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BER analysis of underlay relay cognitive networks with imperfect Nakagami-m fading channel information

机译:具有不完美的NAKAGAMI-M衰落信道信息的底层继电器认知网络的分析

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This paper studies the effect of imperfect Nakagami-m fading channel information on the bit error rate (BER) performance of underlay relay cognitive networks (URCNs) with arbitrary number of hops, taking into account both maximum transmit power constraint (MTPC) and interference power constraint (IPC). To eliminate time-consuming simulations, we propose an exact closed-form BER expression. Various results demonstrate its validity and show the performance saturation of URCNs. Additionally, channel information imperfection (CII), the order of locating secondary users of different maximum transmit power (MTP) levels, and optimum positions of secondary relays dramatically affect their performance.
机译:本文研究了不完美的Nakagami-M衰落信道信息对具有任意数量的跳数的底层中继认知网络(URCNS)的误码率(BER)性能的影响,考虑到最大发射功率约束(MTPC)和干扰功率约束(IPC)。为了消除耗时的模拟,我们提出了精确的封闭式BER表达。各种结果展示了其有效性并显示了URCN的性能饱和度。此外,频道信息不完美(CII),定位不同最大发射功率(MTP)级别的辅助用户的顺序,以及次级继电器的最佳位置显着影响它们的性能。

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