摘要
OFDM系统中,基于压缩感知的稀疏信道估计能够充分利用无线信道的固有稀疏性,进而降低导频开销,提高频谱利用率。针对压缩感知信道估计的导频设计,通过最小化压缩感知理论中测量矩阵的互相关性,提出一种基于混合遗传算法的导频优化方法。该方案首先采用遗传算法获得次优初始导频序列,然后结合导频位置以及导频功率对导频序列逐位进行替换、优化,以使测量矩阵的互相关性最小。MATLAB仿真结果表明,相比于伪随机导频设计和等间距导频设计,该算法能够保证较低的均方误差和误码率。
In OFDM system, sparse channel estimation based on compressed sensing（CS） can make full use of the inherent sparse degree of the wireless channel, which can reduce the pilot overhead and improve the spectrum efficiency. Therefore, a new method based on hybrid genetic algorithm was investigated for the pilot design of CS channel estimation, which was based on the minimization of the matrix cross correlation in the CS theory. In this method, genetic algorithm was used to obtain the initial sub-optimal pilot sequence, and then combined with the pilot position and pilot power, each entry of pilot pattern could be sequentially updated and optimized to make the minimum correlation of measurement matrix. Simulation results show that the proposed method can ensure a better mean square error and bit error rate compared to the pseudo-random pilot design and the equal distance pilot design.
出处
《电信科学》
北大核心
2016年第9期75-81,共7页
Telecommunications Science
基金
国家科技重大专项基金资助项目(No.2014ZX03003004-003)~~
关键词
信道估计
互相关性
混合遗传算法
测量矩阵
压缩感知
导频图案
channel estimation
cross correlation
hybrid genetic algorithm
measurement matrix
compressed sensing
pilot pattern
