Year: 2025
Type: preprint
Abstract: Grant-free random access in massive machine-type communications enables low-latency connectivity with minimal signaling. However, sporadic device activation requires efficient device activity detection. We propose a federated learning-based device activity detection approach, leveraging distributed training to enhance security and privacy while maintaining low computational complexity. Compared to existing methods, our solution achieves competitive detection performance, addressing scalability and security challenges in mMTC networks.
Source: arXiv (Cornell University)
Authors: Elkeshawy, Ali, Ghosh, Ibrahim Al, Fares, Haifa, Nafkha, Amor
Language: English
Topic: IoT Networks and Protocols
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6929287409
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