# SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks

arXiv: https://arxiv.org/abs/2402.17773 · HTML: https://mlsanity.com/paper/2402.17773/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 21 of its 55 references that are in the map.
- First posted on arXiv 17 Feb 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 21 of 21)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [StarCraft II: A New Challenge for Reinforcement Learning](https://mlsanity.com/paper/1708.04782/) (2017)
- [Mastering Complex Control in MOBA Games with Deep Reinforcement Learning](https://mlsanity.com/paper/1912.09729/) (2019)
- [Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks](https://mlsanity.com/paper/1802.06958/) (2018)
- [On Regret-Optimal Learning in Decentralized Multi-player Multi-armed Bandits](https://mlsanity.com/paper/1505.00553/) (2015)
- [Learning in Restless Multi-Armed Bandits via Adaptive Arm Sequencing Rules](https://mlsanity.com/paper/1906.08120/) (2019)
- [Distributed Learning over Markovian Fading Channels for Stable Spectrum Access](https://mlsanity.com/paper/2101.11292/) (2021)
- [Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach](https://mlsanity.com/paper/1810.11758/) (2018)
- [Learning in Restless Bandits under Exogenous Global Markov Process](https://mlsanity.com/paper/2112.09484/) (2021)
- [Multi-agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworks](https://mlsanity.com/paper/2205.05036/) (2022)
- [Distributed Learning Algorithms for Spectrum Sharing in Spatial Random Access Wireless Networks](https://mlsanity.com/paper/1507.05664/) (2015)
- [Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks](https://mlsanity.com/paper/2110.14541/) (2021)
- [Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach](https://mlsanity.com/paper/2303.15544/) (2023)
- [Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access](https://mlsanity.com/paper/1704.02613/) (2017)
- [Distributed Game Theoretic Optimization and Management of Multichannel ALOHA Networks](https://mlsanity.com/paper/1312.7513/) (2013)
- [Decentralized Learning for Channel Allocation in IoT Networks over Unlicensed Bandwidth as a Contextual Multi-player Multi-armed Bandit Game](https://mlsanity.com/paper/2003.13314/) (2020)

## Built on by (most cited first, 5 of 5)

- [DeepOFW: Deep Learning-Driven OFDM-Flexible Waveform Modulation for Peak-to-Average Power Ratio Reduction](https://mlsanity.com/paper/2603.23544/) (2026)
- [Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms](https://mlsanity.com/paper/2501.06454/) (2025)
- [Control-Optimized Deep Reinforcement Learning for Artificially Intelligent Autonomous Systems](https://mlsanity.com/paper/2507.00268/) (2025)
- [Distributed Learning in Markovian Restless Bandits over Interference Graphs for Stable Spectrum Sharing](https://mlsanity.com/paper/2512.17161/) (2025)
- [A Safety-Constrained Reinforcement Learning Framework for Reliable Wireless Autonomy](https://mlsanity.com/paper/2602.13207/) (2026)

## Abstract (arXiv)

We consider the problem of dynamic channel allocation (DCA) in cognitive communication networks with the goal of maximizing a global signal-to-interference-plus-noise ratio (SINR) measure under a specified target quality of service (QoS)-SINR for each network. The shared bandwidth is partitioned into K channels with frequency separation. In contrast to the majority of existing studies that assume perfect orthogonality or a one- to-one user-channel allocation mapping, this paper focuses on real-world systems experiencing inter-carrier interference (ICI) and channel reuse by multiple large-scale networks. This realistic scenario significantly increases the problem dimension, rendering existing algorithms inefficient. We propose a novel multi-agent reinforcement learning (RL) framework for distributed DCA, named Channel Allocation RL To Overlapped Networks (CARLTON). The CARLTON framework is based on the Centralized Training with Decentralized Execution (CTDE) paradigm, utilizing the DeepMellow value-based RL algorithm. To ensure robust performance in the interference-laden environment we address, CARLTON employs a low-dimensional representation of observations, generating a QoS-type measure while maximizing a global SINR measure and ensuring the target QoS-SINR for each network. Our results demonstrate exceptional performance and robust generalization, showcasing superior efficiency compared to alternative state-of-the-art methods, while achieving a marginally diminished performance relative to a fully centralized approach.