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Effects of imperfect channel sensing and estimation on the performance of cognitive radio systems

机译：不完善的信道感知和估计对认知无线电系统性能的影响

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摘要

In realistic scenarios of cognitive radio (CR) systems, imperfect channel sensing may occur due to false alarms and miss detections. Channel estimation between the secondary user transmitter and another secondary user receiver is another challenge in CR systems, especially for frequency-selective fading channels. In this context, this paper presents a study of the effects of imperfect channel sensing and channel estimation on the performance of CR systems. In particular, different methods of channel estimation are analyzed under channel sensing imperfections. Initially, a CR system model with channel sensing errors is described. Then, the expectation maximization (EM) algorithm is implemented in order to learn the channel fading coefficients. By exploiting the pilot symbols and the detected symbols at the secondary user receiver, we can estimate the channel coefficients. We further compare the proposed EM estimation algorithm with different estimation algorithms such as the least squares (LS) and linear minimum mean square error (LMMSE). The expressions of channel estimates and mean squared errors (MSE) are determined, and their dependencies on channel sensing uncertainty are investigated. Finally, to reduce the complexity of EM algorithm, a sub-optimal algorithm is also proposed. The obtained results show that the proposed sub-optimal algorithm provides a comparable bit error rate (BER) performance with that of the optimal one yet with less computational complexity.
机译：在认知无线电（CR）系统的实际场景中，由于错误的警报和未命中检测，可能会发生不完善的信道感测。在CR系统中，特别是对于频率选择衰落信道，在次要用户发射机和另一个次要用户接收机之间的信道估计是另一个挑战。在这种情况下，本文提出了不完善的信道感知和信道估计对CR系统性能的影响的研究。特别地，在信道感测缺陷下分析了不同的信道估计方法。首先，描述具有信道感测误差的CR系统模型。然后，实施期望最大化（EM）算法以学习信道衰落系数。通过在辅助用户接收器处利用导频符号和检测到的符号，我们可以估计信道系数。我们进一步将提出的EM估计算法与不同的估计算法（例如最小二乘法（LS）和线性最小均方误差（LMMSE））进行比较。确定了信道估计和均方误差（MSE）的表达式，并研究了它们对信道感测不确定性的依赖性。最后，为降低EM算法的复杂度，提出了一种次优算法。获得的结果表明，所提出的次优算法可提供与最佳算法相当的误码率（BER）性能，但计算复杂度较低。

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