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Resource allocation for multi-antenna multicast in OFDM-based cognitive radio networks with imperfect channel information

机译:具有不完善信道信息的基于OFDM的认知无线电网络中多天线组播的资源分配

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In this paper, multi-antenna multicast in OFDM-based cognitive radio networks is studied with imperfect channel information at the cognitive base station. The resource allocation problem is formulated to maximize the total weighted transmission rate of all multicast sessions subject to the maximal outage probability constraint of secondary users (SUs) and the maximal interference overweighing probability constraint of primary users (PUs). To tackle the problem efficiently, the closed-form expressions for these two probability constraints are derived, based on which a two-step resource allocation method is proposed: firstly perform the normalized beamforming vector optimization and the subcarrier assignment, and secondly carry out the power allocation. Meanwhile, to optimize the normalized beamforming vectors, two kinds of methods are presented, with one method suitable for the high interference threshold region and the other for the low interference threshold region. Simulation results show that compared with the traditional method which does not consider the channel estimation error, our proposed method can improve the total successfully received rate of multicast users hugely while guaranteeing SUs' outage probability constraint and PUs' interference overweighing probability constraint very well.
机译:本文研究了基于OFDM的认知无线电网络中的多天线组播,其中认知基站的信道信息不完善。制定资源分配问题,以使所有多播会话的总加权传输速率最大化,这要受辅助用户(SU)的最大中断概率约束和主要用户(PU)的最大干扰压倒概率约束。为了有效地解决该问题,导出了这两种概率约束的闭式表达式,在此基础上提出了一种两步式资源分配方法:首先进行归一化波束成形矢量优化和子载波分配,然后进行功率分配。分配。同时,为了优化归一化波束形成矢量,提出了两种方法,一种方法适合于高干扰阈值区域,而另一种方法适合于低干扰阈值区域。仿真结果表明,与不考虑信道估计误差的传统方法相比,本文提出的方法可以在保证用户设备的中断概率约束和用户设备的干扰超过概率约束的同时,极大地提高组播用户的成功接收率。

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