in controlling power in collaborative communication. The energy control algorithm has some practicality. The Friedman and Wilcoxon test criteria are applied and the convergence speed and computational accuracy of the improved algorithm are shown to be higher than those of other algorithms. This indicates that the improved power control method has some practicality in collaborative communication.

To improve the stability of automatic retransmission requests, a competition control method for automatic retransmission requests of cooperative-based listening nodes is proposed. Because the number of source nodes involved in retransmission communication is reduced, the method reduces the communication energy consumption. Cooperative communication consists of two main parts of work. When the source node sends signals to the surrounding nodes, the relay nodes and other nodes receive the broadcast signals. The signals received by both nodes are the same. A node competition mechanism based on the probability of link disruption as an indicator is designed. The node with higher energy is identified as the node that retransmits the signal. An experiment on the performance comparison of multiple algorithms is organized by simulation. The results show that the proposed new strategy has lower values in BER and BER metrics. This indicates that the proposed method transmits high values of signal quality compared to other dominant methods of retransmitting signals. Because the forwarded signal is successfully transmitted and the source node is no longer involved in the retransmission effort. In cooperative communication, the number of retransmission requests is reduced, and the energy consumed by the system is reduced. Because the improved algorithm achieves low-power communication, the proposed algorithm is practical and innovative in cooperative communication.

Routing method is a key technology for swarm communication. In order to reduce the node energy consumption in large-scale swarm communication, a routing method based on moth search link distance and residual energy is proposed. First, the swarm network is divided into sub-cluster networks by filtering of cluster head nodes. The node with more residual energy is used as cluster head to forward data signals from other nodes. The link between cluster head and edge nodes is established by relaying the signals from the nodes. The routing function of the link is established by calculating the distance and residual energy weight of the nodes. During cluster head broadcast communication, a modified moth-based search algorithm is applied to solve the combined weights of the routes. The optimal solution of the function constitutes the best route within the subcluster. Simulation results show that the proposed method meets the communication requirements, while the performance of the proposed method is higher than other mainstream algorithms by comparing the magnitude of three metrics: network coverage, residual energy and BER. The proposed routing method reduces the energy consumption of multi-node cluster network communication. The method achieves routing for large-scale cluster communication.