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In wireless networks, the rate achieved depends on factors like level ofinterference, hardware impairments, and channel gain. Often, instantaneousvalues of some of these factors can be measured, and they provide usefulinformation about the instantaneous rate achieved. For example, higherinterference implies a lower rate. In this work, we treat any such measurablequality that has a non-zero correlation with the rate achieved asside-information and study how it can be exploited to quickly learn the channelthat offers higher throughput (reward). When the mean value of theside-information is known, using control variate theory we develop algorithmsthat require fewer samples to learn the parameters and can improve the learningrate compared to cases where side-information is ignored. Specifically, weincorporate side-information in the classical Upper Confidence Bound (UCB)algorithm and quantify the gain achieved in the regret performance. We showthat the gain is proportional to the amount of the correlation between thereward and associated side-information. We discuss in detail variousside-information that can be exploited in cognitive radio and air-to-groundcommunication in $L-$band. We demonstrate that correlation between the rewardand side-information is often strong in practice and exploiting it improves thethroughput significantly.

Exploiting Side Information for Improved Online Learning Algorithms in Wireless Networks