Towards low-complexity wireless technology classification across multiple environments

Fontaine, Jaron; Fonseca, Erika; Shahid, Adnan; Kist, Maicon; DaSilva, Luiz A.; Moerman, Ingrid; Poorter, Eli De

doi:10.1016/j.adhoc.2019.101881

Cited by 34 publications

(29 citation statements)

References 32 publications

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“…The validation was carried out utilizing commercially available LTE and WiFi hardware. Similarly, CNN based models are used to perform identification of WiFi transmissions from other co-located transmissions of other technologies in [27,28]. Our CNN based technology classification proposed in [26] is used to implement coexistence schemes between private LTE and WiFi in [29,30].…”

Section: Coexistence In Uncoordinated Lte and Wifi Networkmentioning

confidence: 99%

Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning

Girmay

Maglogiannis

Naudts

et al. 2021

Sensors

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Nowadays, broadband applications that use the licensed spectrum of the cellular network are growing fast. For this reason, Long-Term Evolution-Unlicensed (LTE-U) technology is expected to offload its traffic to the unlicensed spectrum. However, LTE-U transmissions have to coexist with the existing WiFi networks. Most existing coexistence schemes consider coordinated LTE-U and WiFi networks where there is a central coordinator that communicates traffic demand of the co-located networks. However, such a method of WiFi traffic estimation raises the complexity, traffic overhead, and reaction time of the coexistence schemes. In this article, we propose Experience Replay (ER) and Reward selective Experience Replay (RER) based Q-learning techniques as a solution for the coexistence of uncoordinated LTE-U and WiFi networks. In the proposed schemes, the LTE-U deploys a WiFi saturation sensing model to estimate the traffic demand of co-located WiFi networks. We also made a performance comparison between the proposed schemes and other rule-based and Q-learning based coexistence schemes implemented in non-coordinated LTE-U and WiFi networks. The simulation results show that the RER Q-learning scheme converges faster than the ER Q-learning scheme. The RER Q-learning scheme also gives 19.1% and 5.2% enhancement in aggregated throughput and 16.4% and 10.9% enhancement in fairness performance as compared to the rule-based and Q-learning coexistence schemes, respectively.

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Section: Coexistence In Uncoordinated Lte and Wifi Networkmentioning

confidence: 99%

Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning

Girmay

Maglogiannis

Naudts

et al. 2021

Sensors

Self Cite

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“…Authors in [33] demonstrate the use of CNNs to identify and classify LTE and Wi-Fi transmissions, and use the neural network's output to improve the coexistence between the two wireless technologies. Finally, authors in [34] classify between LTE, Wi-Fi and DVB-T technologies using multiple ML algorithms, such as a fully-connected neural network (FNN), random forest decision trees and a CNN to investigate the complexity trade-offs between manual and automatic feature extraction. Table 1 provides an overview of recent literature in the field of radio access technology classification using machine learning, the classified signals, input data, used models, and their classification metrics.…”

Section: A Related Workmentioning

confidence: 99%

Towards Enhancing Spectrum Sensing: Signal Classification Using Autoencoders

2021

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The demand for technologies relying on the radio spectrum, such as mobile communications and IoT, has been growing exponentially. As a consequence, providing access to the radio spectrum is becoming increasingly more important. The ever-growing wireless traffic and the increasing scarcity of available spectrum warrants efficient management of the radio spectrum. At the same time, machine learning (ML) is becoming ubiquitous and has found applications in many fields for its ability to identify patterns and assist with decision-making processes. Recently, machine learning algorithms have been used to address challenges in the wireless communications domain, such as radio spectrum sensing, and have shown better performance than traditional sensing methods, such as energy detection. Spectrum sensing, a method for detecting and identifying different wireless signals being transmitted in the same band of the radio spectrum, is crucial for improving dynamic spectrum sharing, which has the potential to enhance sharing and coexistence of different wireless technologies in the same frequency band and ultimately improve spectrum efficiency. To this end, this research evaluates different types of autoencoders, such as deep, variational and Long Short-Term Memory (LSTM) autoencoders, to identify and differentiate between LTE and Wi-Fi transmissions. The goal is to investigate the performance of the different types of autoencoders on an I/Q dataset consisting of LTE and a combination of Wi-Fi signals (IEEE 802.11ax and IEEE 802.11ac) for the classification task in terms of complexity, precision, and recall to identify the best algorithm. Our models have achieved up to 99.9% precision and 88.1% recall for this classification task. Additionally, with a shortest training time of approximately 47 seconds, the models are suitable for online learning and deployment in a dynamic RF environment.

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“…In related work, the authors of [6] use deep neural network (DNN) and CNN for classification of Wi-Fi, DVB-T and LTE, and targets operation in multiple environments. The proposed models achieve an accuracy of 98%, while still achieving 85% accuracy in low signal-to-noise ratio (SNR) scenarios (0 dB).…”

Section: Related Workmentioning

confidence: 99%

“…For the FFT case, the IQ data is transformed into its equivalent real and imaginary frequency domain representation using FFT. This transformation produces a more robust model especially in low SNR environments [6].…”

Section: A Input Sample Descriptionmentioning

confidence: 99%

Multi-band sub-GHz technology recognition on NVIDIA’s Jetson Nano

Fontaine

Shahid

Elsas

et al. 2020

2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall)

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Low power wide area networks support the success of long range Internet of things applications such as agriculture, security, smart cities and homes. This enormous popularity, however, breeds new challenging problems as the wireless spectrum gets saturated which increases the probability of collisions and performance degradation. To this end, smart spectrum decisions are needed and will be supported by wireless technology recognition to allow the networks to dynamically adapt to the ever changing environment where fair coexistence with other wireless technologies becomes essential. In contrast to existing research that assesses technology recognition using machine learning on powerful graphics processing units, this work aims to propose a deep learning solution using convolutional neural networks, cheap software defined radios and efficient embedded platforms such as NVIDIA's Jetson Nano. More specifically, this paper presents low complexity near-real time multi-band sub-GHz technology recognition and supports a wide variety of technologies using multiple settings. Results show accuracies around 99%, which are comparable with state of the art solutions, while the classification time on a NVIDIA Jetson Nano remains small and offers real-time execution. These results will enable smart spectrum management without the need of expensive and high power consuming hardware. Index Terms-Sub-GHz, deep learning, Software-defined radio, low-cost devices I. INTRODUCTION The Internet of things (IoT) paradigm has grown exponentially in the past decade and continues this trend into the foreseeable future. At the beginning of 2020, IoT Analytics estimated that 9.5 billion devices are connected to the Internet and forecasts a growth of 28 billion devices by 2025 [1]. This is due to the proliferation of various IoT application areas such as security, tracking, agriculture, smart metering, smart cities and smart homes. To accommodate commercial deployment of such large number of devices, recently a number of IoT technologies were developed which are called low power wide area networks (LPWANs). These technologies offer very long communication ranges allowing to connect a large number of devices using limited infrastructure cost (e.g. by installing a small number of gateways). Example technologies include Sigfox, LoRA, IEEE 802.11ah, IEEE 802.11g, Dash7, Weightless, etc. These technologies operate in unlicensed sub-GHz band, typically 868 MHz in Europe and 915 MHz in North America. These radio frequencies offer good object penetration performance and can be used for a long range of communication, i.e., up to 15 km (for LoRa) [2]. However, due to

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Towards low-complexity wireless technology classification across multiple environments

Cited by 34 publications

References 32 publications

Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning

Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning

Towards Enhancing Spectrum Sensing: Signal Classification Using Autoencoders

Multi-band sub-GHz technology recognition on NVIDIA’s Jetson Nano

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