期刊
IEEE WIRELESS COMMUNICATIONS LETTERS
卷 10, 期 8, 页码 1667-1671出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2021.3076579
关键词
Manganese; NOMA; Precoding; Satellites; Low earth orbit satellites; Optimization; Silicon carbide; mMIMO; NOMA; convex optimization; LEO satellite communication system
资金
- National Key Research and Development Program of China [2018YFB1801103]
- Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu Province [BK20192002]
- National Natural Science Foundation of China [61901516]
- China Postdoctoral Science Foundation [2019M651648]
- Natural Science Foundation of Jiangsu Province [BK20180578]
In this letter, the application of NOMA in mMIMO LEO satellite communication system to improve spectral efficiency is considered. By decoupling the problem into precoding vectors design and transmit power optimization, an iterative algorithm is proposed to address the sum rate maximization problem. Simulation results validate the convergence of the proposed algorithm and demonstrate the superiority of the mMIMO NOMA approach.
In this letter, we consider non-orthogonal multiple access (NOMA) applying into massive multiple input multiple output (mMIMO) low earth orbit (LEO) satellite communication system (SCS) to improve spectral efficiency. Specifically, we investigate sum rate maximization problem with transmit power, quality of service (QoS) constraints, imperfect successive interference cancelation (SIC) and imperfect channel state information (CSI) considered. We decouple the original problem into two parts: precoding vectors design and transmit power optimization. Precoding vectors are derived for maximizing the average signal power in each beam and eliminating inter-beam interference (IBI). Then, transmit power optimization problem is transformed into a convex optimization problem by utilizing the first order Taylor expansion, an iterative algorithm is proposed to get the optimal sum rate. Finally, simulation results verify the convergence of the proposed algorithm and our proposed mMIMO NOMA approach is better than other approaches.
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