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Research On Key Technologies In Multi-Dimensional Multiplexing Transmission Systems

Posted on:2016-09-27Degree:MasterType:Thesis
Country:ChinaCandidate:C R TuFull Text:PDF
GTID:2298330467995081Subject:Communication and Information System
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As the continuous improvement of the degree of the social informatization, the rapid popularity of Internet applications and the increasing multimedia services, the transmission bandwidth demand for communication networks has witnessed an explosive growth, moreover, network traffic is currently approaching the limits of existing transmission technologies. Development of new transmission technology to meet future bandwidth needs of network development has become an urgent task. As the main content of multi-dimensional communication transmission mechanism and networking method, space-division multiplexing has become the remarkable research and hot topics of the forefront of optical fiber communications. Mode-division multiplexing belongs to space-division multiplexing. Using different modes to be the carries of information and using few-mode fiber to be transmission link, mode-division multiplexing transmission systems utilizes multi-input multi-output structure and still has more critical scientific problems to study and solve. For example, the impact of random mode coupling in the few-mode fiber transmission link on transmitted signals and the performance of the system, and the internal mechanism and design of a variety of key equipments such as mode coupler, mode multiplexer/demultiplexer, few-mode optical amplifier and so on. Based on "The National Key Basic Research Development Plan (973Plan)" project "Multi-dimension multiplexing optical fiber communication basic research", this dissertation studies mode-division multiplexing transmission system and its key technologies, and the main content of this research is the MIMO DSP algorithm which is used for compensating mode coupling in the receiver of mode-division multiplexing transmission system.The main research work and innovations of this dissertation are as follows.1. Propose and simulate the general model of mode multiplexing transmission system.By collecting, reading and studying a large number of literatures, comparing and summarizing, the author proposes the general model of mode-division multiplexing transmission systems, which consists of transmitter, mode converter, mode multiplexer, few-mode fiber, mode demultiplexer, coherent receiver, digital signal processing module and so on. The author has deep studied the principles to achieve the above devices in the current. On this basis, the author sets up a4×4mode-division multiplexing transmission system which can transmit four modes simultaneously, and each mode carries a56GBaud quadrature phase shift keying(QPSK) signal.2. Propose multi-input multi-output constant modulus algorithm with an adaptive step size and use it to compensate mode couplingTaking account of the existing multi-input multi-output constant modulus algorithm (MIMO-CMA) and utilizing adaptive digital signal processing, the dissertation proposes to use multi-input multi-output constant modulus algorithm with an adaptive step size to compensate mode coupling under different coupling coefficients. Then the author measured the bit error rate and convergence perfomiance of adaptive step size MIMO-CMA.3. Propose a multimode division demultiplexing algorithm based on artificial neural network and use it to compensate mode coupling.The dissertation proposes to apply the artificial neural network (Hopfield neural network) in Multi-mode Division Demultiplexing to compensate for mode coupling at the coherent receiver of mode division multiplexing transmission system for the first time. With artificial neural network based mode division demultiplexing algorithm, the author realized to improve the perfomance of the bit error rate of the four modes received signals at different levels in different coupling strength and lessen the dependence of data length of demultiplexing system, compared with MIMO-CMA.
Keywords/Search Tags:Multi-Mode Division Multiplexing, BlindEqualization Algorithm, Adaptive StepSize, Multi-Input Multi-Output Constant Algorithm, Neural Network based Demultiplexing Algorithm
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