Accumulated Polar Feature-Based Deep Learning for Efficient and Lightweight Automatic Modulation Classification with Channel Compensation Mechanism
Journal
IEEE Transactions on Vehicular Technology
Journal Volume
69
Journal Issue
12
Pages
15472-15485
Date Issued
2020
Author(s)
DOI
ITVTA
Abstract
In next-generation communications, massive machine-type communications (mMTC) induce severe burden on base stations. To address such an issue, automatic modulation classification (AMC) can help to reduce signaling overhead by blindly recognizing the modulation types without handshaking. Thus, it plays an important role in future intelligent modems. The emerging deep learning (DL) technique stores intelligence in the network, resulting in superior performance over traditional approaches. However, DL-based approaches suffer from heavy training overhead, memory overhead, and computational complexity, which severely hinder practical applications for resource-limited scenarios, such as Internet-of-Things (IoT) networks and unmanned aerial vehicle (UAV)-aided systems. Furthermore, the overhead of online retraining under time-varying fading channels has not been studied in the prior arts. In this work, an accumulated polar feature-based DL with a channel compensation mechanism is proposed to cope with the aforementioned issues. Firstly, the simulation results show that learning features from the polar domain with historical data information can approach near-optimal performance while reducing training overhead by 99.8 times. Secondly, the proposed neural network-based channel estimator (NN-CE) can learn the channel response and compensate for the distorted channel with 13% improvement. Moreover, in applying this lightweight NN-CE in a time-varying fading channel, two efficient mechanisms of online retraining are proposed, which can reduce transmission overhead and retraining overhead by 90% and 76%, respectively. Finally, the performance of the proposed approach is evaluated and compared with prior arts on a public dataset to demonstrate its great efficiency and lightness. The lightweight and efficient learning features of the proposed mechanism will be very attractive for future resource-constrained/aware IoT and Vehicle-to-Everything (V2X) applications. © 1967-2012 IEEE.
Subjects
Automatic modulation classification; convolutional neural network; deep learning; online retraining; polar coordinate; time-varying fading channel
Other Subjects
Antennas; Arts computing; Cost reduction; Fading channels; Internet of things; Modulation; Time varying networks; Unmanned aerial vehicles (UAV); Automatic modulation classification; Automatic modulation classification (AMC); Internet of Things (IOT); Machine type communications; Near-optimal performance; Time varying fading channels; Traditional approaches; Transmission overheads; Deep learning
Type
journal article