# Distributed Learning Algorithms for Spectrum Sharing in Spatial Random Access Wireless Networks

arXiv: https://arxiv.org/abs/1507.05664 · HTML: https://mlsanity.com/paper/1507.05664/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2015.

## Built on by (most cited first, 8 of 8)

- [Improving the Transient Times for Distributed Stochastic Gradient Methods](https://mlsanity.com/paper/2105.04851/) (2021)
- [Compressed Gradient Tracking for Decentralized Optimization Over General Directed Networks](https://mlsanity.com/paper/2106.07243/) (2021)
- [Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach](https://mlsanity.com/paper/2303.15544/) (2023)
- [SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks](https://mlsanity.com/paper/2402.17773/) (2024)
- [Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing](https://mlsanity.com/paper/2309.08477/) (2023)
- [Provably Accelerated Decentralized Gradient Method Over Unbalanced Directed Graphs](https://mlsanity.com/paper/2107.12065/) (2021)
- [Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex functions](https://mlsanity.com/paper/2403.11565/) (2024)
- [Distributed Learning in Markovian Restless Bandits over Interference Graphs for Stable Spectrum Sharing](https://mlsanity.com/paper/2512.17161/) (2025)

## Abstract (arXiv)

We consider distributed optimization over orthogonal collision channels in spatial random access networks. Users are spatially distributed and each user is in the interference range of a few other users. Each user is allowed to transmit over a subset of the shared channels with a certain attempt probability. We study both the non-cooperative and cooperative settings. In the former, the goal of each user is to maximize its own rate irrespective of the utilities of other users. In the latter, the goal is to achieve proportionally fair rates among users. Simple distributed learning algorithms are developed to solve these problems. The efficiencies of the proposed algorithms are demonstrated via both theoretical analysis and simulation results.