压缩感知实现BBU-RRU之间的数据压缩

王军选; 李小平

doi:doi:10.13756/j.gtxyj.2016.05.021

光通信研究, 2016 (5): 71, 网络出版: 2016-12-21

压缩感知实现BBU-RRU之间的数据压缩

Data Compression Technology Between BBU and RRU with Compressed Sensing

论文大纲

王军选李小平 ^*

作者单位

西安邮电大学通信与信息工程学院, 西安 710121

正交频分复用信号压缩感知稀疏度损伤率误比特率 OFDM signal compressed sensing sparsity damage rate bit error rate

摘要

在BBU-RRU(基带单元和射频单元)之间的下行链路中, 噪声对信号的影响比较小, 且信号存储需要大量的空间, 同时OFDM(正交频分复用)信号频域的稀疏特性满足压缩感知理论的要求。基于上述问题, 利用压缩感知理论实现了不同信噪比下OFDM信号的恢复, 并对其进行了性能分析。仿真结果表明, 该方法能节省将近5倍的信号存储所需空间。通过分析不同信噪比下恢复信号的损伤率和误比特率的特征可知, 其满足通信系统正常工作的需求。

Abstract

The influence of noise on the signal is small between the Base Band Unit and the Remote Radio Unit (BBU-RRU) in downlink and need a lot of space for storage. The sparseness of frequency domain of OFDM signal meets the requirement of the compressed sensing theory. Based on the above issues, this paper realizes OFDM signal reconstruction under different signal-to-noise ratios by using the compressed sensing theory. The simulation results show that this method can save nearly five times storage space of signal. Reconstruction signal has different damage rate and bit error rate under different signal-to-noise ratio, which meets the demand of communication system in the practical situation.

参考文献

[1] DONOHOD. Compressed sensing[J]. IEEE Trans Information Theory, 2006, 52(4): 1289-1306.

[2] Candès E J. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information[J]. IEEE Trans Information Theory, 2006, 52(2): 489-509.

[3] 王庆国,王华力,罗章凯. 压缩感知理论应用于OFDM接收机系统的研究[J]. 军事通信技术. 2014, 35(04): 65-70.

[4] Wu Ji, Liang Qilian, Zhang Baoju, et al. Adaptive compressive sensing for multiuser OFDM system[C]//ICC 2013. Budapest, Hungary: IEEE, 2013: 977-981.

[5] Cai T Tony,Wang Lie.Orthogonal matching pursuit for sparse signal recovery with noise[J]. IEEE Trans Information Theory,2011, 57(7): 4680-4688.

[6] Tropp J A ,Gilbert A C.Signal recovery from random measurements via orthogonal matching pursuit[J].IEEE Trans Information Theory,2007, 53(12): 4655-4666．

[7] 杨成,冯巍,冯辉,等. 一种压缩采样中的稀疏度自适应子空间追踪算法[J]. 电子学报. 2010, 38(08): 1914-1917.

[8] 周灿梅. 基于压缩感知的信号重建算法研究[D]. 北京: 北京交通大学, 2010.

[9] 西安邮电大学．一种BBU-RRU之间的数据压缩方法: 中国, 201510791927.6[P].2016-04-20.

王军选, 李小平. 压缩感知实现BBU-RRU之间的数据压缩[J]. 光通信研究, 2016, 42(5): 71. WANG Jun-xuan, LI Xiao-ping. Data Compression Technology Between BBU and RRU with Compressed Sensing[J]. Study On Optical Communications, 2016, 42(5): 71.

压缩感知实现BBU-RRU之间的数据压缩

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