Dynamic Channel, Rate Selection and Scheduling for White Spaces

We investigate dynamic channel, rate selection and scheduling for wireless systems which exploit the large number of channels available in the White-space spectrum. We first present measurements of radio channel characteristics from an indoor testbed operating in the 500 to 600MHz band and comprising 11 channels. We observe significant and unpredictable (non-stationary) variations in the quality of these channels, and demonstrate the potential benefit in throughput from tracking the best channel and also from optimally adapting the transmission rate. We propose adaptive learning schemes able to efficiently track the best channel and rate for transmission, even in scenarios with non-stationary channel condition variations. We also describe a joint scheduling scheme for providing fairness in an Access Point scenario. Finally, we implement the proposed adaptive scheme in our testbed, and demonstrate that it achieves significant throughput improvement (typically from 40% to 100%) compared to traditional fixed channel selection schemes.

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