An SNR estimation based adaptive hierarchical modulation classification method to recognize M-ary QAM and M-ary PSK signals

Ghodeswar, Shweta; Poonacha, P.G.

doi:10.1109/icscn.2015.7219867

Cited by 19 publications

(10 citation statements)

References 9 publications

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“…Most of existing work is based on traditional low-dimensional machine learning [240,241,242,243,244], which requires (i) extraction and careful selection of complex features from the RF waveform (i.e., average, median, kurtosis, skewness, high-order cyclic moments, etc. ); and (ii) the establishment of tight decision bounds between classes based on the current application, which are derived either from mathematical analysis or by learning a carefully crafted dataset [245].…”

Section: Existing Workmentioning

confidence: 99%

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

et al. 2019

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The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before. For this reason, techniques such as spectrum sharing, dynamic spectrum access, extraction of signal intelligence and optimized routing will soon become essential components of the IoT wireless communication paradigm. In this vision, IoT devices must be able to not only learn to autonomously extract spectrum knowledge on-the-fly from the network but also leverage such knowledge to dynamically change appropriate wireless parameters (e.g., frequency band, symbol modulation, coding rate, route selection, etc.) to reach the network's optimal operating point. Given that the majority of the IoT will be composed of tiny, mobile, and energy-constrained devices, traditional techniques based on a priori network optimization may not be suitable, since (i) an accurate model of the environment may not be readily available in practical scenarios; (ii) the computational requirements of traditional optimization techniques may prove unbearable for IoT devices. To address the above challenges, much research has been devoted to exploring the use of machine learning to address problems in the IoT wireless communications domain. The reason behind machine learning's popularity is that it provides a general framework to solve very complex problems where a model of the phenomenon being learned is too complex to derive or too dynamic to be summarized in mathematical terms.This work provides a comprehensive survey of the state of the art in the application of machine learning techniques to address key problems in IoT wireless communications with an emphasis on its ad hoc networking aspect. First, we present extensive background notions of machine learning techniques. Then, by adopting a bottom-up approach, we examine existing work on machine learning for the IoT at the physical, data-link and network layer of the protocol stack. Thereafter, we discuss directions taken by the community towards hardware implementation to ensure the feasibility of these techniques. Additionally, before concluding, we also provide a brief discussion of the application of machine learning in IoT beyond wireless communication. Finally, each of these discussions is accompanied by a detailed analysis of the related open problems and challenges.

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Section: Existing Workmentioning

confidence: 99%

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

et al. 2019

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“…Learning-based radios are envisioned to be able to automatically infer the current spectrum status in terms of occupancy [27], interference [28] and malicious activities [29]. Most of the existing work is based on low-dimensional machine learning [18][19][20]30], which requires the cumbersome manual extraction of very complex, ad hoc features from the waveforms. For this reason, deep learning has been proposed as a viable alternative to traditional learning techniques [31].…”

Section: Related Workmentioning

confidence: 99%

“…(2) Creating Learning Architectures for the Embedded RF Domain. Recent advances in RF deep learning [11][12][13][14][15][16] have demonstrated that convolutional neural networks (ConvNets) may be applied to analyze RF data without feature extraction and selection algorithms [17][18][19][20]. Moreover, ConvNets present a number of characteristics (discussed in Section 3) that make them particularly desirable from a hardware implementation perspective.…”

Section: Introductionmentioning

confidence: 99%

PolymoRF

Restuccia

Melodia

2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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Today's wireless technologies are largely based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. To address this key issue, wireless receivers will need to (i) infer on-the-fly the physical-layer parameters currently used by transmitters; and if needed, (ii) change their hardware and software structures to demodulate the incoming waveform. In this paper, we introduce PolymoRF, a deep learning-based polymorphic receiver able to reconfigure itself in real time based on the inferred waveform parameters. Our key technical innovations are (i) a novel embedded deep learning architecture, called RFNet, which enables the solution of key waveform inference problems; (ii) a generalized hardware/software architecture that integrates RFNet with radio components and signal processing. We prototype PolymoRF on a custom software-defined radio platform, and show through extensive over-the-air experiments that PolymoRF achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible. CCS CONCEPTS • Hardware → Wireless devices.

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“…The usage of supervised machine learning techniques, moreover, has received a lot of attention over the last years, particularly the problem of modulation recognition [23][24][25]. Similar to radio fingerprinting, feature-based learning constitutes the majority of existing work [26][27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

DeepFIR: Addressing the Wireless Channel Action in Physical-Layer Deep Learning

Restuccia,

D'Oro,

Al-Shawabka

et al. 2020

Preprint

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Deep learning can be used to classify waveform characteristics (e.g., modulation) with accuracy levels that are hardly attainable with traditional techniques. Recent research has demonstrated that one of the most crucial challenges in wireless deep learning is to counteract the channel action, which may significantly alter the waveform features. The problem is further exacerbated by the fact that deep learning algorithms are hardly re-trainable in real time due to their sheer size. This paper proposes DeepFIR, a framework to counteract the channel action in wireless deep learning algorithms without retraining the underlying deep learning model. The key intuition is that through the application of a carefully-optimized digital finite input response filter (FIR) at the transmitter's side, we can apply tiny modifications to the waveform to strengthen its features according to the current channel conditions.We mathematically formulate the Waveform Optimization Problem (WOP) as the problem of finding the optimum FIR to be used on a waveform to improve the classifier's accuracy. We also propose a datadriven methodology to train the FIRs directly with dataset inputs. We extensively evaluate DeepFIR on a experimental testbed of 20 software-defined radios, as well as on two datasets made up by 500 ADS-B devices and by 500 WiFi devices and a 24-class modulation dataset. Experimental results show that our approach (i) increases the accuracy of the radio fingerprinting models by about 35%, 50% and 58%;(ii) decreases an adversary's accuracy by about 54% when trying to imitate other device's fingerprints by using their filters; (iii) achieves 27% improvement over the state of the art on a 100-device dataset;(iv) increases by 2x the accuracy of the modulation dataset.

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An SNR estimation based adaptive hierarchical modulation classification method to recognize M-ary QAM and M-ary PSK signals

Cited by 19 publications

References 9 publications

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

PolymoRF

DeepFIR: Addressing the Wireless Channel Action in Physical-Layer Deep Learning

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