# Augmenting Max-Weight with Explicit Learning for Wireless Scheduling with Switching Costs

arXiv: https://arxiv.org/abs/1808.01618 · HTML: https://mlsanity.com/paper/1808.01618/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2018.

## Built on by (most cited first, 9 of 9)

- [Learning and Information in Stochastic Networks and Queues](https://mlsanity.com/paper/2105.08769/) (2021)
- [Learning While Scheduling in Multi-Server Systems with Unknown Statistics: MaxWeight with Discounted UCB](https://mlsanity.com/paper/2209.01126/) (2022)
- [GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environments](https://mlsanity.com/paper/2112.08916/) (2021)
- [Learning-based Optimal Admission Control in a Single Server Queuing System](https://mlsanity.com/paper/2212.11316/) (2022)
- [Queue Scheduling with Adversarial Bandit Learning](https://mlsanity.com/paper/2303.01745/) (2023)
- [Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space](https://mlsanity.com/paper/2306.02574/) (2023)
- [Deep Index Policy for Multi-Resource Restless Matching Bandit and Its Application in Multi-Channel Scheduling](https://mlsanity.com/paper/2408.07205/) (2024)
- [Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop Networks](https://mlsanity.com/paper/2408.16215/) (2024)
- [Smoothed Online Combinatorial Optimization Using Imperfect Predictions](https://mlsanity.com/paper/2204.10979/) (2022)

## Abstract (arXiv)

In small-cell wireless networks where users are connected to multiple base stations (BSs), it is often advantageous to switch off dynamically a subset of BSs to minimize energy costs. We consider two types of energy cost: (i) the cost of maintaining a BS in the active state, and (ii) the cost of switching a BS from the active state to inactive state. The problem is to operate the network at the lowest possible energy cost (sum of activation and switching costs) subject to queue stability. In this setting, the traditional approach -- a Max-Weight algorithm along with a Lyapunov-based stability argument -- does not suffice to show queue stability, essentially due to the temporal co-evolution between channel scheduling and the BS activation decisions induced by the switching cost. Instead, we develop a learning and BS activation algorithm with slow temporal dynamics, and a Max-Weight based channel scheduler that has fast temporal dynamics. We show using convergence of time-inhomogeneous Markov chains, that the co-evolving dynamics of learning, BS activation and queue lengths lead to near optimal average energy costs along with queue stability.