# A General Deep Learning Framework for Wireless Resource Allocation under Discrete Constraints

arXiv: https://arxiv.org/abs/2603.19322 · HTML: https://mlsanity.com/paper/2603.19322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 46 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [POMO: Policy Optimization with Multiple Optima for Reinforcement Learning](https://mlsanity.com/paper/2010.16011/) (2020)
- [Soft Actor-Critic for Discrete Action Settings](https://mlsanity.com/paper/1910.07207/) (2019)
- [Towards Optimal Power Control via Ensembling Deep Neural Networks](https://mlsanity.com/paper/1807.10025/) (2018)
- [Scalable Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/1908.03119/) (2019)
- [Holographic MIMO Surfaces for 6G Wireless Networks: Opportunities, Challenges, and Trends](https://mlsanity.com/paper/1911.12296/) (2019)
- [A Survey of Recent Advances in Optimization Methods for Wireless Communications](https://mlsanity.com/paper/2401.12025/) (2024)
- [Heterogeneous Transformer: A Scale Adaptable Neural Network Architecture for Device Activity Detection](https://mlsanity.com/paper/2112.10086/) (2021)
- [Fluid Antenna Systems](https://mlsanity.com/paper/2005.11561/) (2020)
- [Multidimensional Graph Neural Networks for Wireless Communications](https://mlsanity.com/paper/2212.11531/) (2022)
- [Movable Antennas for Wireless Communication: Opportunities and Challenges](https://mlsanity.com/paper/2306.02331/) (2023)
- [Unveiling the Power of Complex-Valued Transformers in Wireless Communications](https://mlsanity.com/paper/2502.11151/) (2025)
- [ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks](https://mlsanity.com/paper/2301.00757/) (2022)
- [Local Partial Zero-Forcing Precoding for Cell-Free Massive MIMO](https://mlsanity.com/paper/1909.01034/) (2019)
- [Movable-Antenna Enhanced Multiuser Communication via Antenna Position Optimization](https://mlsanity.com/paper/2302.06978/) (2023)
- [HPE Transformer: Learning to Optimize Multi-Group Multicast Beamforming Under Nonconvex QoS Constraints](https://mlsanity.com/paper/2402.16081/) (2024)
- [When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach](https://mlsanity.com/paper/2501.14211/) (2025)
- [Co-existing/Cooperating Multicell Massive MIMO and Cell-Free Massive MIMO Deployments: Heuristic Designs and Performance Analysis](https://mlsanity.com/paper/2404.15530/) (2024)
- [Globally Optimal Movable Antenna-Enhanced multiuser Communication: Discrete Antenna Positioning, Motion Power Consumption, and Imperfect CSI](https://mlsanity.com/paper/2408.15435/) (2024)
- [Self-Nomination: Deep Learning for Decentralized CSI Feedback Reduction in MU-MIMO Systems](https://mlsanity.com/paper/2504.16351/) (2025)
- [Intelligent Reflecting Surface Aided Mobile Edge Computing With Binary Offloading: Energy Minimization for IoT Devices](https://mlsanity.com/paper/2205.02194/) (2022)
- [A General Optimization Framework for Tackling Distance Constraints in Movable Antenna-Aided Systems](https://mlsanity.com/paper/2503.02344/) (2025)
- [Learning Wideband User Scheduling and Hybrid Precoding with Graph Neural Networks](https://mlsanity.com/paper/2503.04233/) (2025)
- [Joint Beamforming and Integer User Association using a GNN with Gumbel-Softmax Reparameterizations](https://mlsanity.com/paper/2506.05241/) (2025)

## Abstract (arXiv)

While deep learning (DL)-based methods have achieved remarkable success in continuous wireless resource allocation, efficient solutions for problems involving discrete variables remain challenging. This is primarily due to the zero-gradient issue in backpropagation, the difficulty of enforcing intricate constraints with discrete variables, and the inability in generating solutions with non-same-parameter-same-decision (non-SPSD) property. To address these challenges, this paper proposes a general DL framework by introducing the support set to represent the discrete variables. We model the elements of the support set as random variables and learn their joint probability distribution. By factorizing the joint probability as the product of conditional probabilities, each conditional probability is sequentially learned. This probabilistic modeling directly tackles all the aforementioned challenges of DL for handling discrete variables. By operating on probability distributions instead of hard binary decisions, the framework naturally avoids the zero-gradient issue. During the learning of the conditional probabilities, discrete constraints can be seamlessly enforced by masking out infeasible solutions. Moreover, with a dynamic context embedding that captures the evolving discrete solutions, the non-SPSD property is inherently provided by the proposed framework. We apply the proposed framework to two representative mixed-discrete wireless resource allocation problems: (a) joint user association and beamforming in cell-free systems, and (b) joint antenna positioning and beamforming in movable antenna-aided systems. Simulation results demonstrate that the proposed DL framework consistently outperforms existing baselines in terms of both system performance and computational efficiency.