mlsanityData to 7 Oct 2026

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

arXiv 2402.17773 ↗First posted 17 Feb 2024eess.SP cs.LGcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 55 references that are in the map. First posted on arXiv 17 Feb 2024.

Builds on

21 of 55 references.

References · 21

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9 more
  1. 2018Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approachcited by 10
  2. 2021Learning in Restless Bandits under Exogenous Global Markov Processcited by 9
  3. 2015Distributed Learning Algorithms for Spectrum Sharing in Spatial Random Access Wireless Networkscited by 8
  4. 2021Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networkscited by 8
  5. 2022Multi-agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworkscited by 8
  6. 2023Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approachcited by 6
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2026DeepOFW: Deep Learning-Driven OFDM-Flexible Waveform Modulation for Peak-to-Average Power Ratio Reductioncited by 1
  2. 2025Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarmscited by 0
  3. 2025Control-Optimized Deep Reinforcement Learning for Artificially Intelligent Autonomous Systemscited by 0
  4. 2025Distributed Learning in Markovian Restless Bandits over Interference Graphs for Stable Spectrum Sharingcited by 0
  5. 2026A Safety-Constrained Reinforcement Learning Framework for Reliable Wireless Autonomycited by 0