The growing number of services and users leads to an increasing scarcity of spectrum resource and brings challenges to terrestrial-satellite networks (TSNs). For the limited spectrum resource and long-distance transmission characteristics, the improvement of spectrum efficiency is particularly important in TSNs. In this paper, we discuss the optimization problem of spectrum resource utilization in TSNs. By establishing a rate-split multiple-access and multiple-input-single-output (MISO) based terrestrial-satellite network architecture, a joint optimization scheme of user association, power and rate allocation is proposed. The objective of maximizing the system spectrum efficiency can be obtained, so as to realize the efficient utilization of spectrum resource while guaranteeing the communication requirements of users.

Computation offloading tends to be an effective way for mitigating computing pressure of user equipments (UEs). By computation offloading, the task can be handled in network edge and/or cloud center to compensate insufficient resources and capabilities of UEs. In this study, we construct a three-tier cloud-edge integration network, where user tasks are offloaded to satellite based edge server and further to the remote ground cloud server via backhaul links. The optimization problem is modeled for minimizing system energy consumption and considers user association, power allocation, task scheduling, and bandwidth assignment jointly. By the proposed schemes based on relaxation transformation and fractional programming, four subproblems are transformed into corresponding convex optimization problems and solved respectively. In order to find the global optimal solutions, a joint iterative algorithm for three-tier computation offloading problem is designed. In numerical simulations, we compare different communication schemes and computation offloading schemes to present the rationality and superiority of the designed algorithm for reducing system energy consumption.

In this paper, an uplink non-orthogonal multiple access (NOMA) terrestrial-satellite network is investigated, where the terrestrial base stations (BSs) communicate with satellite by backhaul link, and user equipments (UEs) share spectrum resource of access link. Firstly, a utility function which consists of the achieved terrestrial user rate and cross-tier interference caused by terrestrial BSs to satellite is design. Thus, the optimization problem can be modeled by maximizing the system utility function while satisfying the varying backhaul rate and UEs’ quality of service (QoS) constraints. The optimization problem is highly non-convex and can not be solved directly. Thus, we decouple the original problem into user association sub-problem, bandwidth assignment sub-problem, and power allocation sub-problem. In user association sub-problem, an enhanced-caching, preference relation, and swapping based algorithm is proposed, where the satellite UEs are selected by the channel coefficient ratio. The terrestrial UEs association considers the both caching state and backhaul link. Then we derive the closed-form expression of the bandwidth assignment. In power allocation sub-problem, we convert the non-convex term of the target function into the convex one by the Taylor expansion, and solve the transformed convex problem by an iterative power allocation algorithm. Finally, a three-stages iterative resource allocation algorithm by joint considering the three sub-problems is proposed. Simulation results are discussed to show the effectiveness of the proposed algorithms.

In this paper, an uplink non-orthogonal multiple access (NOMA) satellite-terrestrial network is investigated, where the terrestrial base stations (BSs) can simultaneously communicate with the satellite by backhaul, and user equipments (UEs) share fronthaul spectrum resource to communicate. The communication of satellite UEs is influenced by crosstier interference caused by terrestrial cellular UEs. Thus, a utility function which consists of system achieved rate and crosstier interference is build. And we aim to maximize the utility function while satisfying the constraints of the varying backhaul rate and quality of service (QoS) of UEs. The optimization problem is decomposed into AP-UE association, bandwidth assignment, and power allocation sub-problems, and solved by proposed matching algorithm and successive convex approximation (SCA) method, respectively. The simulation results show the effectiveness of the proposed algorithm.