# Spectrum Bandit Optimization

arXiv: https://arxiv.org/abs/1302.6974 · HTML: https://mlsanity.com/paper/1302.6974/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2013.

## Built on by (most cited first, 3 of 3)

- [Near-Optimal Reinforcement Learning with Shuffle Differential Privacy](https://mlsanity.com/paper/2411.11647/) (2024)
- [Exploiting Side Information for Improved Online Learning Algorithms in Wireless Networks](https://mlsanity.com/paper/2202.11699/) (2022)
- [Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex Networks](https://mlsanity.com/paper/2303.05445/) (2023)

## Abstract (arXiv)

We consider the problem of allocating radio channels to links in a wireless network. Links interact through interference, modelled as a conflict graph (i.e., two interfering links cannot be simultaneously active on the same channel). We aim at identifying the channel allocation maximizing the total network throughput over a finite time horizon. Should we know the average radio conditions on each channel and on each link, an optimal allocation would be obtained by solving an Integer Linear Program (ILP). When radio conditions are unknown a priori, we look for a sequential channel allocation policy that converges to the optimal allocation while minimizing on the way the throughput loss or it regret due to the need for exploring sub-optimal allocations. We formulate this problem as a generic linear bandit problem, and analyze it first in a stochastic setting where radio conditions are driven by a stationary stochastic process, and then in an adversarial setting where radio conditions can evolve arbitrarily. We provide new algorithms in both settings and derive upper bounds on their regrets.