Exploiting Side Information for Improved Online Learning Algorithms in Wireless Networks
This work addresses the challenge of efficient channel selection in wireless networks, such as cognitive radio and air-to-ground communications, by leveraging measurable side information to enhance throughput, representing an incremental improvement over existing methods.
The paper tackles the problem of improving online learning algorithms in wireless networks by exploiting side information correlated with channel rates, resulting in algorithms that require fewer samples and achieve better regret performance, with gains proportional to the correlation strength.
In wireless networks, the rate achieved depends on factors like level of interference, hardware impairments, and channel gain. Often, instantaneous values of some of these factors can be measured, and they provide useful information about the instantaneous rate achieved. For example, higher interference implies a lower rate. In this work, we treat any such measurable quality that has a non-zero correlation with the rate achieved as side-information and study how it can be exploited to quickly learn the channel that offers higher throughput (reward). When the mean value of the side-information is known, using control variate theory we develop algorithms that require fewer samples to learn the parameters and can improve the learning rate compared to cases where side-information is ignored. Specifically, we incorporate side-information in the classical Upper Confidence Bound (UCB) algorithm and quantify the gain achieved in the regret performance. We show that the gain is proportional to the amount of the correlation between the reward and associated side-information. We discuss in detail various side-information that can be exploited in cognitive radio and air-to-ground communication in $L-$band. We demonstrate that correlation between the reward and side-information is often strong in practice and exploiting it improves the throughput significantly.