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Optimal energy efficient scheme for MIMO-based cognitive radio networks with antenna selection

机译:具有天线选择的基于MIMO的认知无线电网络的最佳节能方案

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Multiple-input multiple-output (MIMO), which is also known as large-scale antenna system, is a promising technology for achieving the high spectrum efficiency of wireless communications networks. On the other hand, as a smart spectrum sharing technology, Cognitive Radio Network (CRN) is also expected to improve the utilization of spectrum usage for conciliating the current spectrum demand growth. Thus, the combination of MIMO and CRN has received extensive research attention in recent years. Although the large-scale antenna system can yield large network capacities, the radio-frequency (RF) chain also increases as the number of antennas gets large, which also increases the computational complexity, energy consumption, and hardware cost for the wireless networks. As a novel signal processing technology, antenna selection can reduce the number of RF chains while guaranteeing the performance under the system requirements. However, how to efficiently integrate these techniques to optimize energy efficiency still remains as an open and challenging problem. To overcome these challenges, in this paper we propose the optimal energy efficiency scheme for MIMO-based cognitive radio networks with antenna selection. In particular, we develop the joint transmit-power allocation and antenna subsets selection schemes for the transmitter to maximize the CRN's energy efficiency implemented under the constrains of the maximum interference caused by the secondary users (SU) to the primary users (PU), maximum transmission power from SU, and the minimum transmission rate in SU link. Finally, the obtained simulation results validate and evaluate our proposed schemes.
机译:多输入多输出(MIMO),也称为大规模天线系统,是一种用于实现无线通信网络的高频谱效率的有前途的技术。另一方面,作为一种智能频谱共享技术,认知无线电网络(CRN)也有望提高频谱利用率,以协调当前的频谱需求增长。因此,近年来,MIMO和CRN的组合受到了广泛的研究关注。尽管大型天线系统可以产生较大的网络容量,但是随着天线数量的增加,射频(RF)链也会增加,这也会增加无线网络的计算复杂性,能耗和硬件成本。作为一种新颖的信号处理技术,天线选择可以减少RF链的数量,同时保证系统要求下的性能。然而,如何有效地整合这些技术以优化能源效率仍然是一个悬而未决的难题。为了克服这些挑战,本文提出了一种基于天线选择的基于MIMO的认知无线电网络的最佳能效方案。特别是,我们开发了针对发射机的联合发射功率分配和天线子集选择方案,以在次要用户(SU)对主要用户(PU)造成的最大干扰,最大SU的传输功率,以及SU链路的最小传输速率。最后,获得的仿真结果验证并评估了我们提出的方案。

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