Enhancing the Coexistence of LTE and Wi-Fi in Unlicensed Spectrum Through Convolutional Neural Networks

Maglogiannis, Vasilis; Shahid, Adnan; Naudts, Dries; Poorter, Eli De; Moerman, Ingrid

doi:10.1109/access.2019.2902035

Cited by 44 publications

(43 citation statements)

References 25 publications

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“…Simulation results show how ML can assist LTE-U in finding optimal configurations and adapt to changes of the wireless environment thus providing the desired fair coexistence. Authors in [28] propose a convolutional neural network (CNN) that is trained to perform identification of LTE and Wi-Fi transmissions which can also identify the hidden terminal effect caused by multiple LTE transmissions, multiple Wi-Fi transmissions, or concurrent LTE and Wi-Fi transmissions. The designed CNN has been trained and validated using commercial off-the-shelf LTE and Wi-Fi hardware equipment.…”

Section: ML As Applied To Lte Wi-fi Coexistencementioning

confidence: 99%

Machine Learning Enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios

Dziedzic

Sathya

Rochman

et al. 2020

IEEE Open J. Veh. Technol.

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1 Abstract-The application of Machine Learning (ML) techniques to complex engineering problems has proved to be an attractive and efficient solution. ML has been successfully applied to several practical tasks like image recognition, automating industrial operations, etc. The promise of ML techniques in solving non-linear problems influenced this work which aims to apply known ML techniques and develop new ones for wireless spectrum sharing between Wi-Fi and LTE in the unlicensed spectrum. In this work, we focus on the LTE-Unlicensed (LTE-U) specification developed by the LTE-U Forum, which uses the duty-cycle approach for fair coexistence. The specification suggests reducing the duty cycle at the LTE-U base-station (BS) when the number of co-channel Wi-Fi basic service sets (BSSs) increases from one to two or more. However, without decoding the Wi-Fi packets, detecting the number of Wi-Fi BSSs operating on the channel in real-time is a challenging problem. In this work, we demonstrate a novel ML-based approach which solves this problem by using energy values observed during the LTE-U OFF duration. It is relatively straightforward to observe only the energy values during the LTE-U BS OFF time compared to decoding the entire Wi-Fi packet, which would require a full Wi-Fi receiver at the LTE-U base-station. We implement and validate the proposed ML-based approach by real-time experiments and demonstrate that there exist distinct patterns between the energy distributions between one and many Wi-Fi AP transmissions. The proposed ML-based approach results in a higher accuracy (close to 99% in all cases) as compared to the existing auto-correlation (AC) and energy detection (ED) approaches.

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Section: ML As Applied To Lte Wi-fi Coexistencementioning

confidence: 99%

Machine Learning Enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios

Dziedzic

Sathya

Rochman

et al. 2020

IEEE Open J. Veh. Technol.

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“…In our previous work [16], a detailed review of Artificial Intelligence (AI)/Machine Learning (ML) based coexistence studies was performed and a Convolutional Neural Network (CNN) that identifies real time over-the-air LTE and Wi-Fi transmissions was developed. Several statistics of co-located technologies are used in order to select the appropriate transmission and muting period of an adaptive LTE scheme.…”

Section: Related Workmentioning

confidence: 99%

Adaptive CNN-based Private LTE Solution for Fair Coexistence with Wi-Fi in Unlicensed Spectrum

Girmay

Maglogiannis

Naudts

et al. 2020

IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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Recently, the expansion of wireless network deployments is resulting in increased scarcity of available licensed radio spectrum. As the domain of wireless communications is progressing rapidly, many industries are looking into wireless network solutions that can increase their productivity. Private LTE is a promising wireless network solution as it can be customised independently without the control of a mobile network operator while providing reliable and spectrum efficient services. For this reason, the deployment of Private LTE in the unlicensed spectrum and its coexistence with Wi-Fi is becoming a popular topic in research. In this paper, we propose a coexistence scheme for private LTE network in unlicensed spectrum that enables a fair spectrum sharing with co-located Wi-Fi networks. This is achieved by exploiting various LTE frame configurations consisting of different combinations of downlink, uplink, special subframe and muted subframes. The configuration of a single frame is decided based on a rule based algorithm that exploits Wi-Fi spectrum occupancy statistics that is obtained from a technology recognition system which is based on a Convolutional Neural Network. The performance of the proposed private LTE scheme and its coexistence with Wi-Fi is investigated for different traffic scenarios showcasing how the proposed scheme can lead to a harmless coexistence of LTE and Wi-Fi.

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“…Schmidt et al [13] present a CNN based approach for classification of various configurations of technologies such as IEEE 802.11 b/g, IEEE 802.15.4 and IEEE 802.15.1 operating in 2.4 GHz ISM band using IQ samples. In previous work [14], the authors propose a CNN approach for classification of different variants of technologies (LTE and WiFI) and provide a mechanism so that the two technologies can coexist in a fair manner. All the works focus on classification of technologies in 2.4 GHz industrial, scientific and medical (ISM) and the propositions cannot be directly applicable to LPWAN technologies because of their different characteristics such as long packet durations, narrow bandwidths, and different modulation schemes.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 5 shows the CNN structure for the proposed classifiers and is based on [8]. We have also used a similar structure for the classification of different Long Term Evolution (LTE) and WiFi signal classes [14]. The structure comprises of three convolutional and pooling layers followed by two fully connected layers.…”

Section: A Time and Frequency Snapshotsmentioning

confidence: 99%

A Convolutional Neural Network Approach for Classification of LPWAN Technologies: Sigfox, LoRA and IEEE 802.15.4g

Shahid

Fontaine

Camelo

et al. 2019

2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)

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This paper presents a Convolutional Neural Network (CNN) approach for classification of low power wide area network (LPWAN) technologies such as Sigfox, LoRA and IEEE 802.15.4g. Since the technologies operate in unlicensed sub-GHz bands, their transmissions can interfere with each other and significantly degrade their performance. This situation further intensifies when the network density increases which will be the case of future LPWANs. In this regard, it becomes essential to classify coexisting technologies so that the impact of interference can be minimized by making optimal spectrum decisions. State-of-the-art technology classification approaches use signal processing approaches for solving the task. However, such techniques are not scalable and require domain-expertise knowledge for developing new rules for each new technology. On the contrary, we present a CNN approach for classification which requires limited domain-expertise knowledge, and it can be scalable to any number of wireless technologies. We present and compare two CNN based classifiers named CNN based on in-phase and quadrature (IQ) and CNN based on Fast Fourier Transform (FFT). The results illustrate that CNN based on IQ achieves classification accuracy close to 97% similar to CNN based on FFT and thus, avoiding the need for performing FFT.

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Enhancing the Coexistence of LTE and Wi-Fi in Unlicensed Spectrum Through Convolutional Neural Networks

Cited by 44 publications

References 25 publications

Machine Learning Enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios

Machine Learning Enabled Spectrum Sharing in Dense LTE-U/Wi-Fi Coexistence Scenarios

Adaptive CNN-based Private LTE Solution for Fair Coexistence with Wi-Fi in Unlicensed Spectrum

A Convolutional Neural Network Approach for Classification of LPWAN Technologies: Sigfox, LoRA and IEEE 802.15.4g

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