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Joint power and admission control based on hybrid users in cognitive radio network

机译:认知无线电网络中基于混合用户的联合功率和接纳控制

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Hybrid users or primary-secondary users (PSUs) are advanced primary users that are able to access the secondary network using their cognitive functions. Hybrid users can appear as a new type of cognitive users that would be promising for future cognitive radio networks (CRNs). Regarding the unique capabilities of such users and due to the lack of considerable research on this subject, we study the problem of joint power and admission control in spectrum underlay CRN based on hybrid users. In order to fully take the advantages of cognitive capability, an advanced cognitive radio network (ACRN) is proposed by employing the PSUs, and the corresponding power and signal-to-interference-plus-noise ratio (SINR) are derived. Then, feasibility checking mechanism is investigated and the optimal value of interference temperature limit for PSUs is also obtained. A new formulation to maximize the number of admitted secondary users (SUs) in ACRN is presented. Moreover, two power and admission control algorithms are proposed which significantly improve the network performance not only in the number of admitted SUs but also in transmit power consumption. For a feasible network, the problem of aggregate throughput maximization is solved using successive geometric programming. Afterwards, it is proved that our proposed ACRN can improve the aggregate throughput of SUs. The superior efficiency of ACRN in terms of the number of admitted SUs, transmit power consumption and aggregate throughput is verified by simulation results for different scenarios. (C) 2018 Elsevier B.V. All rights reserved.
机译:混合用户或主要-次要用户(PSU)是能够使用其认知功能访问次要网络的高级主要用户。混合用户可以作为一种新型的认知用户出现,这对未来的认知无线电网络(CRN)很有前途。关于此类用户的独特功能,由于缺乏对此主题的大量研究,我们研究了基于混合用户的频谱底层CRN中的联合功率和准入控制问题。为了充分利用认知能力的优势,提出了一种采用PSU的高级认知无线电网络(ACRN),并推导了相应的功率和信噪比(SINR)。然后,研究了可行性检查机制,并获得了PSU的干扰温度极限的最佳值。提出了一种新的配方,可以最大程度地提高ACRN中允许的二级用户(SU)数量。此外,提出了两种功率和接纳控制算法,它们不仅在接纳的SU数量上而且在发射功率上都显着提高了网络性能。对于可行的网络,使用连续几何规划解决了总吞吐量最大化的问题。之后,证明了我们提出的ACRN可以提高SU的总吞吐量。通过针对不同场景的仿真结果,可以证明ACRN在允许的SU数量,发射功率消耗和总吞吐量方面的卓越效率。 (C)2018 Elsevier B.V.保留所有权利。

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