Improving link-level performance and synchronization in wireless networks
Abstract
The number and type of applications that rely on wireless networks has grown rapidly in the last decade. The widely varying characteristics of these applications pose many new challenges and opportunities in the design of these networks. They range from physical-layer issues, such as maximizing the throughput of wireless channels and networks, to systems-level issues, such as how a wide variety of networks should cooperate to share the radio spectrum. We address several of these problems in this thesis. We first investigate link-quality estimation in sensor networks and related low-data-rate communication systems. Communication among wireless sensor nodes that employ cheap, low-power transceivers is often very sensitive to the variations of the wireless channel. Sensor network routing protocols thus strive to continually adapt to temporal variations in wireless links in order to avoid wasteful transmissions over low-quality links. We propose a scheme that uses a pre-calibrated signal-to-noise ratio (SNR) vs. packet success rate (PSR) relationship and instantaneous SNR estimates to calculate the PSR of the link. In our scheme, each receiver continuously tracks the SNR using a Kalman Filter to minimize the estimation error and uses a locally available SNR-PSR curve to estimate the PSR. Through experiments, we demonstrate the advantages of our scheme and compare its performance with current approaches. We also investigate low-complexity, energy-efficient techniques for synchronizing clustered sensor networks and MIMO techniques for improving link throughputs. We then consider cognitive wireless networks. Next generation "cognitive" wireless devices and networks are expected to be sufficiently smart to quickly find and appropriately share available RF spectrum. This will require wireless devices that can adapt their transceivers to optimally use any spectrum found to be available. It will also require that networks composed of these devices be able to rapidly configure themselves to support their own communication needs while avoiding or coexisting with similar networks. We present two threshold-based algorithms for the distributed estimation of incumbents’ characteristics in cognitive wireless networks, investigate their asymptotic performance, and derive Cramér-Rao Bounds for our estimators. Unlike current practice, we consider communication noise in the system. Our results can be applied to an IEEE802.11b/g wireless network that must coexist with other 802.11b/g networks and other devices in the 2.4GHz ISM band.
Degree
Ph.D.
Advisors
Krogmeier, Purdue University.
Subject Area
Electrical engineering
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