This thesis comprises of two parts. In the first, we improve the performance of existing hybrid FSO/RF communication systems. Conventional hybrid RF and optical wireless communication systems make use of independent and parallel Free Space Optical (FSO) and RF channels to achieve higher reliability than individual channels. This thesis is based on the idea that true hybridization can be accomplished only when both channels collaboratively compensate the shortcomings of each other and thereby, improve the performance of the system as a whole. We believe that optimization on the combined channel capacities instead of the individual channel capacities of the FSO and RF channels can increase the system availability by a large amount. Using analysis and simulation, we show that, by using Hybrid Channel Codes, we can obtain more than two orders of magnitude improvement in bit error rates and many-fold increase in system availability over the currently existing best systems. Simulations also show that the average throughput obtained using the new system is over 35% better when compared to the present systems. The goodput is much higher because of the elimination of data repetition. Also by avoiding data duplication, we preserve to a great extent the crucial security benefits of FSO communications.
The second half of the thesis deals with magnetic recording systems. Due to the insatiable and ever-increasing needs of data storage, novel techniques have to be developed to improve the capacity of magnetic recording channels. These capacity requirements translate to improving storage densities and using higher recording rates. For these channels, improvements even in the order of a tenths of a dB have a big impact on the storage densities of the recording device. Recently, LDPC codes have been constructed to achieve the independent and uniformly distributed (i.u.d.) capacity of partial response (PR) channels. The “guess algorithm” has been proposed for memoryless channels, to improve the performance of iterative belief propagation decoding to that of Maximum Likelihood (ML) decoding. In the second part of this thesis, the “guess algorithm” is extended to channels with memory. It is shown using asymptotic density evolution analysis that the gains obtained using this algorithm on these channels are more than those obtained over memoryless channels. The “guess algorithm” is further extended to magnetic recording channels which are characterized by ISI and additive white gaussian noise (AWGN). Simulations show that gains of upto one dB are possible on magnetic recording channels.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:theses-1088 |
| Date | 01 January 2007 |
| Creators | Vangala, Sarma V |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses 1911 - February 2014 |
Page generated in 0.0017 seconds