Sparse direct adaptive equalization based on proportionate recursive least squares algorithm for multiple-input multiple-output underwater acoustic communications
In this paper, the sparse direct adaptive equalization based on the recently developed proportionate recursive least squares (PRLS) adaptive filtering algorithm is investigated for multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications. First, performance analysis is made for...
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| Veröffentlicht in: | The Journal of the Acoustical Society of America Jg. 148; H. 4; S. 2280 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.10.2020
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| ISSN: | 1520-8524, 1520-8524 |
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| Abstract | In this paper, the sparse direct adaptive equalization based on the recently developed proportionate recursive least squares (PRLS) adaptive filtering algorithm is investigated for multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications. First, performance analysis is made for the PRLS, and simulation results show its gain over a standard recursive least squares algorithm under sparse systems. The fast implementation of the PRLS, named the proportionate stable fast transversal filters (PSFTF), is revisited to implement a direct adaptive decision-feedback equalizer which outperforms the existing PSFTF direct adaptive linear equalizer. The PSFTF direct adaptive equalizers (DAEs) are then compared with the selective zero-attracting stable fast transversal filter DAEs (SZA-SFTF-DAEs) enabled by the SZA-SFTF adaptive filtering algorithm. The SZA-SFTF algorithm is designed with the zero-attracting sparsity-promoting principle, which is in parallel to the proportionate updating principle used to design the PSFTF algorithm. Experimental results of an at-sea MIMO UWA communication trial show that PSFTF-DAEs outperform the SZA-SFTF-DAEs.In this paper, the sparse direct adaptive equalization based on the recently developed proportionate recursive least squares (PRLS) adaptive filtering algorithm is investigated for multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications. First, performance analysis is made for the PRLS, and simulation results show its gain over a standard recursive least squares algorithm under sparse systems. The fast implementation of the PRLS, named the proportionate stable fast transversal filters (PSFTF), is revisited to implement a direct adaptive decision-feedback equalizer which outperforms the existing PSFTF direct adaptive linear equalizer. The PSFTF direct adaptive equalizers (DAEs) are then compared with the selective zero-attracting stable fast transversal filter DAEs (SZA-SFTF-DAEs) enabled by the SZA-SFTF adaptive filtering algorithm. The SZA-SFTF algorithm is designed with the zero-attracting sparsity-promoting principle, which is in parallel to the proportionate updating principle used to design the PSFTF algorithm. Experimental results of an at-sea MIMO UWA communication trial show that PSFTF-DAEs outperform the SZA-SFTF-DAEs. |
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| AbstractList | In this paper, the sparse direct adaptive equalization based on the recently developed proportionate recursive least squares (PRLS) adaptive filtering algorithm is investigated for multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications. First, performance analysis is made for the PRLS, and simulation results show its gain over a standard recursive least squares algorithm under sparse systems. The fast implementation of the PRLS, named the proportionate stable fast transversal filters (PSFTF), is revisited to implement a direct adaptive decision-feedback equalizer which outperforms the existing PSFTF direct adaptive linear equalizer. The PSFTF direct adaptive equalizers (DAEs) are then compared with the selective zero-attracting stable fast transversal filter DAEs (SZA-SFTF-DAEs) enabled by the SZA-SFTF adaptive filtering algorithm. The SZA-SFTF algorithm is designed with the zero-attracting sparsity-promoting principle, which is in parallel to the proportionate updating principle used to design the PSFTF algorithm. Experimental results of an at-sea MIMO UWA communication trial show that PSFTF-DAEs outperform the SZA-SFTF-DAEs.In this paper, the sparse direct adaptive equalization based on the recently developed proportionate recursive least squares (PRLS) adaptive filtering algorithm is investigated for multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications. First, performance analysis is made for the PRLS, and simulation results show its gain over a standard recursive least squares algorithm under sparse systems. The fast implementation of the PRLS, named the proportionate stable fast transversal filters (PSFTF), is revisited to implement a direct adaptive decision-feedback equalizer which outperforms the existing PSFTF direct adaptive linear equalizer. The PSFTF direct adaptive equalizers (DAEs) are then compared with the selective zero-attracting stable fast transversal filter DAEs (SZA-SFTF-DAEs) enabled by the SZA-SFTF adaptive filtering algorithm. The SZA-SFTF algorithm is designed with the zero-attracting sparsity-promoting principle, which is in parallel to the proportionate updating principle used to design the PSFTF algorithm. Experimental results of an at-sea MIMO UWA communication trial show that PSFTF-DAEs outperform the SZA-SFTF-DAEs. |
| Author | Han, Xiao Tao, Jun Qin, Zhen |
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