Noise cancellation in cognitive radio systems: A performance comparison of evolutionary algorithms

Adnan Quadri, M. R. Manesh, N. Kaabouch
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引用次数: 11

Abstract

Noise cancellation is one of the important signal processing functions of any communication system, as noise affects data integrity. In existing systems, traditional filters are used to cancel the noise from the received signals. These filters use fixed hardware which is capable of filtering specific frequency or a range of frequencies. However, next generation communication technologies, such as cognitive radio, will require the use of adaptive filters that can dynamically reconfigure their filtering parameters for any frequency. To this end, a few noise cancellation techniques have been proposed, including least mean squares (LMS) and its variants. However, these algorithms are susceptible to non-linear noise and fail to locate the global optimum solution for de-noising. In this paper, we investigate the efficiency of two global search optimization based algorithms, genetic algorithm and particle swarm optimization in performing noise cancellation in cognitive radio systems. These algorithms are implemented and their performances are compared to that of LMS using bit error rate and mean square error as performance evaluation metrics. Simulations are performed with additive white Gaussian noise and random nonlinear noise. Results indicate that GA and PSO perform better than LMS for the case of AWGN corrupted signal but for non-linear random noise PSO outperforms the other two algorithms.
认知无线电系统中的噪声消除:进化算法的性能比较
噪声消除是任何通信系统中重要的信号处理功能之一,因为噪声会影响数据的完整性。在现有的系统中,传统的滤波器用于消除接收信号中的噪声。这些滤波器使用固定的硬件,能够过滤特定的频率或频率范围。然而,下一代通信技术,如认知无线电,将需要使用自适应滤波器,可以动态地重新配置任何频率的过滤参数。为此,提出了几种噪声消除技术,包括最小均方(LMS)及其变体。然而,这些算法容易受到非线性噪声的影响,无法找到全局最优解进行降噪。本文研究了两种基于全局搜索优化的算法——遗传算法和粒子群算法在认知无线电系统中进行噪声消除的效率。实现了这些算法,并以误码率和均方误差作为性能评价指标,与LMS进行了性能比较。用加性高斯白噪声和随机非线性噪声进行了仿真。结果表明,对于AWGN损坏信号,遗传算法和粒子群算法的性能优于LMS,但对于非线性随机噪声,粒子群算法的性能优于其他两种算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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