Projects:2019s1-115 Deep Learning Based Specific Emitter Identification

Team

Students

• Vaibhav Sekhar
• William Voss

Supervisors

• Dr. Brian Ng
• Dr. Joy Li (DST Group)
• Dr. Tharaka Lamahewa (DST Group)

Introduction

Specific Emitter Identification (SEI) is the association of a received signal with its transmitter. Such capability is highly desirable for many military and civilian applications. Cognitive radio applications heavily rely on SEI aided emitter tracking to reinforce the Dynamic Spectrum Access (DSA) rules to improve networks reliability. In wireless communication, many networks often use SEI capability to support emitter authentication to enhance network security.

Traditionally, the SEI process is achieved through precise measurement of the intercepted signals and carefully extracts some of the signal features which are caused by the unintentional impairments of the emitter, known as RF finger printing. Based on the signal feature extraction, the intercepted signals are then clustered by emitter before identification process is carried out. This identification process is usually database aided

IQ imbalance exists in most of the direct-up converters, and is one of the most commonly seen emitter impairments. It refers to the mismatch between the in-phase and quadrature paths of the transmitter and is introduced by the imperfection of analogue components within the transmitter. This mismatch can be amplitude or phase mismatch. But most commonly, it is found in a combination of both. It is also most likely non-linear and frequency dependent, which reflect the nature of analogue components. IQ imbalance is very often used for transmitter characterization as part of the SEI process.

The SEI process is challenging both from the hardware requirement perspective and from the processing perspective. As the SEI process is only made possible by identifying the naturally occurring and unintentional characteristics of the emitter, the receiver that is used for signal intercept must be highly linear to avoid ambiguities. Any hardware imperfection introduced by the intercept receiver such as frequency dependent amplitude or phase offsets must be calibrated out before the intercept process taken place. This calibration process can be very time consuming, and may require to be repeated just before the actual signal intercept and collection process begins.

The computational cost associated with the SEI process is significant. Depending on the method used for the actual SEI process, signals are often transformed into one or multiple joint domains, which can be very computationally expensive. Dimensionality reduction is also used in some of the SEI techniques, which requires large matrix manipulation. This further increases the complexity and number of operations required for the SEI process.

In recent years, a new machine learning methodology known as Deep Learning (DL) has found its own success in areas such as computer vision, speech recognition and image classification areas [1,2]. As the Deep Neural Network (DNN) has proven itself to be very effective at processing unstructured data, many innovative technology companies have started to experiment with DNN to replace signal processing tasks for many communication applications [6]. In particular, authors in [3,4,5] have successfully applied DL algorithms as a computational efficient way to estimate IQ imbalance and characterize transmitters for SEI purposes.

Project Aims and Objectives

This research aims to investigate the use of CNNs for IQ imbalance estimation. The key objectives of this research are:

• To determine the feasibility of CNNs for IQ imbalance estimation
• To compare the IQ imbalance estimation performance of CNNs to existing Digital Signal Processing techniques

Background

Overview of digital signal transmission

Complex-valued signals can be represented digitally as streams of binary data. Transmitters first process the digital data through a digital signal processor (DSP). The DSP divides the binary stream into smaller segments known as symbols, with the length of the symbols (in bits) depending on the desired modulation alphabet. The modulation alphabet maps the symbols to complex numbers.

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References

[1] A. Krizhevsky, I. Sutskever and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 2012, pp. 807-814.
[2] I. Goodfellow, Y. Bengio and A. Courville, “Deep learning,” MIT Press, 2016.
[3] L. J. Wong, “On the use of convolutional neural networks for specific emitter identification”, Masters Thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, Apr. 2018.
[4] L. J. Wong, W. C. Headley and A. J. Michaels, “Emitter identification using CNN IQ imbalance estimators”, https://arxiv.org/pdf/1808.02369.pdf
[5] L. J. Wong, W. C. Headley and A. J. Michaels, “Estimation of transmitter I/Q imbalance using convolutional neural networks,”, in proc. IEEE 8th Annual Computing and Communication Workshop (CCWC), Las Vegas, NV, Jan. 2018, pp 948-955.
[6] T.J. O’Shea, J. Corgan and T.C. Clancy, “ Convolutional Radio Modulation Recognition Networks”, Engineering Applications of Neural Networks, EANN 2016. Communications in Computer and Information Science, Srpinger, 2016, 629