Fast Adaptation for Deep Learning-Based Wireless Communications
Saved in:
| Title: | Fast Adaptation for Deep Learning-Based Wireless Communications |
|---|---|
| Authors: | Wang, Ouya, He, Hengtao, Zhou, Shenglong, Ding, Zhi, Jin, Shi, Letaief, Khaled B., Li, Geoffrey Ye |
| Source: | IEEE Communications Magazine. 63:158-164 |
| Publication Status: | Preprint |
| Publisher Information: | Institute of Electrical and Electronics Engineers (IEEE), 2025. |
| Publication Year: | 2025 |
| Subject Terms: | Computer Science - Networking and Internet Architecture, Networking and Internet Architecture (cs.NI), Signal Processing (eess.SP), FOS: Computer and information sciences, Emerging Technologies (cs.ET), FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Emerging Technologies, Electrical Engineering and Systems Science - Signal Processing |
| Description: | The integration with artificial intelligence (AI) is recognized as one of the six usage scenarios in next-generation wireless communications. However, several critical challenges hinder the widespread application of deep learning (DL) techniques in wireless communications. In particular, existing DL-based wireless communications struggle to adapt to the rapidly changing wireless environments. In this paper, we discuss fast adaptation for DL-based wireless communications by using few-shot learning (FSL) techniques. We first identify the differences between fast adaptation in wireless communications and traditional AI tasks by highlighting two distinct FSL design requirements for wireless communications. To establish a wide perspective, we present a comprehensive review of the existing FSL techniques in wireless communications that satisfy these two design requirements. In particular, we emphasize the importance of applying domain knowledge in achieving fast adaptation. We specifically focus on multiuser multiple-input multiple-output (MU-MIMO) precoding as an examples to demonstrate the advantages of the FSL to achieve fast adaptation in wireless communications. Finally, we highlight several open research issues for achieving broadscope future deployment of fast adaptive DL in wireless communication applications. |
| Document Type: | Article |
| ISSN: | 1558-1896 0163-6804 |
| DOI: | 10.1109/mcom.001.2400502 |
| DOI: | 10.48550/arxiv.2409.04302 |
| Access URL: | http://arxiv.org/abs/2409.04302 |
| Rights: | IEEE Copyright CC BY |
| Accession Number: | edsair.doi.dedup.....8aed94d23fec645e2fae0aad0834257f |
| Database: | OpenAIRE |
Full Text Finder 
Nájsť tento článok vo Web of Science | Abstract: | The integration with artificial intelligence (AI) is recognized as one of the six usage scenarios in next-generation wireless communications. However, several critical challenges hinder the widespread application of deep learning (DL) techniques in wireless communications. In particular, existing DL-based wireless communications struggle to adapt to the rapidly changing wireless environments. In this paper, we discuss fast adaptation for DL-based wireless communications by using few-shot learning (FSL) techniques. We first identify the differences between fast adaptation in wireless communications and traditional AI tasks by highlighting two distinct FSL design requirements for wireless communications. To establish a wide perspective, we present a comprehensive review of the existing FSL techniques in wireless communications that satisfy these two design requirements. In particular, we emphasize the importance of applying domain knowledge in achieving fast adaptation. We specifically focus on multiuser multiple-input multiple-output (MU-MIMO) precoding as an examples to demonstrate the advantages of the FSL to achieve fast adaptation in wireless communications. Finally, we highlight several open research issues for achieving broadscope future deployment of fast adaptive DL in wireless communication applications. |
|---|---|
| ISSN: | 15581896 01636804 |
| DOI: | 10.1109/mcom.001.2400502 |