mlsanityData to 7 Oct 2026

A Deep Learning Based Resource Allocator for Communication Networks with Dynamic User Utility Demands

arXiv 2311.04600 ↗First posted 8 Nov 2023eess.SP cs.LG cs.NIcited by 0

Not on the map. Why?

Abstract (arXiv)

Deep learning (DL) based resource allocation (RA) has recently gained significant attention due to its performance efficiency. However, most related studies assume an ideal case where the number of users and their utility demands, e.g., data rate constraints, are fixed, and the designed DL-based RA scheme exploits a policy trained only for these fixed parameters. Consequently, computationally complex policy retraining is required whenever these parameters change. In this paper, we introduce a DL-based resource allocator (ALCOR) that allows users to adjust their utility demands freely, such as based on their application layer requirements. ALCOR employs deep neural networks (DNNs) as the policy in a time-sharing problem. The underlying optimization algorithm iteratively optimizes the on-off status of users to satisfy their utility demands in expectation. The policy performs unconstrained RA (URA) -- RA without considering user utility demands -- among active users to maximize the sum utility (SU) at each time instant. Depending on the chosen URA scheme, ALCOR can perform RA in either a centralized or distributed scenario. The derived convergence analyses provide theoretical guarantees for ALCOR's convergence, and numerical experiments corroborate its effectiveness compared to meta-learning and reinforcement learning approaches.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 61 references that are in the map. First posted on arXiv 8 Nov 2023.

Builds on

30 of 61 references.

References · 30

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  3. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  4. 2018Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learningcited by 270
  5. 2016Learning to Optimizecited by 195
  6. 2013Mini-batch Stochastic Approximation Methods for Nonconvex Stochastic Composite Optimizationcited by 142
  7. 2017Learning to Optimize: Training Deep Neural Networks for Wireless Resource Managementcited by 96
  8. 2020Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimizationcited by 94
  9. 2019Optimal Wireless Resource Allocation with Random Edge Graph Neural Networkscited by 90
  10. 2020Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysiscited by 80
  11. 2018Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networkscited by 70
  12. 2020Stochastic optimization with decision-dependent distributionscited by 65
18 more
  1. 2023Advances and Challenges in Meta-Learning: A Technical Reviewcited by 65
  2. 2020Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scalingcited by 49
  3. 2018Learning Optimal Resource Allocations in Wireless Systemscited by 39
  4. 2018Towards Optimal Power Control via Ensembling Deep Neural Networkscited by 38
  5. 2018Spatial Deep Learning for Wireless Schedulingcited by 32
  6. 2021Learning Decentralized Wireless Resource Allocations with Graph Neural Networkscited by 24
  7. 2023Solving stochastic weak Minty variational inequalities without increasing batch sizecited by 21
  8. 2019Deep Learning based Wireless Resource Allocation with Application to Vehicular Networkscited by 19
  9. 2022Learning Resilient Radio Resource Management Policies with Graph Neural Networkscited by 17
  10. 2020Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learningcited by 13
  11. 2022State-Augmented Learnable Algorithms for Resource Management in Wireless Networkscited by 13
  12. 2020Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learningcited by 9
  13. 2020DMRO:A Deep Meta Reinforcement Learning-based Task Offloading Framework for Edge-Cloud Computingcited by 8
  14. 2020Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5Gcited by 7
  15. 2023Uncertainty Injection: A Deep Learning Method for Robust Optimizationcited by 5
  16. 2019Model-Free Learning of Optimal Ergodic Policies in Wireless Systemscited by 4
  17. 2023Meta Federated Reinforcement Learning for Distributed Resource Allocationcited by 4
  18. 2021Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Schedulingcited by 3

Built on by

Not cited yet.