中国邮电高校学报(英文) ›› 2012, Vol. 19 ›› Issue (1): 62-68.doi: 10.1016/S1005-8885(11)60229-X

• Networks • 上一篇    下一篇

Compressed sensing based deconvolution algorithm for time-domain UWB channel modeling

李德建1,李斌1,周正2,翟世俊1   

  1. 1. 北京邮电大学
    2. 北京邮电大学电信工程学院
  • 收稿日期:2011-04-07 修回日期:2011-09-26 出版日期:2012-02-28 发布日期:2012-02-21
  • 通讯作者: 李德建 E-mail:lidejian09@gmail.com
  • 基金资助:

    This work was supported by Important National Science & Technology Specific Projects (2009ZX03006-009), the MIKE (The Ministry of Knowledge Economy), Korea, under the ITRC(Information Technology Research Center) support program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2011-C1090-1111-0007), the BUPT excellent Ph.D. students foundations (CX201122).

Compressed sensing based deconvolution algorithm for time-domain UWB channel modeling

  1. Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2011-04-07 Revised:2011-09-26 Online:2012-02-28 Published:2012-02-21
  • Supported by:

    This work was supported by Important National Science & Technology Specific Projects (2009ZX03006-009), the MIKE (The Ministry of Knowledge Economy), Korea, under the ITRC(Information Technology Research Center) support program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2011-C1090-1111-0007), the BUPT excellent Ph.D. students foundations (CX201122).

摘要:

Extracting the parameters of the multipath with high accuracy can be achieved by using high-resolution algorithm for time-domain ultra wideband (UWB) channel modeling. The CLEAN algorithm has been used as such a high-resolution algorithm for UWB time-domain characterization. This paper presents a compressed sensing (CS) based high-resolution deconvolution algorithm for time-domain UWB channel modeling. UWB wireless channels are a prime example of long and sparse channel impulse response (CIR). Furthermore, the dictionary of parameterized waveforms that closely matches the waveform of multipath leads to that the UWB channel measurement signal is more compactly represented. By adjusting the parameter of dictionary, CIRs of different resolutions can be obtained. The matching pursuit (MP) algorithm is used as the signal reconstruction method for CS and outputs the CIR directly. We also demonstrated that if the dictionary of CS is designed specifically, MP is an equivalent of single template CLEAN. Finally, the computation complexity of CS-MP is analyzed and comparison of MP and CLEAN is performed. Simulation results show that compared to CLEAN, the proposed CS-MP deconvolution algorithm can achieve a comparable performance with much fewer samplings.

关键词:

compressed sensing, deconvolution, channel modeling, matching pursuit, UWB

Abstract:

Extracting the parameters of the multipath with high accuracy can be achieved by using high-resolution algorithm for time-domain ultra wideband (UWB) channel modeling. The CLEAN algorithm has been used as such a high-resolution algorithm for UWB time-domain characterization. This paper presents a compressed sensing (CS) based high-resolution deconvolution algorithm for time-domain UWB channel modeling. UWB wireless channels are a prime example of long and sparse channel impulse response (CIR). Furthermore, the dictionary of parameterized waveforms that closely matches the waveform of multipath leads to that the UWB channel measurement signal is more compactly represented. By adjusting the parameter of dictionary, CIRs of different resolutions can be obtained. The matching pursuit (MP) algorithm is used as the signal reconstruction method for CS and outputs the CIR directly. We also demonstrated that if the dictionary of CS is designed specifically, MP is an equivalent of single template CLEAN. Finally, the computation complexity of CS-MP is analyzed and comparison of MP and CLEAN is performed. Simulation results show that compared to CLEAN, the proposed CS-MP deconvolution algorithm can achieve a comparable performance with much fewer samplings.

Key words:

compressed sensing, deconvolution, channel modeling, matching pursuit, UWB