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.

Power control plays a significant role in cognitive networks, which promotes the spectrum sharing between heterogeneous systems. This letter presents the mathematical model for cognitive low earth orbit (LEO) satellite constellation with terrestrial networks, which takes the dynamic characteristics of the LEO satellite into consideration. Two optimal power control schemes are proposed from the long-term and short-term perspectives, which aims to maximize the delay-limited capacity and minimize the outage probability, respectively. Solutions to the optimization problems are analyzed, and numerical results evaluate the performance of the schemes.

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 letter, optimal power allocation strategies are proposed to improve the localization performance in jammed wireless localization systems. For a wireless localization system, the power allocation is a key issue because it relates to network lifetime and localization accuracy. However, when jammer nodes are considered, the power allocation problem for anchor nodes becomes a new task due to the effect of jammer nodes. So in this letter, two power allocation schemes are introduced in jammed wireless localization systems. In the first scheme, the average Cramer–Rao lower bound (CRLB) of agent nodes is the objective and in the second scheme, the objective is to minimize the maximum CRLB of agent nodes. In particular, both power allocation problems can be transformed into semi-definite programs (SDP) with total and peak power constraints. Numerical results indicate that the proposed schemes can reach better performance in localization accuracy compared to uniform power allocation strategies.

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.