(AMC) Are One of the Basic Building Blocks of Electronic Warfare Receivers and Cognitive Radios

ABSTRACT

AUTOMATIC MODULATION CLASSIFIER A BLIND FEATURE-BASED TOOL

by Patrick Cutno

Automatic modulation classifiers (AMC) are one of the basic building blocks of electronic warfare receivers and cognitive radios. Although many research papers on AMC algorithms have been published, very few results on their implementation are available. This thesis presents a feature-based AMC built upon a software-defined radio platform. The developed AMC can detect signals over a broad spectrum and classify the modulation used. The modulation schemes considered in this thesis are amplitude modulation (AM), frequency modulation (FM), phase-shift keying (PSK), and quadrature amplitude modulation (QAM). Experimental results demonstrate the validity of the developed AMC algorithm and its implementation.

AUTOMATIC MODULATION CLASSIFIER A BLIND FEATURE-BASED TOOL

A Thesis

Submitted to the

Faculty of Miami University

in partial fulfillment of

the requirements for the degree of

Master of Science

Patrick Cutno

Miami University

Oxford, Ohio

2016

Advisor: Dr. Chi-Hao Cheng

Reader: Dr. Dmitriy Garmatyuk

Reader: Dr. Jason Pennington

This Thesis titled

AUTOMATIC MODULATION CLASSIFIER A BLIND FEATURE-BASED TOOL

Patrick Cutno

has been approved for publication by

The School of Engineering and Applied Science

and

Department of Electrical and Computer Engineering

______Dr. Chi-Hao Cheng

______Dr. Dmitriy Garmatyuk

______Dr. Jason Pennington

Table of Contents

List of Tables ...... v List of Figures ...... vi Dedication ...... vii Acknowledgements ...... viii Chapter 1 Introduction ...... 1 1.1 AMC ...... 1 1.1.1 Military Applications ...... 1 1.1.2 Civilian Applications ...... 2 1.2 Problem Statement ...... 2 Chapter 2 Fundamentals ...... 4 2.1 Amplitude Modulation ...... 4 2.2 Frequency Modulation ...... 6 2.3 Phase-Shift Keying ...... 6 2.4 Quadrature Amplitude Modulation ...... 8 Chapter 3 Literature Review ...... 10 3.1 Signal Detection ...... 10 3.2 Spectral-based Feature ...... 10 3.3 Wavelet Transform Feature ...... 12 3.4 Fourth-Order Cumulants Feature ...... 12 3.5 k-Nearest Neighbor Algorithm ...... 13 3.6 Summary ...... 14 Chapter 4 Experimentation...... 15 4.1 Modulation & Decision Tree ...... 15 4.2 AMC Walkthrough Guide ...... 17 4.3 Experiments ...... 18 4.3.1 Spectrum Scan and Signal Detection ...... 18 4.3.2 Decision Tree Validation ...... 19

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4.3.3 Over-the-air FM Classification ...... 19 4.3.4 SNR Testing ...... 19 4.3.5 Runtime ...... 19 Chapter 5 Results ...... 20 5.1 Spectrum Scan and Signal Detection...... 20 5.2 Decision Tree Validation ...... 20 5.3 Over-the-air FM Classification ...... 23 5.4 SNR Testing ...... 23 5.5 Runtimes ...... 25 Chapter 6 Conclusion & Future Work ...... 27 Bibliography...... 29

List of Tables

TABLE 4.1 NI USRP-2920 Specification ...... 17 TABLE 4.2 Decision Tree Threshold Calculations Using Average Training Data ...... 17 TABLE 5.1 Correct Classification at 2 GHz and 100 Realizations...... 22 TABLE 5.2 Correct Classification at 1 GHz and 100 Realizations...... 22 TABLE 5.3 Correct Classification at 100 MHz and 100 Realizations ...... 22 TABLE 5.4 System Runtimes ...... 26

List of Figures

Figure 2.1 DSB-AM RX & TX block diagrams ...... 5 Figure 2.2 FM RX & TX block diagrams ...... 5 Figure 2.3 Message (top) and amplitude modulated (bottom) ...... 5 Figure 2.4 Message (top) and frequency modulated (bottom) ...... 5 Figure 2.5 4PSK RX & TX block diagrams ...... 7 Figure 2.6 Message (top) and phase-shift keying (bottom) ...... 7 Figure 2.7 4-QAM RX & TX block diagrams ...... 9 Figure 2.8 16-QAM constellation plot ...... 9 Figure 4.1 Daisy chained NI USRP-2920, interface via Ethernet ...... 16 Figure 4.2 Decision tree for AM, FM, 16-PSK, and 16-QAM ...... 16 Figure 5.1 User interface identifies carrier frequency and modulation scheme ...... 21 Figure 5.2 100 calculated values of UF42 at 2 GHz ...... 21 Figure 5.3 100 calculated values of C42 at 2 GHz...... 21 Figure 5.4 100 calculated values of UF42 at 1 GHz ...... 21 Figure 5.5 100 calculated values of C42 at 1 GHz...... 21 Figure 5.6 100 calculated values of UF42 at 100 MHz ...... 22 Figure 5.7 100 calculated values of C42 at 100 MHz ...... 22 Figure 5.8 100 calculated values of UF42 at 88.5 MHz over-the-air ...... 23 Figure 5.9 400 point correct classification at 10, 15, 16, 18 and 20 dB ...... 24 Figure 5.10 2 GHz single UF42 classification at 0 - 20 dB ...... 24 Figure 5.11 2 GHz single C42 classification at 0 - 20 dB ...... 24 Figure 5.12 Signal energy PDF of 957 MHz, a frequency with only noise ...... 26

Dedication I dedicate this thesis to my hard working single mother that did everything in her power to make sure my sister and I could live a comfortable life growing up.

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Acknowledgements

I would like to express my gratitude to Dr. Chi-Hao Cheng for the opportunity to pursue my Master’s degree, his help as an advisor for my thesis work, and serving as a committee member. Dr. Qihou Zhou and Dr. Peter Jamieson also have my gratitude for their kind recommendation letters for my acceptance into the Electrical and Computational Engineering Department’s Master’s program. Thank you to Dr. Dmitriy Garmatyuk, and Dr. Jason Pennington for not only taking the time out of their busy schedules to serve on my committee, but for the constructive criticism during my proposal phase that allowed me improved the quality of content in this thesis. Thanks to the National Science Foundation and the grant that provided the financial means to work on this software-defined radio project. The grant is the Collaborative: TUES: Software Defined Radio Laboratory Platform for Enhancing Undergraduate Communication and Networking Curricula (Award #: 1323105). Lastly, I would also like to thank my friends in the Electrical and Computational department Tyler Maschino, Andrew Rush, Melissa Simms, Gabriel Hepner, Patryk Giza, and Daniel Kellett for the moral support and being all around awesome friends.

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Chapter 1

Introduction

Automatic modulation sits between a signal’s reception and demodulation of the receiver (RX) chain to determine how a signal is modulated [1]. In a typical communication system, the modulation scheme is agreed upon before a message signal can be transmitted: however, in the application of electronic warfare (EW) receivers and cognitive radios (CR), the receiver may need to classify modulation schemes from non-cooperative transmitters (TX). For this reason a blind AMC which can classify a signal’s modulation scheme without a priori knowledge is necessary.

1.1 AMC

The need for an AMC can be seen in various military and civilian applications such as EW receivers and CRs, respectively. Some of its potential applications are described below.

1.1.1 Military Applications

In terms of military applications, an EW receiver plays an important role in modern defensive strategies to minimize the risk to soldiers. The purpose of EW is to detect hostile signals and allow friendly parties to take appropriate countermeasures. EW encompasses three principal components: electronic protect (EP) prevents enemies from jamming friendly signals, electronic attack (EA) uses jamming techniques to prevent enemies from communicating, and electronic support (ES) implements AMC’s to identify the modulation scheme used by enemies [2] [3]. One of the easiest ways to jam a signal is to transmit a significantly higher-power signal using the same modulation scheme and carrier frequency. Due to the nature of jamming signals,

AMCs can detect them relatively easily due to their high signal power. Once the modulation scheme is known, EP and EA apply that knowledge and decide what to do with it. ES monitors for potential jamming signals and EP informs troops to avoid using those carrier frequencies or modulation schemes. The reverse is also true: with the enemy modulation scheme in hand, EA can jam enemy communications. In addition to jamming, enemy communications could also be demodulated to intercept messages.

1.1.2 Civilian Applications

In civilian applications, CR is an intelligent wireless communication platform built with a software-defined radio (SDR) to improve spectral efficiency [4]. By detecting unoccupied gaps in the radio spectrum, a CR transceiver can dynamically pick a carrier frequency and modulation scheme that will optimize the reliability of communications without interfering with licensed communication users. To ensure optimal data rate and energy efficiency, Goldsmith and Chua designed a communication system called link adaptation [5]. The system selects a modulation scheme based on measured channel conditions and at initialization, creates a collection of modulation schemes that would best fit the channel. In order for this to work, part of each signal frame is used to notify the receiver about a change in modulation or carrier frequency if conditions have changed. If implemented with a blind AMC, both the receiver and transmitter (TX) can agree on the same collection of candidate schemes and decision tree to increase transmission throughput.

1.2 Problem Statement

Various AMC methods have been developed and they generally fall into one of two categories: likelihood-based or feature-based approaches. Likelihood-based modulation classifiers minimize false classification by performing rigorous hypothesis testing and tend to lead to optimal solutions [6] – [9]. This approach, however, can be computationally complex. The feature-based approach classifies a signal’s modulation by determining key features unique to each modulation scheme [10] – [13]. Feature-based approaches are straightforward to implement and still lead to near- optimal solutions. In this thesis, a software-defined radio is used to implement an AMC, focusing on key factors such as the ease of use and implementation. Therefore, the feature-based approach was used to develop the SDR-based AMC.

Although plenty of AMC techniques have been proposed and investigated, to the best of the author’s knowledge, not much literature on experimental results is available to the public and the results presented in this thesis can fill this void. With the advance of SDRs in recent years, the implementation of a radio receiver has been significantly simplified. In this thesis, an SDR based AMC will be presented that can successfully classify AM, FM, 16-PSK, and 16-QAM signals. The results presented in this thesis will be valuable to researchers who are interested in conducting experiments on AMC. The remainder of this thesis is organized as follows: Chapter 2 introduces the fundamentals of each modulation scheme this thesis aims to identify; Chapter 3 provides a literature review on AMC-related techniques; Chapter 4 provides details of the AMC implementation and experimental setup; Chapter 5 presents and discusses the experimental results; Finally, Chapter 6 discusses future work and concludes this thesis.

Chapter 2

Fundamentals

When talking with another person, it is obvious that the human voice does not travel far. If someone were on the opposite side of a huge auditorium or in another room, one would have to yell fairly loudly to communicate. The basic concept of modulation is to modify or modulate a very high frequency sine wave, called a carrier wave, which allows the message to travel much farther distances. However once modulated, the message is no longer audible. Therefore a receiver is necessary to demodulate the signal and retrieve the original message.

2.1 Amplitude Modulation

As the name suggests, amplitude modulation (AM) is an analog technique of modifying the amplitude of a carrier wave. This can be done simply by multiplying the message signal source and carrier wave together as seen in Fig. 2.1. Fig. 2.3 shows an analog message and the resulting AM signal. The spectrum of the resulting wave has the message signal imposed above and below the carrier wave frequency creating two sidebands that can travel farther than the message signal can on its own. If the modulation process is repeated, the spectrum shifts to the left and the copy of the signal that was mirrored in the negative portion of the frequency spectrum will now be centered on zero, or baseband. After using a low pass filter, the AM signal is considered to be demodulated as the original message can be recovered. AM is by far one of the easiest modulation schemes to implement. A considerable flaw however, is its high susceptibility to interference such as from thunder-storms.

Figure 2.1 DSB-AM RX & TX block diagrams Figure 2.2 FM RX & TX block diagrams

Figure 2.3 Message (top) and amplitude modulated (bottom)

Figure 2.4 Message (top) and frequency modulated (bottom)

2.2 Frequency Modulation

Frequency modulation (FM) is another analog modulation scheme. Compared to AM, FM is less prone to outside interference as the message modulates the frequency of the carrier wave instead of the amplitude. In the case of a thunderstorm, if the amplitude of an FM signal is changed, it could still be demodulated unlike with AM. To achieve this, a larger bandwidth is needed which can be a pro or con depending on the application. As a result of the larger bandwidth, it is possible to achieve better audio quality from radio stations, but in terms of efficiency, that means fewer FM stations over a given bandwidth compared to AM stations. The only difference between creating FM and AM signals is that instead of multiplying the message and carrier together, the modulating signal is fed into a voltage-controlled oscillator (VCO) that changes the frequency of the carrier signal as seen in Fig. 2.2 and 2.4. To demodulate an FM signal, a Phase-Locked Loop can be used in conjunction with a VCO to follow the frequency changes in FM. In turn, the VCO will generate the voltage needed to follow the changes in frequency and generate the demodulated signal.

2.3 Phase-Shift Keying

Phase-Shift Keying (PSK) is a digital modulation scheme that can be seen in applications such as Bluetooth and radio frequency identification (RFID). It is significantly less prone to noise and produces a more reliable transmission compared to analog modulation schemes. In a PSK scheme, the message is encoded onto the phase of a carrier wave. In the case of binary phase-shift keying (BPSK), the transmitter will rotate the carrier wave’s phase by +/- 180 degrees depending on the bit pattern of the modulating signal. The number of bits each PSK symbol can represent depends on the number of distinct phases used to encode information. For example, an 8-PSK signal whose 2휋 phase can be a multiple of , one symbol can be represented with three bits (23 = 8). For 8 demodulation, the receiver compares the incoming signal and a reference signal that is identical to the carrier signal to determine the exact phase to synchronize with and detects the phase shift patterns to recreate the original message. PSK has one sizable issue. The local oscillators (LO) at the TX and RX can be out of sync and result in a demodulation error because the information is encoded on the carrier’s phase.

Figure 2.5 4-PSK RX & TX block diagrams

Figure 2.6 Message (top) and phase-shift keying (bottom) Even if both TX and RX were to start at the same exact time and generate the exact same carrier wave, they could still be out of sync since it is impossible to get two LO’s perfectly synchronized unless a complicated phase lock circuit is used. Simply put, the synchronization between TX and RX is crucial for PSK and similar digital modulation schemes. To solve the synchronization issue, many techniques have been proposed and a variation of PSK called differential phase-shift keying (DPSK) has been used in some applications. In this scheme, the receiver no longer needs an exact copy of the carrier because the phase changes are

7 now triggered by a change in state of the modulating signal. Using differential binary PSK (DBPSK) as an example, the carrier phase changes only when the value of the digital signal has a bit change. Fig. 2.6 shows a digital square wave message above the resulting PSK modulated signal. Most digital signals have two sinusoidal carriers with a 90° phase shift between them. These two carriers are referred to as the in-phase (I) and quadrature (Q) components. By modulating the phase onto the IQ carriers with different parts of the digital signal, much more efficient information transfer can be achieved as seen in Fig. 2.5. When a PSK-modulated signal is plotted on a complex plane, it is called a constellation plot where the I-component contains real values and the Q-component contains imaginary values. The values of similar phases tend to group together on a complex plane to create something like a constellation, hence the name.

2.4 Quadrature Amplitude Modulation

As described in the previous section, a PSK signal can be displayed on a complex plane with real (cosine) and imaginary (sine) components. Since a PSK signal only uses the phase to encode information, the resulting signal has a constant amplitude. If both the amplitude and phase of a carrier wave were used to encode data on the two I and Q carrier waves, the resulting modulation scheme is called Quadrature Amplitude Modulation (QAM), depicted in Fig. 2.7. Like PSK, there are variations of QAM found in applications such as cable TV, Internet, and 802.11 Wi-Fi. QAM is significantly more bandwidth efficient if compared with PSK of the same order. QAM has larger distances between I-Q symbols, meaning it is less susceptible to noise or it can support more symbols (i.e. more bits per symbol) with the same distance between symbols. Fig. 2.8 shows a constellation plot with a 16 point QAM, and it is shown that there are four different magnitudes in both the I and Q components resulting in 16 combinations, each of which represent 4 bits. Since QAM uses both the amplitude and phase of a complex signal to encode information, it is one of the more challenging modulation schemes to implement.

Figure 2.7 4-QAM RX & TX block diagrams

Figure 2.8 16-QAM constellation plot

Chapter 3

Literature Review

This chapter provides a review of literature in the areas of signal detection, feature generation, and algorithms.

3.1 Signal Detection

As F.F. Liedtke mentioned in [14], signal classification and demodulation can only be done if the system is robust enough to overcome disturbing factors such as a mismatch of center frequencies. He tested the use of a signal energy analyzer by sweeping across the spectrum and comparing the signal energy with a noise threshold to help determine the presence of signals in a densely packed frequency region. The preliminary results showed he could determine carrier frequencies of potential signals and correctly classify them. While Liedtke did not develop the idea of an energy detector [15], his work and many others have recognized the importance of an energy detector in CR applications [16] - [19].

3.2 Spectral-based Feature

In the 1990’s, E.E. Nandi and A.K. Azzouz collected and advanced the results of many leading papers, and even created new signal features that extracted spectral characteristics of various modulation schemes [20]. Their papers have become some of the most cited works in the field of AMC’s and are often used as the benchmark to compare new AMC techniques. With eight key features based on signal amplitude, phase, and frequency they were able to classify 13 analog and digital signals through simulation. The following is a list of modulations that can be identified by E.E. Nandi and A.K. Azzouz’s techniques and decision tree:

 Amplitude Modulation  Amplitude Shift-keying (ASK), 2 & 4 order  Combined AM and FM  Double Sideband (DSB)  Frequency Modulation  Frequency Shift-keying (FSK), 2 & 4 order  Lower Sideband (LSB)  Phase Shift-keying, 2 & 4 order  Upper Sideband (USB)  Vestigial Sideband (VSB) Two key features of note are the kurtosis of the normalized and centered instantaneous

푎 푓 amplitude and frequency, 휇42 and 휇42 respectively. They are defined as follows: 4 푎 퐸{퐴푐푛(푛)} (3.1) 휇42 = 2 2 {퐸{퐴푐푛(푛)}} 4 푓 퐸{푓푁 (푛)} (3.2) 휇42 = 2 2 {퐸{푓푁 (푛)}}

1 푁 (3.3) 푓(푖) − ∑푖=1 푓(푖) 푓 (푖) = 푁 푁 푟푠 where 퐴푐푛 is the normalized & centered instantaneous amplitude of the received signal, 푓푁 is the normalized & centered instantaneous frequency, and N is the number of samples. The variable rs is the symbol rate of a digital signal but to keep things uniform for analog and digital signals the 푎 authors defined rs as a 12.5 kHz constant. The feature 휇42 measures the compactness of the instantaneous amplitude’s distribution. Nandi and Azzouz use this feature to differentiate between AM and ASK signals, as the distribution of AM signal’s instantaneous amplitude would not be as compact as ASK signals, which only have a limited number of amplitudes. The same argument 푓 also applies to 휇42 for the compactness of the instantaneous frequency’s distribution being used to differentiate between FM and FSK signals. All analog and digital signals were correctly classified more than 94.5% of the time at a signal-to-noise ratio (SNR) of 15.

3.3 Wavelet Transform Feature

Another feature-based approach to modulation classification is the use of the signal’s waveform. K.C. Ho, W. Prokopiw, and Y.T. Chan proposed the use of the wavelet transform to blindly identify M-PSK and M-FSK signals [21] (where M represents number of constellation points). The feature used is the continuous wavelet transform (CWT) which can be defined as: 1 푡 − 휏 (3.4) 퐶푊푇(푎, 휏) = ∫ 푠(푡)휓∗ ( ) 푑푡 √푎 푎 where a is the scale, 휏 is the translation, and 휓∗ is the complex conjugate of the mother wavelet. Through simulation, they have shown that any PSK signal can be differentiated form an FSK signal, as the magnitude of the resulting wavelet transform will primarily have a constant DC offset from zero with occasional peaks whenever the phase changes. To further differentiate various levels of PSK, all that is needed is to identify the number of different peak values generated at phase changes. FSK on the other hand will generate a multi-step function when the wavelet transform is applied and can easily be differentiated by identifying the number of different DC step levels. This approach can successfully classify BFSK, QFSK, and 8FSK as FSK signals with 100% correct classification. When used to classify BPSK, QPSK, and 8PSK signals, this approach achieves about 98% correct classification. Both results were found at an SNR of 13 dB.

3.4 Fourth-Order Cumulants Feature

In 2000, A. Swami and B.M. Sadler proposed the use of the fourth-order cumulant as a key feature for the classification of M-ary pulse amplitude modulation (PAM), M-PSK, and M-QAM [22]. If the given signal is zero-mean, the fourth-order cumulant can be estimated as: 푁 (3.5) 1 2 퐶̂ = ∑|푦(푛)|4 − |퐶̂ | − 2퐶̂2 42 푁 20 21 푛=1 푁 1 (3.6) 퐶̂ = ∑|푦(푛)|2 21 푁 푛=1 푁 1 (3.7) 퐶̂ = ∑ 푦2(푛) 20 푁 푛=1

12 where y is the signal segment with zero-mean. The subscript of the cumulant is composed of two numbers. The first represents the cumulant order and the second denotes the number of conjugated inputs.

Depending on the value of 퐶̂42, Swami’s and Sadler’s classifier can determine if the constellation of the digital signal is real-valued such as BPSK and 4-PAM, circular such as 8-PSK, or rectangular such as 16-QAM. It was found that 퐶̂42 could not be used to separate QPSK from

PSK of higher orders. Therefore, another fourth-order cumulant, 퐶̂40, is used to separate QPSK from other PSK signals of higher order. However, all PSK signals with order above eight were still indistinguishable from each other. At 10 dB of SNR, this approach achieved 96.85% accuracy when used to classify BPSK, 4-PAM, 16-QAM, and 8-PSK.

3.5 k-Nearest Neighbor Algorithm

After extracting signal features, a decision tree needs to be designed and tested to apply the signal features to classify the modulation schemes in question. Each scheme may lead to different signal feature values therefore, different thresholds need to be defined for each feature to separate different modulation schemes. This requires a significant amount of data and pattern recognition by the developer to create an AMC with consistently accurate performance. In many cases, if newer modulation schemes were to be incorporated, a decision tree with new thresholds would have to be designed and tested. All of these tasks can be simplified or even eliminated with machine learning algorithms such as k-nearest neighbor (k-NN). By supplying the algorithm with x number of feature sets for all modulation schemes in question, the system can be trained to classify a signal by comparing how close the unknown signal is to trained data sets. The algorithm decides its nearest neighbor by distance and it is up to the developer to decide how that distance is calculated or weighted. The most common equation used for distance is the Euclidean distance between points. (3.8) 푁 2 푑(퐴, 퐵) = √∑(퐴푖 − 퐵푖) 푖=0 where N is the number of different features used in the AMC. A is the set of features collected from the unknown signal and B is a set of features collected from the trained data with known modulations. Having multiple sets of training data for each modulation scheme will help increase

13 accuracy but also increase computational complexity during training and operation. Once the distance is defined, the algorithm can be done in four simple steps: First, calculate the distance between the unknown signal and all trained data sets, sort the results in ascending order, look at the k lowest results and extract the modulation schemes, determine the mode of the extracted modulation schemes. The modulation scheme that occurs the most is the classification decision for the unknown signal.

3.6 Summary

Several AMC concepts used for classifying modulated signals have been introduced in this chapter including spectral-based features, wavelet transform features, fourth-order cumulant features, 푓 decision trees, and k-NN. In this research, the spectral-based feature 휇42 and the fourth-order cumulant 퐶̂42 will be used to cover a range of analog and digital signals. AM and FM are both ubiquitous in terms of analog modulation schemes making them the perfect choice to test the system’s ability to detect analog schemes. For the same reason, PSKs and QAMs use in many house hold applications, strikes a great balance between analog and digital schemes. Both PSK and QAM are of the same order; 16 symbols are used to test the system’s ability to differentiate between two similar digital signals. Hence, the modulation schemes under consideration are AM, FM, 16-PSK, and 16-QAM.

Chapter 4

Experimentation

This chapter details the experimental setup implemented to reach a functional AMC. Section 4.1 explains the decision tree used for modulation classification in detail. Section 4.2 details how the AMC tool works. Finally, in section 4.3, an explanation of the tests conducted to determine the systems accuracy, versatility, robustness, and computational efficiently is given. The hardware devices used in this thesis are two National Instruments NI USRP-2920 software- defined radios. One operates as the blind AMC, and the other is used for training the system. Both are controlled by a Windows computer with LabVIEW 2014. While less expensive solutions like the USRP1 and RTL-SDR exist, it was concluded that this configuration provides a great balance between cost, radio features, and ease of use. The NI USRP-2920 has a frequency range from 50 MHz to 2.2 GHz and has an instantaneous bandwidth of 20 MHz. Additional hardware specifications can be found in Table 4.1. The NI USRP-2920 communicates via Ethernet instead of USB 2.0 to allow for a higher sampling rate. USB 2.0 has a data bottleneck of 8 mega samples per second (MS/s) at half duplex in the USRP1 [23], while Ethernet allows for 25 MS/s at full duplex in the NI USRP-2920 [24]. The full duplex feature allows both NI USRP-2920s to be daisy chained together with a National Instruments proprietary MIMO cable and controlled by a single computer as seen in Fig. 4.1.

4.1 Modulation & Decision Tree

This thesis focuses on the two analog and two digital modulation schemes mentioned in Chapter 2. AM is generated with a modulation index of 0.7 and FM with a frequency deviation of 30 kHz.

Figure 4.1 Daisy chained NI USRP-2920, interface via Ethernet

Figure 4.2 Decision tree for AM, FM, 16-PSK, and 16-QAM TABLE 4.1 NI USRP-2920 Specifications Frequency Range 50 MHz to 2.2 GHz Max Instantaneous Bandwidth 20 MHz Max I/Q Sampling Rate 25 MS/s DAC 400 MS/s ADC 100 MS/s Max Output Power level 20 dBm

The digital signals PSK and QAM are generated with 16 symbols, 4 samples per symbols, and a sampling rate of 50 kS/s. In practice, 16-QAM will always be picked over 16-PSK as the distance between constellation points make QAM less susceptible to incorrect demodulation. However, to demonstrate that the blind AMC can differentiate between PSK and QAM, both modulation schemes will be modulated using the same number of symbols and samples per symbol. All schemes will also have a bandwidth of 200 kHz. 푓 As the decision tree in Fig. 4.2 shows, 휇42 is used to identify AM signals that ideally have no variance in instantaneous frequency compared to FM, PSK, and QAM. This leads to AM having 푓 a compact distribution and producing a significantly larger 휇42 value, making it easy to distinguish 푓 from the rest. The 휇42 feature also distinguishes FM from PSK and QAM, as the distribution of FM signals is slightly more compact than that of the two digital signals. With an I/Q rate of 200

TABLE 4.2 DECISION TREE THRESHOLD CALCULATIONS USING AVERAGE TRAINING DATA Modulation Decision 휇푓 (퐴푀) + 휇푓 (퐹푀) AM 휇푓 > 42 42 42 2 휇푓 (퐴푀) + 휇푓 (퐹푀) 휇푓 (퐹푀) + 휇푓 (푃푆퐾) FM 42 42 > 휇푓 > 42 42 2 42 2 Ĉ (푃푆퐾16) + Ĉ (푄퐴푀16) 16-PSK 42 42 > Ĉ 2 42 Ĉ (푃푆퐾16) + Ĉ (푄퐴푀16) 16-QAM Ĉ > 42 42 42 2 kHz, the rs used in equation 3.3 is set to 50 kHz, equivalent to the symbol rate of the generated digital signals. Ĉ42 is then used to characterize the constellation shape of a digital signal. In this case, it is used to differentiate the lower-valued PSK signals with a circular constellation from QAM with a rectangular constellation.

4.2 AMC Walkthrough Guide

The control software developed for the AMC was created in LabVIEW and the procedure for using the software is described in this section. The blind AMC employs an energy detector to identify the existence of signals as described in Chapter 3.1. The detection threshold is set in four steps. The user first defines the receiver’s carrier frequency so data from a spectrum is without signals, just noise. The data is then multiplied with a Blackman window and the resulting energy is calculated. This procedure is repeated a thousand times so a probability density function (PDF) of the noise energy can be estimated. The detection threshold is then set to be six times the noise energy’s standard deviation which yields a false alarm −9 푓 ̂ rate of 2×10 . The system also requires 휇42 and 퐶42 training data from AM, FM, 16-PSK, and 16-QAM signals. The characteristics of the antennas used may conceivably be frequency-dependent, so multiple training sets at different frequencies are recommended. For this thesis, training data is obtained at carrier frequencies of 100 MHz, 1 GHz, and 2 GHz. The carrier frequency and the number of points to average can be defined by the user. By default, the training data is obtained through averaging 300 measurements. 푓 Table 4.2 shows how each signal is classified and calculated where values such as 휇42(퐴푀) represents the 300 measurement average of the trained AM signal. A threshold is set as the mean 17

푓 of two adjacent modulation schemes as shown previously in Fig 4.2. If the resulting unknown 휇42 푓 value is above the mean between AM’s and FM’s 휇42, the signal is AM. If the signal is not an AM 푓 signal, the mean between FM’s and PSK’s 휇42 is used to determine if the unknown is FM. The 푓 휇42 value for PSK and QAM are near identical because of the same number of symbols and samples per symbol used. So the mean between QAM’s and PSK’s Ĉ42 is used to find PSK signals below the QAM threshold. Once all of the training data is collected, the blind AMC is finally ready to run. The user needs to enter the range of center frequencies they are interested in and the increment of the carrier frequency shift when sweeping the spectrum. The AMC will first sweep across the spectrum and identify signals by comparing the received signal energy with the noise detection threshold. As soon as a signal is detected, the Blackman windowed signal is run through the classification decision tree. By default, the system collects and averages 100 signal feature measurements instead of the 300 used in training to reduce computational time. The system also employs the use of an automatic gain controller to bring the received signal power to 20 dBm to alleviate the difference in signal power from signals generated at different locations and the testing signals used to generate the decision thresholds. The user interface updates with a list of carrier frequencies and the classified modulation scheme in real time, as the information is found and calculated.

4.3 Experiments

The developed AMC needs to be accurate, robust, computationally efficient, and versatile. This section describes several experiments used to evaluate the effectiveness of the developed system.

4.3.1 Spectrum Scan and Signal Detection

In the first experiment, three USRP-2920s were used to transmit FM, 16-PSK, and 16-QAM at 900 MHz, 1 GHz, and 1.1 GHz respectively. One was placed next to the AMC and the other two placed 10 ft. away. The system scanned through 800 MHz to 1.2 GHz with frequency shifts of 500 kHz. Before any testing began, a preliminary scan did not detect any externally broadcasted signals that may interfere with the test. Hence, a frequency shift of 500 kHz was used instead of a smaller value to expedite the process.

4.3.2 Decision Tree Validation

In the second experiment, a USRP-2920 located next to the AMC receiver was used as the transmitter and broadcasts AM, FM, 16-PSK, and 16-QAM signals. After training the system at 100 MHz, 1 GHz, and 2 GHz each modulation scheme was classified 100 times at all three frequencies. The results will verify how robust the decision tree is by determining if it can function at different frequencies.

4.3.3 Over-the-air FM Classification

The third experiment calls for the system to be connected to an antenna on the roof of the engineering building to detect signals from nearby FM radio stations. The detection threshold needs to be rederived as a different antenna is used in a different environment. The modulation threshold values however, will be recycled from the 100 MHz over-the-air (OTA) experiment in the lab. The system then scans through the commercial FM radio spectrum in an attempt to detect and classify over-the-air FM signals. This will demonstrate the versatility of the decision tree and verify the system in real world applications.

4.3.4 SNR Testing

To measure the accuracy of the AMC system’s performance in the presence of noise, the AMC receiver and transmitter were connected with an SMA coaxial cable and Gaussian noise was added to the transmitted signals. Each scheme was classified 400 times at 10, 15, 16, 18, and 20 dB SNR. The values of each feature are also measured at an SNR ranging from 0 to 20 dB to observe the impact of noise as SNR decreases.

4.3.5 Runtime

As other experiments were being conducted, the time the system takes to obtain the detection threshold with 1000 measurements, achieve a classification with 300 measurements and the time needed to detect and classify a signal were all recorded. All of the timing information will be used to evaluate the AMC systems computational efficiency.

Chapter 5

Results

This chapter presents and discusses the results of the five experiments described in the previous chapter to determine if the AMC is accurate, versatile, robust, and computational efficient.

5.1 Spectrum Scan and Signal Detection

Fig. 5.1 shows a screen shot of the user interface of the AMC system with the noise threshold, unknown wireless signals with energies above the noise threshold, carrier frequency of these signals, and the modulation classification of the signals. AM, FM, 16-PSK, and 16-QAM are identified as 1, 2, 3, and 4 respectively. All three transmitted signals were detected and correctly classified. The first line identifies the FM signal at 900 MHz, and its signal energy is very much above the noise threshold as expected since the FM transmitter is only six inches away from the AMC. Both 16-PSK’s and 16-QAM’s energies are notably greater than the noise threshold and the transmitters are 10 ft. away from the AMC receiver. This shows the energy detector is robust enough to detect signals of different types and at different distances.

5.2 Decision Tree Validation

푓 Fig. 5.2, 5.4, and 5.6 show the wireless calculated 휇42 values of all four signal types considered in this thesis, transmitted at 2 GHz and 100 MHz over 100 measurements respectively. As shown in 푓 these three figures, the values of 휇42 were unaffected by the carrier frequency as all three tested 푓 ̂ carrier frequencies led to similar 휇42 values. On the other hand, values of 퐶42 are frequency dependent as shown in Fig. 5.3, 5.5, and 5.7.

Figure 5.1 User interface identifies carrier frequency and modulation scheme

Figure 5.2 100 calculated values of UF42 at 2 GHz Figure 5.3 100 calculated values of C42 at 2 GHz

Figure 5.4 100 calculated values of UF42 at 1 GHz Figure 5.5 100 calculated values of C42 at 1 GHz

Figure 5.6 100 calculated values of UF42 at 100 MHz Figure 5.7 100 calculated values of C42 at 100 MHz

TABLE 5.1 TABLE 5.2 TABLE 5.3 Correct Classification Correct Classification Correct Classification at 2 GHz and 100 Realizations at 1 GHz and 100 Realizations at 100 MHz and 100 Realizations PSK QAM PSK QAM PSK QAM TX \ RX AM FM TX \ RX AM FM TX \ RX AM FM 16 16 16 16 16 16 AM 100 0 0 0 AM 100 0 0 0 AM 100 0 0 0

FM 0 100 0 0 FM 0 100 0 0 FM 0 100 0 0 PSK PSK PSK 0 0 100 0 0 0 100 0 0 0 99 1 16 16 16 QAM QAM QAM 0 0 0 100 0 0 0 100 0 0 0 100 16 16 16

These figures show the 퐶̂42 values of 16-PSK and 16-QAM at 2 GHz, 1 GHz and 100 MHz. While the proposed decision tree still holds since 16-QAM consistently generates a higher value of 퐶̂42 than 16-PSK, a different threshold value needs to be used at different carrier frequencies. A possible explanation is the frequency-dependent characteristics present in the receiver components. For instance, as mentioned in Chapter 4.3, the tri-band antennas used in this thesis have frequency-dependent characteristics that could contribute to the different values observed in Fig. 5.3, 5.5, and 5.7. When the system was tested at 100 MHz, 1 GHz, and 2 GHz, Tables 5.1 – 5.3 show how accurately the system can classify every signal transmitted. From these results, the AMC can classify the four modulations considered in this thesis with an accuracy of 99.9%.

Figure 5.8 100 calculated values of UF42 at 88.5 MHz over-the-air

5.3 Over-the-air FM Classification

When using the rooftop antenna to detect over-the-air radio stations, the radio station WMUB- 88.5, a nearby radio station, was identified. It is worth noting that due to the location of the lab it is the only FM radio station that can be detected with the current detection threshold setting. The carrier frequency of this FM signal is 88.5 MHz. The decision thresholds determined from the 100 푓 MHz training are used, and Fig. 5.8 graphs the calculated values of 휇42 for the detected OTA FM 푓 signal. While a majority of the calculated 휇42 values fall within the range of an FM classification, it was observed to have a much higher variance than the indoor FM signals generated in the lab. The resulting probability of correct detection is 97%.

5.4 SNR Testing

In this test, the four different modulated signals are transmitted at 2 GHz over an SMA cable at different SNR settings, and the AMC attempts to classify the received signal. The experiment is repeated 400 times for each of the four schemes at SNR of 10, 15, 16, 18 and 20 dB, and the percentage of correct classification is graphically shown in Fig. 5.9.

Figure 5.9 400 point correct classification at 10, 15, 16, 18 and 20 dB

Figure 5.10 2 GHz single UF42 classification at 0 - 20 dB Figure 5.11 2 GHz single C42 classification at 0 - 20 dB

This figure shows that the system correctly classifies signals 99.13% of the time at 20 dB of SNR, 98.93% at 18 dB, and 99.5% at 16 dB. The slight fluctuation in percentages of correct classification can be attributed to the fact that only 400 measurements were taken for each modulation. However, when the SNR drops to 15 dB, system performance deteriorates significantly: the classification rate drops to 61.06%, and at SNR of 10 dB the correct classification rate is only 30.25%, both of which are unacceptable for communication systems. It can be concluded that the system operates with acceptable results at a minimum of 16 dB of SNR. It should also be noted that SNR tests were done without the energy detector enabled, meaning the AMC tried to classify a signal regardless of the received signal’s energy. In the experiments using over-the-air signals, if a signal is too weak, the AMC would not consider classifying the signals as their signal energy would not be greater than the detection threshold. For the second part of this experiment, Fig. 5.10 shows the impact of Gaussian noise on 푓 푓 the 휇42 signal feature. The value of 휇42 for four types of signals are measured at different SNR

푓 that ranges from 0 dB to 20 dB in increments of 1 dB. As expected, AM’s 휇42 deteriorates the fastest as it is much more susceptible to noise. The AM signal requires an SNR of at least 16 dB to be correctly classified which coincides with the 400 measurement test. The FM signal needs approximately 12 dB of SNR to be separated from the digital signals, 16-PSK and 16-QAM. 푓 Although 휇42 can be used to differentiate the digital signals from AM and FM at 12 dB of SNR, Fig. 5.11 shows a minimum of 14 dB of SNR is needed to separate 16-PSK from 16-QAM. Taking into account that the calculated results shown in Fig. 5.10 and 5.11 are the result of single measurements, not an average; the SNR requirements drawn from these figures may not be exact. For example, it can be seen that 16-PSK can be correctly classified 50% of the time at an SNR of

15 dB based on the data used to generate Fig. 5.9. Another interesting observation is the 퐶̂42 values for both 16-PSK and 16-QAM increasing in value as SNR decreases. This explains why 16-QAM can be easily identified in Fig. 5.9. As SNR decreased, 퐶̂42 values of both 16-PSK and 16-QAM would increase and both signals would be classified as 16-QAM.

5.5 Runtimes

Table 5.4 shows the recorded runtimes of the system. Sixteen minutes to determine a noise threshold is a considerable amount of time. However, Fig. 5.12 shows that the PDF of the noise energy obtained from 1,000 measurements is necessary to confirm a Gaussian distribution of noise. Determining the decision threshold for modulation classification from 300 measurements takes 2 minutes. From signal detection to classification, the system takes 41 seconds and 100 measurements are taken to classify one detected signal. Unfortunately, in electronic warfare applications where knowledge is key, an ideal AMC would operate with strict real time constraints. Even assuming noise and modulation training was done beforehand, 41 seconds may not be fast enough when lives could be on the line. A cognitive radio application may be more appropriate as communication techniques such as retransitions could be used to help the receiver pickup information it may have missed while classifying the signal.

TABLE 5.4 System Runtimes

TEST Time

NOISE THRESHOLD 16 Minutes

MODULATION TRAINING 2 Minutes Each

DETECT & CLASSIFY 41 Seconds

Figure 5.12 Signal energy PDF of 957 MHz, a frequency with only noise

Chapter 6

Conclusion & Future Work

A feature-based SDR AMC that can classify AM, FM, 16-PSK and 16-QAM signals was developed. The developed AMC scans through a broad spectrum, detecting and classifying modulation schemes using just two signal features. To the best of the author’s knowledge, this is one of only a few, if any, papers on the implementation of an AMC implemented on an SDR, and the results presented in this thesis provide useful information to researchers who would like to develop and test AMC algorithms in a lab. The system’s ability to operate at lower SNRs and computational efficiency could still use some improvements: however, with the only prerequisite for the system to operate being training data, the AMC can easily be used to further EW and CR research. The work done in this project shows promising results in creating a working blind AMC prototype. Test results also help us further understand the extent of the systems capabilities. Correct classification at SNR of 15 dB was found to be 61.06% and 16 dB was observed to be the minimum SNR, at which AM could be differentiated from FM and digital signals. Since the performance of the AMC system depends on the SNR, an SNR estimator could be used to create a confidence score for each classification to minimize the number of misclassifications and increase the correct classification percentages in the presence of noise. Another area to study is the minimum number of measurements needed for modulation training and classification. In this thesis, 1,000 measurements were used to determine the noise threshold, 300 measurements were used for the modulation decision threshold, and 100 measurements were used to obtain accurate signal features for modulation classification. It could be argued that more measurements lead to more accurate results. However, if the system is to be

27 used in real time environments, the number of measurements needs to be reduced to speed up the classification process. The signals used in this project all have a bandwidth of 200 kHz, the same as the AMC’s channel bandwidth. For signals with a larger bandwidth, the energy detector could potentially claim a signal is present when measuring energy frequencies near the center of a signal with a broad bandwidth and claim the existence of multiple signals. To alleviate this risk, a peak finding algorithm should be implemented to identify the peaks over a broad spectrum to ensure only the center frequency of a signal is detected. The k-Nearest Neighbor machine learning algorithm described in Chapter 3.6 can be an alternative to the modulation decision tree used in this thesis. Using multiple features for the algorithm could be beneficial in distinguishing modulation schemes that are currently difficult to classify with a decision tree because of similar values. For example, PSK of different orders have similar 퐶̂42 values because all PSK signals have a circular constellation shape. If other features and modulation schemes were to be considered, the k-Nearest Neighbor algorithm can be used to automatically determine a correlation between features and modulation schemes. This would reduce the computational complexity and time needed for classifications in addition to reducing the time needed to develop and test a new decision tree. At the time of writing this thesis, National Instruments recently released LabVIEW Communications System Design Suite 1.0 that is specifically designed for software-defined radios to stream line the process of developing communication systems on the USRP. If a new algorithm or a rework of the system is to be done, the new design suite should be considered as it is free for institutions that already have a LabVIEW license.

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