In this thesis, CMs of linear and non-linear multiple antenna receivers, in particular linear minimum mean squared error (LMMSE) and LMMSE with decision feedback (LMMSE-DF), are developed. To develop these CMs, first a simple analytical expression of the distribution of the post processing signal to interference and noise (SINR) of an LMMSE receiver is developed. This expression is then used to develop SINR- and ABER-based CMs. However, the analytical forms of these CMs are derived only for the following scenarios: (i) any number of receive antennas with three users having arbitrary received powers and (ii) two antenna receiver with arbitrary number of equal received power users. For all the other scenarios a semi-analytical CM is used.
The PHY abstractions or CMs are next used in the evaluation of a random access cellular network and an ad hoc network. Analytical model of the random access cellular network is developed using the SINR- and ABER-based CM of the LMMSE receiver. The impact of receiver processing is measured in terms of throughput. In this case, the random access mechanism is modeled by a single channel S-Aloha channel access scheme. Another analytical model is developed for single and multi-packet reception in a multi-channel S-Aloha channel access. An emph{ideal} receiver is modeled in this case, i.e. the packet(s) are successfully received as long as the total number of colliding packets is not greater than the number of antennas. Throughput and delay are used as performance metrics to study the impact of different PHY designs.
Finally, the SINR-based semi-analytical CMs of LMMSE and LMMSE-DF are used to evaluate the performance of multi-hop ad hoc networks. Throughput is used as the performance evaluation metric. A novel MAC, called S-MAC, is proposed and its performance is compared against another MAC for wireless networks, called CSMA/CA(k).
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/22681 |
| Date | 15 January 2008 |
| Creators | Shekhar, Hemabh |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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