Finite Precision Digital Signal Processing for Space Division Multiplexing

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/255732
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Type: Examensarbete för masterexamen
Master Thesis
Title: Finite Precision Digital Signal Processing for Space Division Multiplexing
Authors: Jin, Yingshi
Abstract: Optical fibers are widely used for data transmission in modern data networks, but the development of fiber systems is approaching some fundamental limits in terms of data capacity. Space division multiplexing (SDM) can help to overcome some of these fundamental limits. The most scalable SDM approach is so-called modedivision multiplexing (MDM) in multi-mode fiber (MMF). In MDM, each mode in principle will propagate independently and can be easily separated at the receiver. However in a real system, small perturbations of the fiber, such as manufacturing imperfections, bends or vibrations will cause mixing between the modes. Therefore at the receiver, an adaptive equalizer is needed to separate the orthogonal states and recover the signals. So far, most demonstrations have relied heavily on offline digital signal processing using floating point representation. To achieve the required high data rates, real systems would need to employ purpose designed application-specific integrated circuits (ASICs) with finite precision. The goal of this project is to investigate how limited fixed point precision affects the performance of constant modulus algorithm (CMA) equalizer for SDM data transmission. In this report, we will introduce how to design and implement adaptive CMA equalizers, perform system simulations initially using floating point and then reducing its precision to fixed point to evaluate performance penalty. Evaluations will be done with 2x2 mode with two polarizations and 4x4 mode with four polarizations.
Keywords: Informations- och kommunikationsteknik;Data- och informationsvetenskap;Information & Communication Technology;Computer and Information Science
Issue Date: 2018
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/255732
Collection:Examensarbeten för masterexamen // Master Theses



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