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Regularized Lattice Sphere Decoding for Block Data Transmission Systems.

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Title: Regularized Lattice Sphere Decoding for Block Data Transmission Systems.
Authors: Albreem, Mahmoud1 mahmoudam@unimap.edu.my, Salleh, Mohd2 fadzlisalleh@eng.usm.my
Source: Wireless Personal Communications. Jun2015, Vol. 82 Issue 3, p1833-1850. 18p.
Subjects: Data transmission systems, Maximum likelihood decoding, Regularization parameter, Intersymbol interference, Additive white Gaussian noise channels, Signal-to-noise ratio
Abstract: This paper presents Lattice Sphere Decoding (LSD) with regularization techniques for block data transmission systems. It has shown that a small condition number ( $$\tau $$ ) results in better detection performance. This paper aims to reduce this value to its smallest possible value. Regularization technique offers reduction in condition number ( $$\tau $$ ) that improves LSD performance. In this work, two regularization techniques are utilized on LSD. First, $$\hbox {L}_{1}$$ -regularization method is introduced, which sums the mixed norms. Second, $$\hbox {L}_{2}$$ - regularization method, which is the most commonly used method of regularization for ill-conditioned problems in mathematics. We derive the exact relationship between the LSD performance and condition number ( $$\tau $$ ) as well as the relationship between LSD initial radius ( d) and condition number ( $$\tau $$ ). The derived equations show their convergence to the fact that the performance increases as the radius ( d) increases. Simulation results show that the LSD with $$\hbox {L}_{1}$$ -regularization technique offers smaller condition number ( $$\tau $$ ), and therefore, produces better system performance. From the performance results and the complexity analysis, it is apparent that the proposed techniques achieve a good balance between complexity and performance. [ABSTRACT FROM AUTHOR]
Copyright of Wireless Personal Communications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: This paper presents Lattice Sphere Decoding (LSD) with regularization techniques for block data transmission systems. It has shown that a small condition number ( $$\tau $$ ) results in better detection performance. This paper aims to reduce this value to its smallest possible value. Regularization technique offers reduction in condition number ( $$\tau $$ ) that improves LSD performance. In this work, two regularization techniques are utilized on LSD. First, $$\hbox {L}_{1}$$ -regularization method is introduced, which sums the mixed norms. Second, $$\hbox {L}_{2}$$ - regularization method, which is the most commonly used method of regularization for ill-conditioned problems in mathematics. We derive the exact relationship between the LSD performance and condition number ( $$\tau $$ ) as well as the relationship between LSD initial radius ( d) and condition number ( $$\tau $$ ). The derived equations show their convergence to the fact that the performance increases as the radius ( d) increases. Simulation results show that the LSD with $$\hbox {L}_{1}$$ -regularization technique offers smaller condition number ( $$\tau $$ ), and therefore, produces better system performance. From the performance results and the complexity analysis, it is apparent that the proposed techniques achieve a good balance between complexity and performance. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Wireless Personal Communications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Regularization parameter
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              Text: Jun2015
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