认知无线电中的稀疏信道估计与导频优化

发布时间：2018-04-02 12:27

本文选题：认知无线电　切入点：稀疏信道估计　出处：《电子与信息学报》2014年04期

【摘要】：认知无线电技术能充分利用闲置的频谱进行数据传输,从而提高频谱利用率。而稀疏信道估计能充分发掘无线信道的稀疏性,从而节省导频开销,并进一步提高频谱利用率。因此,该文研究了采用稀疏信道估计的认知无线电系统及导频优化,将信道估计转化为稀疏重建问题,以最小化观测矩阵的互相关为目标进行优化,并提出了一种快速的导频优化算法。该算法通过灵活设置外循环和内循环次数,实现了对导频序列进行逐位置的替换与优化。仿真结果表明,相比于最小二乘信道估计,稀疏信道估计能节省72.4%的导频开销,提高8.2%的频谱利用率;此外,该导频优化算法优于目前的随机优化算法,在相同的0.012误码率性能下,相比后者能节省约5 dB的信噪比。
[Abstract]:Cognitive radio technology can make full use of idle spectrum for data transmission, so as to improve spectrum efficiency.Sparse channel estimation can fully exploit the sparsity of wireless channel, thus saving pilot cost and further improving spectrum efficiency.Therefore, the cognitive radio system using sparse channel estimation and pilot optimization are studied in this paper. The channel estimation is transformed into a sparse reconstruction problem, and the objective is to minimize the cross-correlation of the observation matrix.A fast pilot optimization algorithm is proposed.By setting the times of outer loop and inner loop flexibly, the algorithm can replace and optimize the pilot sequence one by one.The simulation results show that the sparse channel estimation can save 72.4% of the pilot overhead and increase the spectral efficiency by 8.2% compared with the least square channel estimation, in addition, the pilot optimization algorithm is superior to the current random optimization algorithm.Under the same performance of 0.012 bit error rate, the SNR can be saved by about 5 dB compared with the latter.
【作者单位】：东南大学信息科学与工程学院;
【基金】：国家科技支撑计划(2012BAH15B02) 国家科技重大专项(2012ZX03001036-004) 国家自然科学基金(61302097) 教育部博士点基金(20120092120014) 华为创新研究计划资助课题
【分类号】：TN925

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