摘要
针对低精度模数转换器(ADC)下的大规模多输入多输出正交频分复用(MIMO-OFDM)系统,提出一种基于量化压缩感知的信道估计算法——块稀疏多比特迭代硬阈值(B-MIHT)算法。该算法挖掘了大规模MIMO-OFDM系统信道的块稀疏特性,通过构建等效块稀疏信道矩阵结合多比特迭代硬阈值算法,基于训练序列对低精度ADC下的大规模MIMO-OFDM系统进行信道估计,并在MATLAB平台进行仿真实验。结果表明:B-MIHT算法能够准确地恢复低精度ADC下的大规模MIMO-OFDM系统信道信息,在系统量化精度为5 bits的条件下具有良好的信道估计性能,信噪比为30 dB时,误码率(BER)为5.45×10^(-3),归一化均方误差(NMSE)为1.73×10^(-3),且在信道路径数增多的情况下其信道估计性能损失相对较小。
A channel estimation algorithm based on quantized compressive sensing is proposed for massive multiple input multiple output-orthogonal frequency division multiplexing(MIMO-OFDM)systems with low-precision analog-to-digital converters(ADCs),which is the block sparsity multi-bit iterative hard thresholding(B-MIHT)algorithm.The B-MIHT algorithm exploits the block sparsity characteristics of massive MIMO-OFDM system channels,and combines with the multi-bit iterative hard thresholding algorithm by constructing the equivalent block sparse channel matrices.The algorithm estimates the channel information of massive MIMO-OFDM systems with low-precision ADCs based on training sequences,the simulation is performed on MATLAB platform.The results show that B-MIHT algorithm can accurately recover the channel information of massive MIMO-OFDM systems with low-precision ADCs and has good channel estimation performance under the condition that the system quantization accuracy is 5 bits.When the signal to noise ratio is 30 dB,the bit error rate(BER)of B-MIHT algorithm is 5.45×10^(-3) and the normalized mean square error(NMSE)is 1.73×10^(-3).The channel estimation performance loss of B-MIHT algorithm is relatively small when the number of channel paths increases.
作者
戈立军
朱德宝
GE Li-jun;ZHU De-bao(School of Electronics and Information Engineering,Tiangong University,Tianjin 300387,China)
出处
《天津工业大学学报》
CAS
北大核心
2021年第2期74-80,共7页
Journal of Tiangong University
基金
国家自然科学基金青年基金资助项目(61302062)。
关键词
大规模MIMO-OFDM
低精度ADC
信道估计
量化压缩感知
块稀疏特性
massive multiple input multiple output-orthogonal frequency division multiplexing(MIMO-OFDM)
low-precision analog-to-digital converter(ADC)
channel estimation
quantized compressive sensing
block sparsity characteristic
