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Learning Non-myopic Power Allocation in Constrained Scenarios

arXiv 2401.10297 ↗First posted 18 Jan 2024eess.SP cs.LG cs.NIcited by 0

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Abstract (arXiv)

We propose a learning-based framework for efficient power allocation in ad hoc interference networks under episodic constraints. The problem of optimal power allocation -- for maximizing a given network utility metric -- under instantaneous constraints has recently gained significant popularity. Several learnable algorithms have been proposed to obtain fast, effective, and near-optimal performance. However, a more realistic scenario arises when the utility metric has to be optimized for an entire episode under time-coupled constraints. In this case, the instantaneous power needs to be regulated so that the given utility can be optimized over an entire sequence of wireless network realizations while satisfying the constraint at all times. Solving each instance independently will be myopic as the long-term constraint cannot modulate such a solution. Instead, we frame this as a constrained and sequential decision-making problem, and employ an actor-critic algorithm to obtain the constraint-aware power allocation at each step. We present experimental analyses to illustrate the effectiveness of our method in terms of superior episodic network-utility performance and its efficiency in terms of time and computational complexity.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 23 references that are in the map. First posted on arXiv 18 Jan 2024.

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References · 15

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  3. 2019Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processingcited by 421
  4. 2019Constrained Reinforcement Learning Has Zero Duality Gapcited by 151
  5. 2018Convolutional Neural Network Architectures for Signals Supported on Graphscited by 95
  6. 2019Safe Policies for Reinforcement Learning via Primal-Dual Methodscited by 79
  7. 2020Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocationcited by 61
  8. 2020Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systemscited by 44
  9. 2018Learning Optimal Resource Allocations in Wireless Systemscited by 40
  10. 2019A Graph Neural Network Approach for Scalable Wireless Power Controlcited by 26
  11. 2019Deep Learning based Wireless Resource Allocation with Application to Vehicular Networkscited by 20
  12. 2022Coordinated Sum-Rate Maximization in Multicell MU-MIMO with Deep Unrollingcited by 8
3 more
  1. 2023Deep Graph Unfolding for Beamforming in MU-MIMO Interference Networkscited by 6
  2. 2022Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networkscited by 5
  3. 2021Stability Analysis of Unfolded WMMSE for Power Allocationcited by 2

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  6. 2025Joint User Priority and Power Scheduling for QoS-Aware WMMSE Precoding: A Constrained-Actor Attentive-Critic Approach
  7. 2026A Graph Foundation Model for Wireless Resource Allocation
  8. 2024PAMLR: A Passive-Active Multi-Armed Bandit-Based Solution for LoRa Channel Allocation