# Exploiting Side Information for Improved Online Learning Algorithms in Wireless Networks

arXiv: https://arxiv.org/abs/2202.11699 · HTML: https://mlsanity.com/paper/2202.11699/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 65 references that are in the map.
- First posted on arXiv 16 Feb 2022.

## Builds on (most cited first, 12 of 12)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [On Regret-Optimal Learning in Decentralized Multi-player Multi-armed Bandits](https://mlsanity.com/paper/1505.00553/) (2015)
- [Multi-armed Bandits with Application to 5G Small Cells](https://mlsanity.com/paper/1510.00627/) (2015)
- [Fast mmwave Beam Alignment via Correlated Bandit Learning](https://mlsanity.com/paper/1909.03313/) (2019)
- [Multi-Player Bandits: A Trekking Approach](https://mlsanity.com/paper/1809.06040/) (2018)
- [Resource Allocation in NOMA-based Self-Organizing Networks using Stochastic Multi-Armed Bandits](https://mlsanity.com/paper/2101.06340/) (2021)
- [Observe Before Play: Multi-armed Bandit with Pre-observations](https://mlsanity.com/paper/1911.09458/) (2019)
- [Spectrum Bandit Optimization](https://mlsanity.com/paper/1302.6974/) (2013)
- [Multi-Access Communications with Energy Harvesting: A Multi-Armed Bandit Model and the Optimality of the Myopic Policy](https://mlsanity.com/paper/1501.00329/) (2015)
- [Optimal Rate Sampling in 802.11 Systems](https://mlsanity.com/paper/1307.7309/) (2013)
- [Online Learning Schemes for Power Allocation in Energy Harvesting Communications](https://mlsanity.com/paper/1607.02552/) (2016)
- [Learning to Optimize Energy Efficiency in Energy Harvesting Wireless Sensor Networks](https://mlsanity.com/paper/2012.15203/) (2020)

## Abstract (arXiv)

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.