认知无线电中的稀疏信道估计与导频优化

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。

Sparse Channel Estimation and Pilot Optimization for Cognitive Radio

Cognitive radio can make full use of idle spectrum for data transfer, and therefore improve the spectrum utilization. Sparse channel estimation explores the sparse property of wireless channels, which reduces the pilot overhead and further improves the spectrum efficiency. This paper investigates the sparse channel estimation in cognitive radio systems as well as the pilot optimization therein, and the channel estimation is formulated as a sparse recovery issue. With the objective to minimize the cross correlation of the measurement matrix, a fast pilot optimization algorithm is then proposed. By flexibly setting the number of outer loop and inner loop, each entry of pilot pattern can be sequentially updated and optimized. Simulation results show that compared to the Least Squares (LS) channel estimation, sparse channel estimation can reduce 72.4% of the pilot overhead and improve the spectrum efficiency by 8.2%. Moreover, the proposed pilot optimization algorithm outperforms the current random search algorithm by saving 5 dB of Signal to Noise Ratio (SNR) at the same 0.012 of Bit Error Rate (BER).