In this letter, a non-orthogonal multiple access (NO-MA) scheme is employed for irregular repetition slotted ALOHA (IRSA). Specifically, packet replicas are transmitted with discrete power levels which are pre-determined by the NOMA scheme. In this case, most packet collisions can be resolved in the power domain, contributing to a much lower packet loss rate. Density evolution (DE) analysis is formulated and the degree distributions are optimized for different number of power levels. Simulation results validate our analysis and show that the proposed scheme can outperform existing IRSA schemes.
In order to deal with the difficulty of spectrum sensing in cognitive satellite wireless networks, a large-scale cognitive network spectrum sensing algorithm based on big data analysis theory is studied, and a new algorithm using mean exponential eigenvalue is proposed. This new approach fully uses all the eigenvalues in sample covariance matrix of the sensing results to make the decision, which can effectively improve the detection performance without obtaining the prior information from licensed users. Through simulation, the performance of various large scale cognitive radio spectrum sensing algorithms based on big data analysis theory is compared, and the influence of satellite to ground channel conditions and the number of sensing nodes on the performance of the algorithm is discussed.
In order to maximize the available data rate and spectrum utilization efficiency, a high-throughput satellite communication system adopts the full spectrum reuse scheme, which will cause serious co-frequency interference. In this paper, a forward link model, considering the effects of free space loss, rainfall attenuation, and beam gain, is established, and the classical low-complexity of the zero-forcing precoding algorithm is improved in order to solve the serious co-frequency interference. Moreover, the regularized zero-forcing precoding algorithm considering the influence of system noise is studied, and a low complexity regularized zero-forcing dirty paper precoding algorithm is proposed, whose basic principle is to sort users based on the principle of channel maximum norm selection and practical application scenarios. Simulation results show that it can encode users sequentially, according to the channel conditions, to maximize the SINR (signal-to-interference-plus-noise ratio) and increase the throughput of the system.
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