Variable Is Better Than Invariable: Sparse VSS-NLMS Algorithms with Application to Adaptive MIMO Channel Estimation
Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized leas...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/274897 |
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| author | Guan Gui Zhang-xin Chen Li Xu Qun Wan Jiyan Huang Fumiyuki Adachi |
| author_facet | Guan Gui Zhang-xin Chen Li Xu Qun Wan Jiyan Huang Fumiyuki Adachi |
| author_sort | Guan Gui |
| collection | DOAJ |
| description | Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics. |
| format | Article |
| id | doaj-art-74307fd314d0400aa808d6826dd73673 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-74307fd314d0400aa808d6826dd736732025-02-03T01:20:56ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/274897274897Variable Is Better Than Invariable: Sparse VSS-NLMS Algorithms with Application to Adaptive MIMO Channel EstimationGuan Gui0Zhang-xin Chen1Li Xu2Qun Wan3Jiyan Huang4Fumiyuki Adachi5Department of Electronics and Information Systems, Akita Prefectural University, Akita 015-0055, JapanDepartment of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaDepartment of Electronics and Information Systems, Akita Prefectural University, Akita 015-0055, JapanDepartment of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaDepartment of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaDepartment of Communications Engineering, Tohoku University, Sendai 980-8579, JapanChannel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics.http://dx.doi.org/10.1155/2014/274897 |
| spellingShingle | Guan Gui Zhang-xin Chen Li Xu Qun Wan Jiyan Huang Fumiyuki Adachi Variable Is Better Than Invariable: Sparse VSS-NLMS Algorithms with Application to Adaptive MIMO Channel Estimation The Scientific World Journal |
| title | Variable Is Better Than Invariable: Sparse VSS-NLMS Algorithms with Application to Adaptive MIMO Channel Estimation |
| title_full | Variable Is Better Than Invariable: Sparse VSS-NLMS Algorithms with Application to Adaptive MIMO Channel Estimation |
| title_fullStr | Variable Is Better Than Invariable: Sparse VSS-NLMS Algorithms with Application to Adaptive MIMO Channel Estimation |
| title_full_unstemmed | Variable Is Better Than Invariable: Sparse VSS-NLMS Algorithms with Application to Adaptive MIMO Channel Estimation |
| title_short | Variable Is Better Than Invariable: Sparse VSS-NLMS Algorithms with Application to Adaptive MIMO Channel Estimation |
| title_sort | variable is better than invariable sparse vss nlms algorithms with application to adaptive mimo channel estimation |
| url | http://dx.doi.org/10.1155/2014/274897 |
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