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Executive Summary By 2030, our societies will become digitalized and data-driven, enabled through the key verticals like connected industries, intelligent transport systems and smart cities. Machine Type Communication (MTC) encompassing its massive and critical aspects, and near instant unlimited wireless connectivity are among the main enablers of such digitalization at large. The recently introduced 5G New Radio is natively designed to support both aspects of MTC to promote the digital transformation of the society and particularly improve the overall efficiency of different vertical sectors. However, it is evident that some of the more demanding requirements of MTC cannot be fully supported by 5G networks. Alongside, further development of the society towards 2030 will give rise to new and more stringent requirements on wireless connectivity in general, and MTC in particular. Driven by the societal trends towards 2030, the next generation (6G) will be an agile and efficient convergent network serving a set of diverse service classes and a wide range of key performance indicators (KPI). This white paper explores the main drivers and requirements of an MTC-optimized 6G network, and presents a set of research directions for different aspects of MTC that can be synthesized through the following six key research questions: Will the main KPIs of 5G, namely reliability-latencyscalability, continue to be the dominant KPIs in 6G; or will emerging metrics such as energy-efficiency, end-to-end (E2E) performance measures and sensing become more important? How can different E2E service mandates with different KPI requirements be delivered through a multidisciplinary approach jointly considering optimization at the physical up to the application layer? What are the key enablers towards designing ultralow power receivers and highly efficient sleep modes to support ultra-low-cost ultra-low-power or even passive MTC devices? How can a disruptive rather than incremental joint design of a massively scalable waveform and medium access policy be tackled to efficiently support global connectivity for MTC? How can new service classes characterizing missioncritical and dependable MTC in 6G be supported through multifaceted connectivity and non-cellular centric wireless solutions? What are the potential enablers of long-term secure schemes considering the heterogeneous requirements and capabilities of MTC devices? How can lightweight and flexible usable ways of handling privacy and trust be designed in MTC by combining the user perspective with the technical perspective?

This white paper is one of the twelve new themed 6G White Papers led by the 6G Flagship program. It involved the participation of more than 50 experts and enthusiasts of future 6G technologies. In this white paper, we intend to shed light on advanced features relevant to networking that would shape the evolution beyond 5G, ultimately leading to the 6G mobile system.

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.

This paper demonstrates a Convolutional Neural Network (CNN) based mechanism for identification of low power wide area network (LPWAN) technologies such as LoRA, Sigfox, and IEEE 802.15.4g. Since the technologies operate in unlicensed bands and can interfere with each other, it becomes essential to identify technologies (or interference in general) so that the impact of interference can be minimized by better managing the spectrum. Contrary to the traditional rule-based identification mechanisms, we use Convolutional Neural Networks (CNNs) for identification, which do not require any domain expertise. We demonstrate two types of CNN based classifiers: (i) CNN based on raw IQ samples, and (ii) CNN based on Fast Fourier Transform (FFT), which give classification accuracies close to 95% and 98%, respectively. In addition, an online video is created for demonstrating the process [1].