With the development of satellite communication, the number of satellites in space continuously increases. However, the available spectrum resources are scarce. To address the spectrum scarcity, sharing the spectrum between different communication systems is a promising option. In this paper, a novel cognitive satellite network with geostationary earth orbit (GEO) and low earth orbit (LEO) broadband systems is studied in the downlink case. First, we present a general interference analysis model and simplify it by transforming the spatial dimension into the time dimension according to the satellite motion. Based on the interference analysis, an optimization algorithm with beamhopping and adaptive power control techniques is proposed, which can simultaneously enhance the spectral efficiency and protect the primary system. The system performance of the coexistence scenario is evaluated in terms of the system throughput. Numerical results demonstrate that the spectrum-sharing method between GEO and LEO systems is feasible, and the cognitive network can achieve a high spectral efficiency. Furthermore, the factors that affect the system in the spectral coexistence scenario are analyzed.

With the growing demand, Wireless Multimedia Sensor Networks (WMSNs) play an increasingly important role, which enhances the capacity of typical Wireless Sensor Networks (WSNs). Additionally, integrating satellite systems into WMSNs brings about the beneficial synergy, especially in rural and sparsely populated areas. However, the available spectrum resource is scarce, which contradicts the high-speed content required for multimedia. Cognitive radio is a promising solution to address the conflict. In this context, we propose a novel spectrum-sharing method for the integrated wireless multimedia sensor and cognitive satellite network based on the dynamic frequency allocation. Specifically, the Low Earth Orbit (LEO) satellite system plays the role of the auxiliary to connect sensor nodes and the remote control host, and it shares the same frequency with the Geostationary Earth Orbit (GEO) system in the downlink. Because the altitudes of GEO and LEO satellites differ greatly, the beam size of GEO is much larger than that of LEO, which provides the opportunity for LEO beam to reuse the frequency that was allocated to the GEO beam. A keep-out region is defined to guarantee the spectral coexistence based on the interference analysis in the worst case. In addition, a dynamic frequency allocation algorithm is presented to deal with the dynamic configuration caused by the satellite motion. Numerical results demonstrate that the dynamic spectrum-sharing method can improve the throughput.

In this paper, the user selection problem is investigated in the multi-user satellite multiple-input multiple-output (MIMO) downlink, which includes a multi-beam geostationary earth orbit (GEO) satellite in space and multiple terrestrial users on the ground. Considering that the number of users is much larger than that of beams or feeds, user selection should be performed so that multiple users can be served successively in different time slots. In order to meet the quality of service (QoS) requirements of users and maximize the system sum rate, three user selection algorithms are proposed: greedy user selection (GUS) algorithm, add swap user selection (AS) algorithm, and random add swap user selection (RAS) algorithm. Among the three algorithms, only the "add" operation is adopted in the GUS algorithm, both the "add", and "swap" operations are considered in the AS algorithm. On this basis, heuristic search and "random swap" operation are introduced and the RAS algorithm is obtained. The complexity of the above three algorithms is analyzed analytically and the RAS algorithm is found to have the highest complexity. Besides, the performance of the three algorithms is analyzed and compared through simulations, where the random user selection (RUS) algorithm is taken as a benchmark. Numerical results show that the system sum rate and energy efficiency are greatly improved by the proposed algorithms and the RAS algorithm has the best performance.

massive MIMO technology is critical in the 5G mobile communication system, which can greatly improve the frequency efficiency and communication capacity of terrestrial mobile communication systems, and also provides a new thinking for LEO satellite communication systems to fully reuse frequency between beams. In the paper, according to the massive MIMO-LEO satellite communications theory, we focus on the scenario of demands distributing unevenly spatially, and study the resource allocation problem of LEO satellite communications with full frequency reuse from the spatial perspective. Under the LEO satellite coverage-zoning model, we propose a rasterizing-coverage model for LEO satellites based on the characteristics of massive MIMO, establish a spatial resource allocation model with full frequency reuse, which models the spatial resource allocation problem with the objective of maximizing throughput as an integer programming problem, and put forward a greedy-based spatial resource allocation method. The simulation results show that when the service demand is 100% and the number of spatial resource groups is 16, compared with the fixed-beam method and the hopping-beam allocation method, the resource utilization can be improved respectively by 19% and 26%, and the satisfaction can be improved respectively by 16% and 17.5%, whose improvement are enhanced with the size of the on-board MIMO antenna increasing.

With the growing demand, wireless sensor networks (WSNs) play an increasingly important role in many fields. The focus has been on deploying Luby transform (LT) codes in WSNs because of their inherent advantages in erasure channels. Since most data transmitted by WSNs are small, the Gaussian elimination (GE) decoding algorithm is highly attractive. In this paper, we investigate the performance analysis and code design methods for LT codes under the GE algorithm. First, the analytical expressions of the data erasure probability are detailed for two simple kinds of LT codes. Then, a characteristic indicator of the data erasure probability is introduced to evaluate the performance of general LT codes. Finally, we propose novel design methods by optimizing degree distributions from three goals based on the characteristic indicator. Numerical results validate the proposed performance analysis and code design methods in comparison with the conventional method.