To satisfy the explosive access demands of Internet-of-Things (IoT) devices, various kinds of multiple access techniques have received much attention. In this article, we investigate the multicast communication of a satellite and aerial-integrated network (SAIN) with rate-splitting multiple access (RSMA), where both satellite and unmanned aerial vehicle (UAV) components are controlled by network management center and operate in the same frequency band. Considering a content delivery scenario, the UAV subnetwork adopts the RSMA to support massive access of IoT devices (IoTDs) and achieve desired performances of interference suppression, spectral efficiency, and hardware complexity. We first formulate an optimization problem to maximize the sum rate of the considered system subject to the signal-interference-plus-noise-ratio requirements of IoTDs and per-antenna power constraints at the UAV and satellite. To solve this nonconvex optimization problem, we exploit the sequential convex approximation and the first-order Taylor expansion to convert the original optimization problem into a solvable one with the rank-one constraint, and then propose an iterative penalty function-based algorithm to solve it. Finally, simulation results verify that the proposed method can effectively suppress the mutual interference and improve the system sum rate compared to the benchmark schemes.

In this paper, we propose a joint optimization design for a non-orthogonal multiple access (NOMA) based satellite-terrestrial integrated network (STIN), where a satellite multicast communication network shares the millimeter wave (mmWave) spectrum with a cellular network employing NOMA technology. By assuming that the satellite uses multibeam antenna array and the base station employs uniform planar array (UPA), we first formulate a constrained optimization problem to maximize the sum rate of the STIN while satisfying the constraint of per-antenna transmit power and quality-of-service (QoS) requirements of both satellite and cellular users. Since the formulated optimization problem is NP-hard and mathematically intractable, we develop a novel user pairing scheme so that more than two users can be grouped in a cluster to exploit NOMA technique. Based on the user clustering, we further propose to transform the non-convex problem into an equivalent convex one, and present an iterative penalty function based beamforming (BF) scheme to obtain the weight vectors and power coefficients with fast convergence. Simulation results confirm the effectiveness and superiority of the proposed approach in comparison with the existing works.

The increasing demand for high-rate broadcast and multicast services over satellite networks has pushed for the development of high throughput satellites characterized by a large number of beams (e.g., more than 100). This, together with the variable distribution of data traffic request across beams and over time, has called for the design of a new generation of satellite payloads, able to flexibly allocate bandwidth and power. In this context, this paper studies the problem of radio resource allocation in the forward link of multibeam satellite networks adopting the digital video broadcasting-satellite-second generation standard. We propose a novel objective function with the aim to meet as close as possible the requested traffic across the beams while taking fairness into account. The resulting non-convex optimization problem is solved using a modified version of the simulated annealing algorithm, for which a detailed complexity analysis is presented. Simulation results obtained under realistic conditions confirm the effectiveness of the proposed approach and shed some light on possible payload design implications.

In the context of the 5G ecosystem, the integration between the terrestrial and satellite networks is envisioned as a potential approach to further enhance the network capabilities. In light of this integration, the satellite community is revisiting its role in the next generation 5G networks. Emerging technologies such as Software-Defined Networking (SDN) which rely on programmable and reconfigurable concepts, are foreseen to play a major role in this regard. Therefore, an interesting research topic is the introduction of management architecture solutions for future satellite networks driven by means of SDN. This anticipates the separation of the data layer from the control layer of the traditional satellite networks, where the control logic is placed on programmable SDN controllers within traditional satellite devices. While a centralized control layer promises delay reductions, it introduces additional overheads due to reconfiguration and migration costs. In this paper, we propose a method to quantify the overhead imposed on the network by the aforementioned parameters while investigating the use-case scenario of an SDN-enabled satellite space segment. We make use of an optimal controller placement and satellite-to-controller assignment which minimizes the average flow setup time with respect to varying traffic demands. Furthermore, we provide insights on the network performance with respect to the migration and reconfiguration cost for our proposed SDN-enabled architecture. Finally, we compare our proposed space segment SDN-enabled architecture with alternative solutions in the state-of-the-art given the aforementioned performance metrics.

Software Defined Networking (SDN) has been identified as a potential approach to achieve a more flexible control and management of the traditional satellite systems and enhance the opportunities for future services including the possibility of a hybrid satellite/terrestrial network. Given the renewed interest towards Low-Earth-Orbit (LEO) constellations, an interesting research topic is the design of a suitable network management model taking into account user specific metrics. In this paper, we address this issue while investigating the use-case scenario of an SDN-enabled satellite space segment. A Dynamic Controller Placement Problem (DCPP) is considered for a LEO constellation where the traffic demands change dynamically based on users' geographical position and time zone. To this end, we develop a mathematical model and formulate it as an Integer Linear Programming (ILP) guaranteeing an optimal controller placement and satellite-to-controller assignment minimizing the average flow setup time with respect to the traffic dynamics. We show results for the DCPP regarding the average flow setup time. Furthermore, a comparison with respect to the static approach is investigated and the proposed SDN-enabled LEO constellation architecture is compared with alternative architectures proposed in the state of the art.