AbstractsComputer Science

In this dissertation, we focus on designing several cross-layer optimizations to boost the performance of wireless networks. We categorize our efforts into two directions: (a) improvement of spatial reusability and multi-hop performance via Directional Antennas, and (b) analysis and optimization of the IEEE 802.11 multi-rate networks. Since existing protocols are incapable of fully exploiting the benefits of a directional antenna system, we propose a, broadcast and routing protocol for such systems. All our proposals assume cross layer interaction between the Network, MAC and PHY layers. Next, we analyze multi-rate networks in the context of IEEE 802.11 DCF networks and propose an analytical model to study the link-delay characteristics of such systems. We then propose an online algorithm Time Fair CSMA (TFCSMA), for guaranteeing air-time fairness and thereby mitigating the recently observed rate anomaly problem of IEEE DCF multi-rate networks. Following the design of TFCSMA, we concentrate on the problem of rate adaptation for time-varying wireless channels. We thoroughly investigate the impact of transmission rate on the performance of a wireless link. We then propose Stochastic Automata Rate Adaptation Algorithm (SARA). SARA is inspired by Stochastic Learning Automata (SLA), a machine learning technique for adaptation in random environments. As opposed to the previous work in this area, SARA is ideally suited for both stationary and non-stationary channel environments and is completely compatible with the existing IEEE 802.11 MAC standard.