Towards Specialized Wireless Networks Using an ML-Driven Radio Interface
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909516999688192 |
|---|---|
| author | Szczech, Kamil Wojnar, Maksymilian Kosek-Szott, Katarzyna Rusek, Krzysztof Szott, Szymon Marasinghe, Dileepa Rajatheva, Nandana Combes, Richard Wilhelmi, Francesc Jonsson, Anders Bellalta, Boris |
| author_facet | Szczech, Kamil Wojnar, Maksymilian Kosek-Szott, Katarzyna Rusek, Krzysztof Szott, Szymon Marasinghe, Dileepa Rajatheva, Nandana Combes, Richard Wilhelmi, Francesc Jonsson, Anders Bellalta, Boris |
| contents | Future wireless networks will need to support diverse applications (such as extended reality), scenarios (such as fully automated industries), and technological advances (such as terahertz communications). Current wireless networks are designed to perform adequately across multiple scenarios so they lack the adaptability needed for specific use cases. Therefore, meeting the stringent requirements of next-generation applications incorporating technology advances and operating in novel scenarios will necessitate wireless specialized networks which we refer to as SpecNets. These networks, equipped with cognitive capabilities, dynamically adapt to the unique demands of each application, e.g., by automatically selecting and configuring network mechanisms. An enabler of SpecNets are the recent advances in artificial intelligence and machine learning (AI/ML), which allow to continuously learn and react to changing requirements and scenarios. By integrating AI/ML functionalities, SpecNets will fully leverage the concept of AI/ML-defined radios (MLDRs) that are able to autonomously establish their own communication protocols by acquiring contextual information and dynamically adapting to it. In this paper, we introduce SpecNets and explain how MLDR interfaces enable this concept. We present three illustrative use cases for wireless local area networks (WLANs): bespoke industrial networks, traffic-aware robust THz links, and coexisting networks. Finally, we showcase SpecNets' benefits in the industrial use case by introducing a lightweight, fast-converging ML agent based on multi-armed bandits (MABs). This agent dynamically optimizes channel access to meet varying performance needs: high throughput, low delay, or fair access. Results demonstrate significant gains over IEEE 802.11, highlighting the system's autonomous adaptability across diverse scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20996 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Specialized Wireless Networks Using an ML-Driven Radio Interface Szczech, Kamil Wojnar, Maksymilian Kosek-Szott, Katarzyna Rusek, Krzysztof Szott, Szymon Marasinghe, Dileepa Rajatheva, Nandana Combes, Richard Wilhelmi, Francesc Jonsson, Anders Bellalta, Boris Networking and Internet Architecture Future wireless networks will need to support diverse applications (such as extended reality), scenarios (such as fully automated industries), and technological advances (such as terahertz communications). Current wireless networks are designed to perform adequately across multiple scenarios so they lack the adaptability needed for specific use cases. Therefore, meeting the stringent requirements of next-generation applications incorporating technology advances and operating in novel scenarios will necessitate wireless specialized networks which we refer to as SpecNets. These networks, equipped with cognitive capabilities, dynamically adapt to the unique demands of each application, e.g., by automatically selecting and configuring network mechanisms. An enabler of SpecNets are the recent advances in artificial intelligence and machine learning (AI/ML), which allow to continuously learn and react to changing requirements and scenarios. By integrating AI/ML functionalities, SpecNets will fully leverage the concept of AI/ML-defined radios (MLDRs) that are able to autonomously establish their own communication protocols by acquiring contextual information and dynamically adapting to it. In this paper, we introduce SpecNets and explain how MLDR interfaces enable this concept. We present three illustrative use cases for wireless local area networks (WLANs): bespoke industrial networks, traffic-aware robust THz links, and coexisting networks. Finally, we showcase SpecNets' benefits in the industrial use case by introducing a lightweight, fast-converging ML agent based on multi-armed bandits (MABs). This agent dynamically optimizes channel access to meet varying performance needs: high throughput, low delay, or fair access. Results demonstrate significant gains over IEEE 802.11, highlighting the system's autonomous adaptability across diverse scenarios. |
| title | Towards Specialized Wireless Networks Using an ML-Driven Radio Interface |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2502.20996 |