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[] An important task in the field of sensor technology is the efficient implementation of adaptation procedures of measurements from one sensor to another sensor of identical design. One idea is to use the estimation of an affine transformation (AT) between different systems, which can be improved by the knowledge of experts. This article presents an improved solution from Glacier Research that was published back in 1973. The results demonstrate the adaptability of this solution for various applications, including software calibration of sensors, implementation of expert-based adaptation, and paving the way for future advancements such as distributed learning methods. One idea here is to use the knowledge of experts for estimating an AT between different systems. We evaluate our research with simulations and also with real measured data of a multisensor board with eight identical sensors. Both dataset and evaluation script are provided for download. The results show an improvement for both the simulation and the experiments with real data.

Ensuring the security of the Internet of Vehicles (IoV) has been a challenging task, particularly due to the high mobility rate of the many elements in the network. With vehicles constantly moving and frequently exchanging data, authentication is crucial for maintaining security. We propose a new approach based on group authentication to handle authentication of several units simultaneously while protecting privacy in an IoV environment. The proposed protocol is designed to perform an authentication and handover process of multiple users within a predefined group. The group authentication based approach ensures privacy of users as the group members’ identification is systematically redefined after each group exchange process. Thus, the proposed scheme is designed to offer a promising solution to security challenges within the IoV environment by simultaneously addressing scalability and protecting the privacy of user data. Additionally, our scheme is resistant to various types of attacks, including offline ID attacks, replay attacks, location spoofing attacks, and replay attacks. Comprehensive tests have been conducted and the results are presented in the analysis section, both in terms of the computational cost and the running times, which reveal that the proposed method demonstrates remarkable practicality when compared to the existing approaches.

The use of sub-Terahertz (sub-THz) band is gaining considerable attention in 6G networks. In this study, we introduce hardware and propagation integrated 3D Propagation Model to describe sub-THz channels and discuss its advantages over both deterministic and stochastic 6G channel models. The unexplored mutuality of localization and communication is presented and its potential in integrated sensing and communication (ISAC) applications is highlighted. Afterward, a real-time sub-THz localization experiment is conducted to show the impact of the mispositioned and misaligned narrow beams on service quality. In continuation, we highlight the most current challenges and developments in THz localization and explore the potential of sub-THz frequencies to efficiently utilize the ultra-wideband spectrum. In the end, the open issues that need to be over-come to provide high spatial resolution and millidegree-level angle of arrival estimation in ISAC applications have been explored.

Due to the unprecedented advances in satellite fabrication and deployment, innovative communications and networking technologies, ambitious space projects and programs, and the resurgence of interest in satellite networks, there is a need to redefine space networks (SpaceNets) to incorporate all of these evolutions. This paper introduces a vision for future SpaceNets that considers advances in several related domains. First, we present a reference architecture that captures the various network entities and terminals in a holistic manner. Based on this, space, air, and ground use cases are studied. Then, the architectures and technologies that enable the envisaged SpaceNets are investigated. In so doing, we highlight the activities and projects of different standardization bodies, satellite operators, and national organizations towards the envisioned SpaceNets. Finally, the challenges, potential solutions, and open issues from communications and networking perspectives are discussed.

Satellite direct-to-cell (D2C) connectivity considers the communication between satellites and low-cost handheld devices on Earth. It represents one of the most challenging aspects of the integration between terrestrial and non-terrestrial networks. Low Earth orbit (LEO), sub-6GHz bands, and large aperture satellite antennas are the key to enable D2C connectivity. The industry is tackling the problem with a conventional approach, consisting of the design of very large reflectors or phased arrays. This article proposes a new way to face the problem: the distributed approach. A satellite base station (BS) is decomposed into several small platforms in a so-called swarm configuration to form a sparse phased array. The use of small satellites promises cost-effective solutions, while distributed satellite systems (DSSs) increase the fault tolerance, and thus the reliability, of the entire constellation. This article compares the performance of conventional and distributed approaches under different conditions. It shows that distributed approaches outperform conventional ones even under unfavorable conditions and pessimistic assumptions. Important tradeoffs are derived showing the flexibility of distributed approaches. Finally, major research aspects for exploiting the full potential of the distributed approach are highlighted.

In this August 2024 issue of Mobile Communications and Networks series, we are pleased to present an extended series of articles covering various topics related to 5G, Wi-Fi, and future 6G. In particular, it covers positioning of reduced capability (RedCap) devices in 5G, timing sensitive applications in Wi-Fi 7, and aspects of ultra-high reliability under Wi-Fi 8. The topics potentially for 6G include some considerations towards software-based, multi-input-multi-output (MIMO), open radio access network (Open-RAN) architectures, several aspects for large-scale MIMO enabled satellite communications, system-level metrics for non-terrestrial networks (NTN) under a stochastic geometry framework, investigation of machine learning for radiomap (radio environmental map) estimation by integrating radio propagation models and artificial intelligence (AI), deep learning based site-specific beam alignment for 6G, analysis of the frequency range of 7–24 GHz potentially for 6G, presentation of an operator's perspective on 6G in terms of services, vision, and spectrum, and integration of sensing and localization in vehicle to everything (V2X) sidelink communications.

A dynamic and flexible generalized spatial modulation (GSM) framework is proposed for large-scale MIMO systems. Our framework is leveraged on the utilization of machine learning methods for GSM in order to improve the error performance in the presence of time-correlated channels and channel estimation errors. The decision tree and multi-layer perceptron algorithms are adopted as transmit antenna selection approaches. Simulation results indicate that in the presence of real-life impairments, machine learning based approaches provide a superior performance when compared to the classical Euclidean distance based approach. The observations are validated through measurement results over the designed 16 $\times $ 4 MIMO test-bed using software defined radio nodes.

In this January 2024 issue of Mobile Communications and Networks series, we are pleased to present six articles covering various topics related to 5G, WiFi, and future standardization such as 6G. In particular, it includes the use of multiple frequency bands (including Tera Hertz, or THz) in multi-band networks, the analysis of location-aware beyond 5G (B5G) local area network (LAN)-type services related to Industry 4.0, a block-chain-based self-sovereign identity for routing in an inter domain network, the usage of mmWave for 5G vehicle-to-everything (V2X), the practical deployment of automatic frequency coordination (AFC) based on WiFi 6G in South Korea, and the proposal of an alternative waveform for ultra-wideband (UWB).

It is our pleasure to present a rich set of papers with the objective of constructing the path to very high rate, ultra-reliable and ubiquitous mobile communications in the July 2023 issue of Mobile Communications and Networks series. The articles span a wide range of topics, including the use of machine learning for channel quality prediction, uplink time synchronization techniques for non-terrestrial networks (NTN) in 3GPP, evolution of digital terrestrial television (DTT), the integrated sensing and communications in IEEE 802.11bf, the use of high altitude platform station (HAPS) in green computing, blockchain-based network slice management, and subsequently their potential to address dynamic and unpredictable traffic demands of users in an energy-efficient manner, and finally a network architecture proposal for 6G networks to address the new requirements. Channel quality information is critical in cellular communication to manage resources and provide services within the required quality limit. Due to the emergence of diverse applications, uses and scenarios (e.g., sensors, vehicles, IoT, massive antenna arrays, flying base stations) in 6G, with challenging channel quality need and increased channel density, the criticality and complexity of acquiring channel quality information amplify significantly. The article “Machine Learning-based Channel Quality Prediction in 6G Mobile Networks” outlines the limitations of the existing solution of obtaining channel quality information and proposes a forward-looking solution of predicting channel quality based on machine learning. The traditional approach of transmitting, measuring and reporting reference signals is not scalable for future massive deployment, as it is time, energy and resource heavy. The proposed alternative solution reduces overhead and energy consumption, as it predicts channel quality based on machine learning by finding relationship among channel characteristics and network correlation.

This October 2023 issue contains a Feature Topic titled “MIMO Evolutions: Capacity, Coverage, Reliability, AI-Enabled, and More,” aiming to provide a comprehensive presentation of the state-of-the-art findings potentially shaping future evolutions of multi-input multi-output (MIMO) in research and standardization. The special issue drew ten submissions, resulting in three accepted papers after rigorous reviews. These three papers covers energy efficient extreme MIMO, gigantic MIMO channel characterization, and orbital angular momentum (OAM) for future MIMO.