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Decomposition of optical MIMO systems using polynomial matrix factorization

Ahrens, Andreas, Sandmann, Andre, Lochmann, Steffen and Wang, Zeliang 2015. Decomposition of optical MIMO systems using polynomial matrix factorization. Presented at: 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP), London, UK, 1-2 December 2015. Proceedings of the 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP). IET, pp. 1-6. 10.1049/cp.2015.1758

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Abstract

Within the last years the multiple-input multiple-output (MIMO) technology has revolutionized the optical fiber community. Theoretically, the concept of MIMO is well understood and shows some similarities to wireless MIMO systems. The interference in broadband MIMO systems can be removed by applying a spatio-temporal vector coding (STVC) channel description and using singular value decomposition (SVD) in combination with signal pre- and post-processing. In this contribution a newly developed SVD algorithm for polynomial matrices (PMSVD) is analyzed and compared to the commonly used SVD-based STVC. The PMSVD is implemented by an iterative polynomial matrix eigenvalue decomposition (PEVD) algorithm, namely the second order sequential best rotation algorithm (SBR2). The bit-error rate (BER) performance is evaluated and optimized by applying bit and power allocation schemes. For our simulations, the specific impulse responses of the (2 × 2) MIMO channel, including a 1.4 km multi-mode fiber and optical couplers at both ends, are measured for the operating wavelength of 1576 nm. The computer simulation results show that the PMSVD could be an alternative signal processing approach compared to conventional SVD-based MIMO approaches in frequency-selective MIMO channels.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Engineering
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Publisher: IET
ISBN: 9781785611360
Date of First Compliant Deposit: 18 November 2016
Last Modified: 04 Jun 2017 09:30
URI: http://orca-mwe.cf.ac.uk/id/eprint/96087

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