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Benefited from the characteristics of high mobility, low cost and convenient deployment, unmanned aerial vehicles (UAVs) can be deployed in wireless communication systems as mobile base stations (BSs) to improve the communication performance of users. In addition, by deploying communication and sensing equipment and supporting efficient resource sharing of communication and sensing technologies, UAVs are expected to act as high-performance aerial platforms which integrate communication and sensing technologies. In this paper, a multi-antenna UAV-enabled joint communication and sensing scenario is examined by jointly considering the flight energy of the UAV, multi-antenna transmissions, and the user service requirements. Two optimization problems are respectively formulated for various UAV states. In particular, the problem of communication precoding and UAV flight trajectory optimization is formulated as a minimum user rate maximization problem and the joint optimization problem of UAV sensing position, communication and sensing precoding is formulated as a minimum target detection probability maximization problem. Since the minimum-rate maximization problem is a non-convex optimization problem, which is difficult to solve directly, we decompose the original optimization problem into a communication precoding design subproblem and a UAV trajectory design subproblem, and solve the two subproblems successively by applying an alternate iteration method. Specifically, a zero forcing (ZF) algorithm is put forward for solving the communication precoding design subproblem. A successive convex approximation (SCA) algorithm is applied to determine the optimal trajectory of the UAV. Based on the optimal trajectory of the UAV, the sensing position optimization problem is modeled as a weighted distance minimization problem, and then a heuristic algorithm is proposed to obtain the optimal positions. Finally, a ZF algorithm-based joint communication and sensing precoding is presented. The effectiveness of the proposed algorithm is verified by simulations.

Unmanned aerial vehicles (UAVs) have emerged as a promising solution to provide wireless data access for ground users (GUs) in various applications. In the scenario where GUs are far away from the gateway, both the access and backhaul links in UAV networks significantly affect the system performance. In this paper, we study the UAV deployment problem in an integrated access and backhaul network (IABN), where a number of UAVs are deployed as aerial base stations (ABSs) and aerial relays (ARs) to forward GUs $^\prime$ data packets to the remote gateway via multi-hop transmissions. Aiming at minimizing the system cost, which is defined as the weighted sum of UAV deployment cost and the energy consumption required for data transmission, a constrained system cost minimization problem is formulated, where the joint UAV deployment, GU association, route selection and resource allocation strategy is optimized. To solve the formulated non-convex problem, we propose an alternative iteration method, which includes two subalgorithms. For the first subalgorithm, we focus on the optimal design of the access links and propose a joint ABS deployment and resource allocation algorithm. Specifically, a modified K-means based clustering scheme is proposed to determine ABS deployment and GU association strategy, then an iterative Lagrange-based power allocation strategy and a greedy Kuhn-Munkras algorithm-based subchannel assignment strategy are presented, respectively. Given the obtained ABS deployment strategy, for the second subalgorithm, we then design a joint AR deployment, route selection and power allocation scheme for the backhaul links and propose a minimum circle algorithm-based AR deployment and route selection strategy. Finally, the suboptimal solution is obtained by alternatively handling the two subalgorithms. Numerical results show that the proposed UAV deployment algorithm can achieve a 19% performance gain over the existing scheme when the packet size is $6\times 10^{6}$ bits and system bandwidth is $0.5\times 10^{6}$ Hz.

In order to ensure efficient data transmission from satellites to ground stations (GSs), resource allocation schemes must be designed. In cases where direct data transmission from satellites to GSs is not possible due to a dynamic network topology and limited contact time, efficient relay selection or route selection schemes should be employed. This paper considers a satellite communication network in which a number of source low earth orbit(SLEO) satellites are attempting to transmit their data flows to the designated GSs. To improve the transmission performance of the data flows, one geosynchronous earth orbit (GEO) satellite and a number of relay LEO (RLEO) satellites in the network are used as relays. To maximize energy efficiency, a joint route selection and resource allocation mechanism is proposed. The energy cost of the system, which is the sum of the energy cost of the SLEO satellites and the RLEO satellites, is used to formulate the joint route selection and resource allocation as a system energy cost minimization problem. Since the original optimization problem is NP hard, it is transformed into three subproblems: inter-satellite power allocation, satellite-ground power allocation, and route selection. These subproblems are solved using a greedy algorithm, the Lagrange dual method, and a matching-based heuristic algorithm, respectively. The numerical results demonstrate the effectiveness of the proposed scheme.