Robust Beamforming Design in a NOMA Cognitive Radio Network Relying on SWIPT
Robust Beamforming Design in a NOMA Cognitive Radio Network Relying on SWIPT
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DOI：
10.1109/icc.2018.8422527
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Haijian Sun;Fuhui Zhou;Zekun Zhang
中科院分区：
文献类型：
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作者：
Haijian Sun;Fuhui Zhou;Zekun Zhang
研究了一种基于无线信息和功率传输的多输入单输出非正交多址认知无线电网络。采用了一种逼真的非线性能量采集模型，并在每个二次用户处采用功率分割结构。由于实际中很难获得完美的信道状态信息(CSI)，因此考虑了一个有界的CSI误差模型。我们的稳健波束形成和功率分配比是为了最小化认知基站的发射功率而联合设计的。然后利用半定松弛将原非凸优化问题转化为凸优化问题。对于最小发射功率问题，我们证明了最优解的秩限小于等于2。仿真结果表明，该方案的性能明显优于传统的正交多址接入方案。
This paper studies a multiple-input-single-output non-orthogonal multiple access cognitive radio network relying on simultaneous wireless information and power transfer. A realistic non- linear energy harvesting model is applied and a power splitting architecture is adopted at each secondary user. Since it is difficult to obtain the perfect channel state information (CSI) in practice, a bounded CSI error model is considered. Our robust beamforming and power splitting ratio are jointly designed for minimizing the transmission power of the cognitive base station. The original non-convex optimization problem is then converted into convex forms by using semi- definite relaxation. For the minimum transmission power problem, we prove that the optimal solution has a limited rank of less than or equal to 2. Our simulation results show that the proposed scheme significantly outperforms its traditional orthogonal multiple access counterpart.