As the transition from 5G to 6 G unfolds, a substantial increase in Internet of Things (IoT) devices is expected, enabling seamless and pervasive connectivity across various applications. Accommodating this surge and meeting the high capacity demands will necessitate the integration of NonTerrestrial Networks (NTNs). However, the extensive coverage area of satellites, relative to terrestrial receivers, will lead to a high density of users attempting to access the channel at the same time, increasing the collision probability. In turn, the deployment of mega constellations make it possible for ground users to be in visibility of more than one satellite at the same time, enabling receiver diversity. Therefore, in this paper, we evaluate the impact of multi-receivers in scenarios where IoT nodes share the channel following a non-orthogonal multiple access (NOMA)irregular repetition slotted ALOHA (IRSA) protocol. Considering the impairments of satellite channels, we derive a lower bound of system performance, serving as a fast tool for initial evaluation of network behavior. Additionally, we identify the trade-offs inherent to the network design parameters, with a focus on packet loss rate and energy efficiency. Notably, in the visibility of only one extra satellite as receiver yields significant gains in overall system performance.

Faster-than-Nyquist (FTN) signaling aims at improving the spectral efficiency of wireless communication systems by exceeding the boundaries set by the Nyquist-Shannon sampling theorem. 50 years after its first introduction in the scientific literature, wireless communications have significantly changed, but spectral efficiency remains one of the key challenges. To adopt FTN signaling, intersymbol interference (ISI) patterns need to be equalized at the receiver. Motivated by the pattern recognition capabilities of convolutional neural networks with skip connections, we propose such deep learning architecture for ISI equalization and symbol demodulation in FTN receivers. We investigate the performance of the proposed model considering quadrature phase shift keying modulation and low density parity check coding, and compare it to a set of benchmarks, including frequency-domain equalization, a quadratic-programming-based receiver, and an equalization scheme based on a deep neural network. We show that our receiver outperforms any benchmark, achieving error rates comparable to those in additive white Gaussian noise channel, and higher effective throughput, thanks to the increased spectral efficiency of FTN signaling. With a compression factor of 60% and code rate 3/4, the proposed model achieves a peak effective throughput of 2.5 Mbps at just 10dB of energy per bit over noise power spectral density ratio, with other receivers being limited by error floors due to the strong intersymbol interference. To promote reproducibility in deep learning for wireless communications, our code is open source at the repository provided in the references.

Through the transition from 5G to 6G, a significant rise in the number of Internet of Things (IoT) devices is anticipated, enabling pervasive and uninterrupted connectivity for several applications, in different verticals. Coping with the substantial influx of IoT devices and fulfilling the high capacity demands of different IoT technologies, such as NB-IoT, will necessitate the involvement of Non-Terrestrial Networks (NTNs), which will serve as crucial complements to terrestrial systems, enhancing the availability, resilience, and coverage of the network and will guarantee cost/benefit for some services and will fully satisfy some key requirements. Nevertheless, a primary obstacle to be faced when integrating IoT terrestrial communication systems in NTN, in particular with Non-Geostationary (NGSO) satellites, lies in the short visibility time of the flying platform due to its high speed. The latter introduces criticalities in various communication phases, including the Random Access (RA) procedure. In a highly congested scenario, the large Round Trip Delay and a limited visibility window, which varies for each user within the satellite’s coverage area, contribute to reducing the number of users successfully concluding the RA procedure. In this paper, to enhance the percentage of users who successfully conclude the RA, we introduce the concept of Coverage Enhancement Levels in time and a novel backoff mechanism, namely Smart Backoff, that leverages the beam coverage visibility period of individual users to adjust the random backoff interval. The numerical results obtained from our proposed scheme substantiate significant improvements compared to the standard backoff scheme. Specifically, our approach yields an increase of up to 16% per channel in the percentage of users who successfully complete the RA process.

With the evolution process of the 5G into the 6G, there will be an exponential growth of the Internet of Things (IoT) devices, offering ubiquitous and continuous connectivity services in all areas of our life. In order to deal with such huge amount of IoT devices, and to satisfy the large capacity requirements of the most advanced of them, non-terrestrial networks (NTNs) will play a pivotal role to assist and complement the terrestrial systems. However, one of the major challenges of the NTN channel is represented by the large delay which hampers the different communication phases, such as the Random Access (RA) procedure. In this paper, we provide an assessment of the system level performances, in terms of access delay and access success probability, of the Narrowband IoT (NB-IoT) devices in typical satellite scenarios defined by the 3GPP. In particular, we provide a detailed analysis under different network densities, for various combinations of the related configuration parameters, and for different system architectures supporting the NB-IoT over NTN. The analysis led to a useful comparison of the RA performances obtained with 3GPP compliant configurations for access parameters and satellite configuration with the aim to maximize the access success probability and minimize the access time in a NB-IoT NTN system.

Satellite Communications systems are a promising solution to extend and support terrestrial networks in un- or under-served areas. In Release 17, 3GPP initiated a Study Item for IoT over Non-Terrestrial Networks (NTN) to assess and adapt the NB-IoT air interface to the NTN characteristics. One of the main objective of the study is the evaluation of the Random Ac-cess procedure and the estimation of the up-link synchronization parameters at the satellite. In this context, it is essential that the detection algorithms of the NB-IoT preamble, at the receiver, satisfy the user detection requirements as well as the timing synchronization accuracy. This is not a trivial task, especially in satellite channels where the carrier frequency offset (CFO) is more severe than that of terrestrial links. In order to cope with this problem, we propose a new algorithm based on a non-decimated dyadic wavelet transform. This method is able to detect the incoming preambles and to estimate their time of arrival, without having to compensate the CFO first. The performance of the proposed algorithm is compared with a classical estimation approach based on the Fast Fourier Transform, substantiating the significant advantage obtained in the considered NTN scenarios.

Machine Type Communications (MTC) and Internet of Things (IoT) applications are growing exponentially and are forecast to play an even more important role in Future Networks and Systems. The Third Generation Partnership Project (3GPP) introduced the Narrowband IoT (NB-IoT) air interface as a response to the IoT use case requirements. However, it is widely accepted that the terrestrial network alone is not able to serve the requirement of the IoT market of a truly ubiquitous coverage. To this aim, several initiatives are currently addressing the inclusion of a satellite component into the telecommunication infrastructure to extend its coverage to those areas that are unserved or underserved by the terrestrial network. The recently approved 3GPP study item on NB-IoT over Non-Terrestrial Network (NTN) is the most important of these initiatives. The study item, starting at the beginning of 2021, will assess the performance of the NB-IoT air interface over satellite and will identify which adaptations are needed to enable its use. In this context, our work provides an assessment of the system level performance, in terms of the link budget parameters, of the NB-IoT air interface in typical satellite scenarios. In particular, we provide a detailed discussion of the system architecture supporting the NB-IoT over NTN, a description of the link budget computation methodology, and the numerical results of the link budget analysis in both single-satellite and multi-satellite scenarios.