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Title: Impulsive noise mitigation for OFDM based power line communication systems
Author: Mehboob, Anser
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2013
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There has been significant progress in the area of Power Line Communications (PLC) in the recent few years primarily focused on in-house PLC systems. Since the cables are already laid down in the house, PLC offers quite attractive option for in-house automation, broadband communication and high definition video transmission. However, since the power lines have not been designed for data communications, the environment is considerably unfavorable for communications due to the multipath effects from the joints in the cables and different types of noise impurities especially asynchronous impulsive noise. The asynchronous impulsive noise in power lines varies in its density over the time which makes it formidable to estimate and remove. This thesis documents the premises of Orthogonal Frequency Division Multiplexing (OFDM) based PLC systems in regards to the impulsive noise mitigation. For this purpose, an adaptive impulsive noise mitigation scheme has been developed to accurately estimate the 'varying-density' impulsive noise using adaptive pilots assignment and Compressed Sensing thereby coining the term Multi Mode Compressed Sensing (MMCS). The MMCS scheme has further been improved by proposing more accurate density estimation algorithm called Modified Gini Index. The MMCS scheme has been shown to improve the Bit Error Rate (BER) performance and overall throughput of the system as compared to fixed pilots CS schemes. In addition to that, the time-domain impulsive noise and channel impulse response reconstruction is cast as a single joint estimation problem using the notion of sparsity for OFDM system wherein the probability of impulsive noise interfering with channel impulse response is shown to be negligibly small. This allows the impulsive noise and channel impulse response supports to be assumed as disjoint. Furthermore, Compressed Sensing (CS) based algorithm is shown - with the aid of Signal to Noise Ratio (SNR) and Mean Square Error (MSE) analysis - to achieve better performance in the joint setting as compared to the separate estimations of impulsive noise and channel impulse response. Numerical simulations confirm the improvements in terms of Mean Square Error (MSE), Bit Error Rate (BER) or spectral efficiency offered by the proposed (joint) scheme.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available