A General Deep Learning Framework for Wireless Resource Allocation under Discrete Constraints
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 fa...
| Authors | Yikun Wang, Yang Li, Yik-Chung Wu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 March 2026 |
| Last updated at source | 25 July 2026 |
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