1. SplitAMC: Split Learning for Robust Automatic Modulation Classification
- Author
-
Park, Jihoon, Oh, Seungeun, and Kim, Seong-Lyun
- Subjects
Signal Processing (eess.SP) ,FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer Science - Cryptography and Security ,Information Theory (cs.IT) ,Computer Science - Information Theory ,Numerical Analysis (math.NA) ,Machine Learning (cs.LG) ,Computer Science - Distributed, Parallel, and Cluster Computing ,FOS: Electrical engineering, electronic engineering, information engineering ,FOS: Mathematics ,Distributed, Parallel, and Cluster Computing (cs.DC) ,Mathematics - Numerical Analysis ,Electrical Engineering and Systems Science - Signal Processing ,Cryptography and Security (cs.CR) - Abstract
Automatic modulation classification (AMC) is a technology that identifies a modulation scheme without prior signal information and plays a vital role in various applications, including cognitive radio and link adaptation. With the development of deep learning (DL), DL-based AMC methods have emerged, while most of them focus on reducing computational complexity in a centralized structure. This centralized learning-based AMC (CentAMC) violates data privacy in the aspect of direct transmission of client-side raw data. Federated learning-based AMC (FedeAMC) can bypass this issue by exchanging model parameters, but causes large resultant latency and client-side computational load. Moreover, both CentAMC and FedeAMC are vulnerable to large-scale noise occured in the wireless channel between the client and the server. To this end, we develop a novel AMC method based on a split learning (SL) framework, coined SplitAMC, that can achieve high accuracy even in poor channel conditions, while guaranteeing data privacy and low latency. In SplitAMC, each client can benefit from data privacy leakage by exchanging smashed data and its gradient instead of raw data, and has robustness to noise with the help of high scale of smashed data. Numerical evaluations validate that SplitAMC outperforms CentAMC and FedeAMC in terms of accuracy for all SNRs as well as latency., to be presented at IEEE VTC2023-Spring
- Published
- 2023