No / he automatic classification of digital modulated signals has been subject to extensive studies over the last decade, with numerous scholarly articles and research studies published. This paper provides an insightful guidance and discussion on the most practical approaches of automatic modulation classification (AMC) in cognitive radio (CR) using likelihood based (LB) statistical tests. It also suggests a novel idea of storing the known constellation sets on the receiver side using a look-up table (LUT) to detect the transmitted replica. Relevant performance measures with simulated comparisons in flat fading additive white Gaussian noise (AWGN) channels are examined. Namely, the average likelihood ratio test (ALRT), generalized LRT (GLRT) and hybrid LRT (HLRT) are particularly illustrated using linearly phase-modulated signals such as M-ary phase shift keying (MPSK) and quadrature amplitude modulation (MQAM). When the unknown signal constellation is estimated using the maximum likelihood (ML) method, results indicate that the HLRT performs well and near optimal in most situations without extra computational burden.

This thesis delves into the advancement of 5G portable test-nodes in wireless communication systems with cognitive radio capabilities, specifically addressing the critical need for dynamic spectrum sensing and awareness at the radio receiver through AI-driven automatic modulation classification. Our methodology is centered around the transformer encoder architecture incorporating a multi-head self-attention mechanism. We train our architecture extensively across a diverse range of signal-to-noise ratios (SNRs) from the RadioML 2018.01A dataset. We introduce a novel transformer-based architecture with a gated mechanism, designed as a runtime re-configurable automatic modulation classification framework, which demonstrates enhanced performance with low SNR RF signals during evaluation, an area where conventional methods have shown limitations, as corroborated by existing research. Our innovative single-model framework employs distinct weight sets, activated by varying SNR levels, to enable a gating mechanism for more accurate modulation classification. This advancement in automatic modulation classification marks a crucial step toward the evolution of smarter communication systems. / Master of Science / This thesis delves into the advancement of wireless communication systems, particularly in developing portable devices capable of effectively detecting and analyzing radio signals with cognitive radio capabilities. Central to our research is leveraging artificial intelligence (AI) for automatic modulation classification, a method to identify signal modulation types. We utilize a transformer-based AI model trained on the RadioML 2018.01A dataset. Our training approach is particularly effective when evaluating low-quality signals using a gating mechanism based on signal-to-noise ratios, an area previously considered challenging in existing research. This work marks a significant advancement in creating more intelligent and responsive wireless communication systems.

Yes / A rigorous model for automatic modulation classification (AMC) in cognitive radio (CR) systems is proposed in this paper. This is achieved by exploiting the Kalman filter (KF) integrated with an adaptive interacting multiple model (IMM) for resilient estimation of the channel state information (CSI). A novel approach is proposed, in adding up the squareroot singular values (SRSV) of the decomposed channel using the singular value decompositions (SVD) algorithm. This new scheme, termed Frobenius eigenmode transmission (FET), is chiefly intended to maintain the total power of all individual effective eigenmodes, as opposed to keeping only the dominant one. The analysis is applied over multiple-input multiple-output (MIMO) antennas in combination with a Rayleigh fading channel using a quasi likelihood ratio test (QLRT) algorithm for AMC. The expectation-maximization (EM) is employed for recursive computation of the underlying estimation and classification algorithms. Novel simulations demonstrate the advantages of the combined IMM-KF structure when compared to the perfectly known channel and maximum likelihood estimate (MLE), in terms of achieving the targeted optimal performance with the desirable benefit of less computational complexity loads.

Cyclostationarity feature-based (C-FB) detection and classification is a large field of research that has promising applications to intelligent receiver design. Cyclostationarity FB classification and detection algorithms have been applied to a breadth of wireless communication signals — analog and digital alike. This thesis reports on an investigation of existing methods of extracting cyclostationarity features and then presents a novel robust solution that reduces SNR requirements, removes the pre-processing task of estimating occupied signal bandwidth, and can achieve classification rates comparable to those achieved by the traditional method while based on only 1/10 of the observation time. Additionally, this thesis documents the development of a novel low order consideration of the cyclostationarity present in Continuous Phase Modulation (CPM) signals, which is more practical than using higher order cyclostationarity. Results are presented — through MATLAB simulation — that demonstrate the improvements enjoyed by FB classifiers and detectors when using robust methods of estimating cyclostationarity. Additionally, a MATLAB simulation of a CPM C-FB detector confirms that low order C-FB detection of CPM signals is possible. Finally, suggestions for further research and contribution are made at the conclusion of the thesis. / Master of Science

The current and future trends of modern wireless communication systems place heavy demands on fast data transmissions in order to satisfy end users’ requirements anytime, anywhere. Such demands are obvious in recent applications such as smart phones, long term evolution (LTE), 4 & 5 Generations (4G & 5G), and worldwide interoperability for microwave access (WiMAX) platforms, where robust coding and modulations are essential especially in streaming on-line video material, social media and gaming. This eventually resulted in extreme exhaustion imposed on the frequency spectrum as a rare natural resource due to stagnation in current spectrum management policies. Since its advent in the late 1990s, cognitive radio (CR) has been conceived as an enabling technology aiming at the efficient utilisation of frequency spectrum that can lead to potential direct spectrum access (DSA) management. This is mainly attributed to its internal capabilities inherited from the concept of software defined radio (SDR) to sniff its surroundings, learn and adapt its operational parameters accordingly. CR systems (CRs) may commonly comprise one or all of the following core engines that characterise their architectures; namely, adaptive coded modulation (ACM), automatic modulation classification (AMC) and spectrum sensing (SS). Motivated by the above challenges, this programme of research is primarily aimed at the design and development of new paradigms to help improve the adaptability of CRs and thereby achieve the desirable signal processing tasks at the physical layer of the above core engines. Approximate modelling of Rayleigh and finite state Markov channels (FSMC) with a new concept borrowed from econometric studies have been approached. Then insightful channel estimation by using Kalman filter (KF) augmented with interacting multiple model (IMM) has been examined for the purpose of robust adaptability, which is applied for the first time in wireless communication systems. Such new IMM-KF combination has been facilitated in the feedback channel between wireless transmitter and receiver to adjust the transmitted power, by using a water-filling (WF) technique, and constellation pattern and rate in the ACM algorithm. The AMC has also benefited from such IMM-KF integration to boost the performance against conventional parametric estimation methods such as maximum likelihood estimate (MLE) for channel interrogation and the estimated parameters of both inserted into the ML classification algorithm. Expectation-maximisation (EM) has been applied to examine unknown transmitted modulation sequences and channel parameters in tandem. Finally, the non-parametric multitaper method (MTM) has been thoroughly examined for spectrum estimation (SE) and SS, by relying on Neyman-Pearson (NP) detection principle for hypothesis test, to allow licensed primary users (PUs) to coexist with opportunistic unlicensed secondary users (SUs) in the same frequency bands of interest without harmful effects. The performance of the above newly suggested paradigms have been simulated and assessed under various transmission settings and revealed substantial improvements.

Radio Frequency Machine Learning (RFML) is the application of ML techniques to solve problems in the RF domain as an alternative to traditional digital-signal processing (DSP) techniques. Notable among these are the tasks of specific emitter identification (SEI), determining source identity of a received RF signal, and automated modulation classification (AMC), determining the modulation scheme of a received RF transmission. Both tasks have a number of algorithms that are effective on simulated data, but struggle to generalize to data collected in the real-world, partially due to the lack of available datasets upon which to train models and understand their limitations. This thesis covers the practical considerations for systems that can create high-quality datasets for RFML tasks, how variances from real-world effects in these datasets affect RFML algorithm performance, and how well models developed from these datasets are able to generalize and adapt across different receiver hardware platforms. Moreover, this thesis presents a proof-of-concept system for large-scale and efficient data generation, proven through the design and implementation of a custom platform capable of coordinating transmissions from nearly a hundred Software-Defined Radios (SDRs). This platform was used to rapidly perform experiments in both RFML performance sensitivity analysis and successful transfer between SDRs of trained models for both SEI and AMC algorithms. / Master of Science / Radio Frequency Machine Learning (RFML) is the application of machine learning techniques to solve problems having to do with radio signals as an alternative to traditional signal processing techniques. Notable among these are the tasks of specific emitter identification (SEI), determining source identity of a received signal, and automated modulation classification (AMC), determining the data encoding format of a received RF transmission. Both tasks have practical limitations related to the real-world collection of RF training data. This thesis presents a proof-of-concept for large-scale, efficient data generation and management, as proven through the design and construction of a custom platform capable of coordinating transmissions from nearly a hundred radios. This platform was used to rapidly perform experiments in both RFML performance sensitivity analysis and successful cross-radio transfer of trained behaviors.

One component of wireless communications of increasing necessity in both civilian and military applications is the process of automatic modulation classification. Modulation of a detected signal of unknown origin requiring interpretation must first be determined before the signal can be demodulated. This thesis presents a novel architecture for a modulation classifier that determines the most likely modulation using Grey Relational Analysis with the extraction and combination of multiple signal features. An evaluation of data preprocessing methods is conducted and performance of the classifier is investigated with the addition of each new signal feature used for classification. / Master of Science

Sensor Fusion is an essential framework in many Engineering fields. It is a relatively new paradigm for integrating data from multiple sources to synthesize new information that in general would not have been feasible from the individual parts. Within the wireless communications fields, many emerging technologies such as Wireless Sensor Networks (WSN), the Internet of Things (IoT), and spectrum sharing schemes, depend on large numbers of distributed nodes working collaboratively and sharing information. In addition, there is a huge proliferation of smartphones in the world with a growing set of cheap powerful embedded sensors. Smartphone sensors can collectively monitor a diverse range of human activities and the surrounding environment far beyond the scale of what was possible before. Wireless communications open up great opportunities for the application of sensor fusion techniques at multiple levels. In this dissertation, we identify two key problems in wireless communications that can greatly benefit from sensor fusion algorithms: Automatic Modulation Classification (AMC) and indoor localization and mapping based on smartphone sensors. Automatic Modulation Classification is a key technology in Cognitive Radio (CR) networks, spectrum sharing, and wireless military applications. Although extensively researched, performance of signal classification at a single node is largely bounded by channel conditions which can easily be unreliable. Applying sensor fusion techniques to the signal classification problem within a network of distributed nodes is presented as a means to overcome the detrimental channel effects faced by single nodes and provide more reliable classification performance. Indoor localization and mapping has gained increasing interest in recent years. Currently-deployed positioning techniques, such as the widely successful Global Positioning System (GPS), are optimized for outdoor operation. Providing indoor location estimates with high accuracy up to the room or suite level is an ongoing challenge. Recently, smartphone sensors, specially accelerometers and gyroscopes, provided attractive solutions to the indoor localization problem through Pedestrian Dead-Reckoning (PDR) frameworks, although still suffering from several challenges. Sensor fusion algorithms can be applied to provide new and efficient solutions to the indoor localization problem at two different levels: fusion of measurements from different sensors in a smartphone, and fusion of measurements from several smartphones within a collaborative framework. / Ph. D.

Recently, automatic modulation classification techniques using deep neural networks on raw IQ samples have been investigated and show promise when compared to more traditional likelihood-based or feature-based techniques. While likelihood-based and feature-based techniques are effective, making classification decisions directly on the raw IQ samples removes the need for expertly crafted transformations and feature extractions. In practice, RF environments are typically very dense, and a receiver must first detect and isolate each signal of interest before classification can be performed. The errors introduced by this detection and isolation process will affect the accuracy of deep neural networks making automatic modulation classification decisions directly on raw IQ samples. The importance of defining upper limits on estimation errors in a detector is highlighted, and the negative effects of over-estimating or under-estimating these limits is explored. Additionally, to date, most of the published research has focused on synthetically generated data. While large amounts of synthetically generated data is generally much easier to obtain than real-world signal data, it requires expert knowledge and accurate models of the real world, which may not always be realistic. The experiments conducted in this work show how augmented real-world signal captures can be successfully used for training neural networks used in automatic modulation classification on raw IQ samples. It is shown that the quality and duration of real world signal captures is extremely important when creating training datasets, and that signal captures made from a single transmitter with one receiver can be broadly applicable to other radios through dataset augmentation. / Master of Science

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