Regularized Lattice Sphere Decoding for Block Data Transmission Systems.

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Bibliographic Details
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]
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Database:	Engineering Source

Description
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]
ISSN:	09296212
DOI:	10.1007/s11277-015-2317-2