Block-sparsity-aware LMS algorithm for network echo cancellation
Network echo path impulse response is single-block-sparse in nature. In order to obtain a single-block-sparse estimate of the unknown echo path, a new least mean squares (LMS) algorithm is proposed by introducing the penalty of single block sparsity, which is the difference between the mixed l 2,1 n...
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| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/89501 http://hdl.handle.net/10220/46251 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Network echo path impulse response is single-block-sparse in nature. In order to obtain a single-block-sparse estimate of the unknown echo path, a new least mean squares (LMS) algorithm is proposed by introducing the penalty of single block sparsity, which is the difference between the mixed l 2,1 norm and l 2 norm of the uniformly partitioned filter tap-weight vector, into the original mean-square-error cost function. This is motivated by the fact that the difference between the mixed l 2,1 norm and l 2 norm of a vector is minimised only when there is at most one non-zero block in the vector. Numerical simulation results show that the proposed algorithm can effectively estimate and track the unknown echo path, outperforming existing block-sparsity-induced LMS algorithms. |
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