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

Abstract

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.

A General Approach for Traffic Classification in Wireless Networks Using Deep Learning

“…One possible solution to the problem described above is that the GW 1 recognizes the radio technology that interferes and adapts its behavior accordingly [7], [32], [33]. However, this approach is not enough to increase spectrum efficiency as (i) the interfering/interfered technology may not include a mechanism to adapt or coexist, e.g., to change its frequency band to avoid the interference, and (ii) (iii) traffic prioritization can be only based on technology and not on application.…”

“…The design of the CNN architecture was based on some of our previous experiences solving classification tasks using raw spectrum data such as in [7], [10], [33], [34], [37], [39]. More precisely, we started with a 2D-CNN architecture that worked well in the task of TR with raw IQ samples (see Fig.…”

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“…To compare the performance of the proposed rule-based algorithm with machine learning (ML), we use a convolutional neural network (CNN), of which the architecture is shown in Figure 9. The CNN has shown tremendous performance in various classification problems such as image classification [74], modulation classification [75], and wireless technology classification [76][77][78]. In this work, the CNN is trained with three publicly available fall datasets presented in [79][80][81].…”

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