Performance Analysis of Transmitter Identification Based on I/Q Imbalance Estimation

Performance of a transmitter identification method based on I/Q imbalance estimation is analyzed for different modulation types in this work. This analysis is considered on the basis of the constellation structure of proper and improper modulation signals. The effect of impropriety of complex signals on the features obtained from I/Q imbalance is analyzed. Simulation results show that transmitters can be identified with a high accuracy for proper communication signals even at low SNR, whereas the identification performance degrades for improper communications signals, especially at low SNR. © 2019 IEEE

Learning Robust Radio Frequency Fingerprints Using Deep Convolutional Neural Networks

Radio Frequency Fingerprinting (RFF) techniques, which attribute uniquely identifiable signal distortions to emitters via Machine Learning (ML) classifiers, are limited by fingerprint variability under different operational conditions. First, this work studied the effect of frequency channel for typical RFF techniques. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models leads to deterioration in MCC to under 0.05 (random guess), indicating that single-channel models are inadequate for realistic operation. Second, this work presented a novel way of studying fingerprint variability through Fingerprint Extraction through Distortion Reconstruction (FEDR), a neural network-based approach for quantifying signal distortions in a relative distortion latent space. Coupled with a Dense network, FEDR fingerprints were evaluated against common RFF techniques for up to 100 unseen classes, where FEDR achieved best performance with MCC ranging from 0.945 (5 classes) to 0.746 (100 classes), using 73% fewer training parameters than the next-best technique

Transmitter Classification With Supervised Deep Learning

International audienceHardware imperfections in RF transmitters introduce features that can be used to identify a specific transmitter amongst others. Supervised deep learning has shown good performance in this task but using datasets not applicable to real world situations where topologies evolve over time. To remedy this, the work rests on a series of datasets gathered in the Future Internet of Things / Cognitive Radio Testbed [4] (FIT/CorteXlab) to train a convolutional neural network (CNN), where focus has been given to reduce channel bias that has plagued previous works and constrained them to a constant environment or to simulations. The most challenging scenarios provide the trained neural network with resilience and show insight on the best signal type to use for identification , namely packet preamble. The generated datasets are published on the Machine Learning For Communications Emerging Technologies Initiatives web site 4 in the hope that they serve as stepping stones for future progress in the area. The community is also invited to reproduce the studied scenarios and results by generating new datasets in FIT/CorteXlab

Joint estimation of multiple RF impairments using deep multi-task learning

Radio-frequency (RF) front-end forms a critical part of any radio system, defining its cost as well as communication performance. However, these components frequently exhibit non-ideal behavior, referred to as impairments, due to the imperfections in the manufacturing/design process. Most of the designers rely on simplified closed-form models to estimate these impairments. On the other hand, these models do not holistically or accurately capture the effects of real-world RF front-end components. Recently, machine learning-based algorithms have been proposed to estimate these impairments. However, these algorithms are not capable of estimating multiple RF impairments jointly, which leads to limited estimation accuracy. In this paper, the joint estimation of multiple RF impairments by exploiting the relationship between them is proposed. To do this, a deep multi-task learning-based algorithm is designed. Extensive simulation results reveal that the performance of the proposed joint RF impairments estimation algorithm is superior to the conventional individual estimations in terms of mean-square error. Moreover, the proposed algorithm removes the need of training multiple models for estimating the different impairments

IEEE Access Special Section Editorial: Artificial Intelligence and Cognitive Computing for Communication and Network

With the rapid development of communication and network technologies, novel information services and applications are rapidly growing worldwide. Advanced communications and networks greatly enhance the user experience, and have a major impact on all aspects of people's lifestyles in terms of work, society, and the economy. Although advanced techniques have extensively improved users' quality of experience (QoE), they are not adequate to meet the various requirements of seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability, and other scenarios. Therefore, it is a great challenge to develop smart communications and networks that support optimized management, dynamic configuration, and feasible services