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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.02417 |
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| _version_ | 1866911394772811776 |
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| author | Zhang, Hanwen Sun, Haijian |
| author_facet | Zhang, Hanwen Sun, Haijian |
| contents | The emergence of 6G wireless communication enables massive edge device access and supports real-time intelligent services such as the Internet of things (IoT) and vehicle-to-everything (V2X). However, the surge in edge devices connectivity renders wireless resource allocation (RA) tasks as large-scale constrained optimization problems, whereas the stringent real-time requirement poses significant computational challenge for traditional algorithms. To address the challenge, feasibility-aware learning-to-optimize (L2O) techniques have recently gained attention. These learning-based methods offer efficient alternatives to conventional solvers by directly learning mappings from system parameters to feasible and near-optimal solutions. This article provide a comprehensive review of L2O model designs and feasibility enforcement techniques and investigates the application of constrained L2O in wireless RA systems and. The paper also presents a case study to benchmark different L2O approaches in weighted sum rate problem, and concludes by identifying key challenges and future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02417 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Feasibility-Aware Learning-to-Optimize in Wireless Communication Resource Allocation Zhang, Hanwen Sun, Haijian Information Theory The emergence of 6G wireless communication enables massive edge device access and supports real-time intelligent services such as the Internet of things (IoT) and vehicle-to-everything (V2X). However, the surge in edge devices connectivity renders wireless resource allocation (RA) tasks as large-scale constrained optimization problems, whereas the stringent real-time requirement poses significant computational challenge for traditional algorithms. To address the challenge, feasibility-aware learning-to-optimize (L2O) techniques have recently gained attention. These learning-based methods offer efficient alternatives to conventional solvers by directly learning mappings from system parameters to feasible and near-optimal solutions. This article provide a comprehensive review of L2O model designs and feasibility enforcement techniques and investigates the application of constrained L2O in wireless RA systems and. The paper also presents a case study to benchmark different L2O approaches in weighted sum rate problem, and concludes by identifying key challenges and future research directions. |
| title | Feasibility-Aware Learning-to-Optimize in Wireless Communication Resource Allocation |
| topic | Information Theory |
| url | https://arxiv.org/abs/2509.02417 |