Novel Markov channel predictors for interference alignment in cognitive radio network
Novel Markov channel predictors for interference alignment in cognitive radio network
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用于认知无线电网络中干扰对齐的新型马尔可夫信道预测器
DOI：
10.1007/s11276-017-1445-x
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发表时间：
2017-01
影响因子：
3
通讯作者：
Cheng Qingqing
中科院分区：
文献类型：
--
作者：
Shi Zhenguo;Wu Zhilu;Yin Zhendong;Yang Zhutian;Cheng Qingqing
在认知无线电（CR）网络中，如何降低不同用户之间的干扰是一个关键的任务。干扰对齐技术是解决通信系统中多用户干扰的一种有效方法。与其他干扰管理方法（如迫零）相比，IA不仅可以有效地消除干扰，而且可以大大增加系统容量。然而，IA算法需要发送端和接收端都具有理想的信道状态信息（CSI），这在实际应用中很难实现。本文从信干噪比和可达和速率两个方面分析了非理想CSI对CR系统IA的影响。为了降低不完美CSI对CR网络系统性能的影响，提出了一种线性有限状态马尔可夫链（LFSMC）预测器，该预测器将有限状态马尔可夫链与AR预测器相结合。此外，为了简化LFSMC预测器的初始化，提出了一种简化的LFSMC（S-LFSMC）预测器。仿真结果表明，LFSMC和S-LFSMC预测器都可以大大提高IA系统的性能与不准确的CSI。具体地说，LSFMC预测器可以达到令人满意的性能与本文提到的其他预测。而LSFMC预测器结构简单，性能仍优于传统的预测器。因此，我们可以选择一个合适的预测器（LAFMC或S-LSFMC）根据不同的要求。
In cognitive radio (CR) network, how to mitigate interference between different users is a key task. Interference alignment (IA) is a promising technique to tackle the multi-user interference in communication system. Compared with other interference management methods (such as zero-forcing), IA can not only effectively eliminate the interference, but also greatly increase the system capacity. However, the perfect channel state information (CSI) is required for both transmitters and receivers to apply the IA algorithm, which is hard to achieve in practical applications. In this paper, the effect of imperfect CSI on IA in CR system is analyzed in terms of signal to interference plus noise ratio and achievable sum rate. A linear finite state Markov chain (LFSMC) predictor, which incorporates the finite state Markov chain into the AR predictor, is proposed to reduce the impact of imperfect CSI on system performance of CR network. Moreover, for the sake of simplifying the initialization of LFSMC predictor, a simplified LFSMC (S-LFSMC) predictor is provided. Simulation results indicate that both of the LFSMC and S-LFSMC predictor can greatly improve the performance of IA system with the inaccurate CSI. Specifically, the LSFMC predictor can achieve satisfied performance compared with other predictors mentioned in this paper. And the LSFMC predictor which is simpler and its performance is still much better than traditional predictors. Therefore, we can choose a suitable predictor (LAFMC or S-LSFMC) based on the different requirements.
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作者：
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影响因子：
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DOI：
10.1109/jstsp.2010.2093210
发表时间：
2011-02-01
影响因子：
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