Persistent rural–urban disparities in broadband connectivity remain a major policy challenge, even in digitally advanced countries. This paper examines how these inequalities manifest in northern Finland and Sweden, where sparse populations, long distances, and seasonal variations in demand create persistent gaps in service quality and reliability. Drawing on survey data (n = 148), field interviews, and spatial analysis, the study documents how variability in connectivity shapes everyday life and work in Arctic rural communities.

To assess these disparities more systematically, the paper applies the Cellular Coverage Inequality (CCI) index as a spatial Key Performance Indicator (KPI). By relating mobile coverage outcomes to degrees of rurality, the CCI highlights how network performance is unevenly distributed across space and reveals disparities that population-based or aggregate coverage statistics often obscure. While headline indicators suggest high levels of availability, experiential evidence points to recurring reliability constraints and location-specific coverage gaps affecting safety, work practices, and access to essential services.

Building on these findings, the paper develops policy reflections in six areas: shared infras- tructure and roaming frameworks, spectrum flexibility and local access models, performance- based Quality-of-Service monitoring, standardized and transparent reporting, techno-economic and environmental constraints of rural deployment, and digital-skills initiatives. Together, these reflections underscore the importance of combining spatial performance metrics with gover- nance and cost considerations when evaluating broadband policy in sparsely populated regions. The study contributes to ongoing debates on how connectivity monitoring can move beyond nominal coverage targets toward more equitable and performance-sensitive policy frameworks.

This paper presents a new quantitative means to measure how cellular areal network coverage is distributed over a country or region, in particular the extent to which coverage is concentrated to urban regions. We present a new Cellular Coverage Inequality (CCI) index useful as a quantitative measure of fairness in a urban-rural perspective. Necessary data to determine the index are, on one hand, a geographical rurality map, essentially derived from a population map and, on the other hand, a cellular coverage map of the kind typically published by cellular operators. Our CCI-index then allows different cellular networks to be compared in terms of their coverage fairness and urban-rural inequality. Alternatively, the urban-rural coverage divide can be monitored over time. We give examples of the use of the CCI index for public coverage maps of Sweden over the years 2013-2020.

This paper investigates the role of standalone Reconfigurable Intelligent Surfaces (RIS) in enhancing positioning capabilities in 6G networks. A standalone RIS differs from a conventional RIS in that the former is not controlled by the network or any other external node. Rather, it operates autonomously and its configurations and location are known to the network. We explore RIS as a tool to enable positioning through Time Difference of Arrival (TDoA) estimation with fewer base stations (BSs), leveraging its ability to act as a virtual BS. A theoretical framework is developed using the Cramér-Rao Lower Bound (CRLB) to analyze the estimation accuracy under various deployment scenarios and parameter settings. Our findings demonstrate that while RIS does not inherently improve TDoA accuracy, it facilitates sparser BS deployments by requiring only one BS and one RIS for effective estimation of TDoA. Additionally, we evaluate the impact of system parameters such as the number of subcarriers, pilot spacing, and RIS elements, identifying subcarrier spacing as the most influential factor for improving the TDoA estimate. These insights highlight the potential of RIS to complement conventional BS deployments and advance 6G TDoA estimation systems.

This letter introduces a reconfigurable intelligent surface (RIS)-assisted modulation scheme tailored for non-coherent receivers. Employing a RIS of dual-polarized elements, we manipulate the polarization state of the reflected signals to enable a differential polarization shift keying modulation scheme while simultaneously beamforming the reflected signal towards the receiver. Subsequently, an additional differential phase shift keying (DPSK) modulation layer is superimposed under two distinct deployments, where either the source or the RIS performs the DPSK modulation. Furthermore, the analytical performance is investigated, and a comparison with benchmark schemes is evaluated.

Cellular communication standards have been established to ensure connectivity across most urban environments, complemented by deployment hardware and facilities tailored for city life. At the same time, numerous initiatives seek to broaden connectivity to rural and developing areas. However, with nearly half the global population still offline, there is an urgent need to drive research toward enhancing connectivity in areas and conditions that deviate from the norm. This article delves into innovative communication solutions not only for hard-to-reach and extreme environments but also introduces “hard-to-serve” areas as a crucial, yet underexplored, category within the broader spectrum of connectivity challenges.We explore the latest advancements in communication systems designed for environments subject to extreme temperatures, harsh weather, excessive dust, or even disasters such as fires. Our exploration spans the entire communication stack, covering communications on isolated islands, sparsely populated regions, mountainous terrains, and even underwater and underground settings. We highlight system architectures, hardware, materials, algorithms, and other pivotal technologies that promise to connect these challenging areas. Through case studies, we explore the application of 5G for innovative research, long range (LoRa) for audio messages and emails, LoRa wireless connections, free-space optics, communications in underwater and underground scenarios, delay-tolerant networks, satellite links, and the strategic use of shared spectrum and TV white space (TVWS) to improve mobile connectivity in secluded and remote regions. These studies also touch on prevalent challenges such as power outages, regulatory gaps, technological availability, and human resource constraints, where we introduce the concept of peri-urban hard-to-serve areas where populations might struggle with affordability or lack the skills for traditional connectivity solutions. This article provides an exhaustive summary of our research, showcasing how 6G and future networks will play a crucial role in delivering connectivity to areas that are hard-to-reach, hard-to-serve, or subject to extreme conditions (ECs).

Polarization is a fundamental property of electromagnetic radio signals but often neglected in localization studies. In this paper, we study the potential benefits of integrating the polarization dimension into localization applications. We develop a three-dimensional (3D) geometric channel model between a base station (BS) and user equipment (UE), both equipped with dual-polarized (DP) antennas, which offers fundamental insights into the angles of departure (AoD) from the BS to the UE as well as the 3D orientation of the UE. From the model, we identify the degrees of freedom (DoF) provided by the polarization dimension for localization solutions by evaluating the rank of the equivalent Fisher information matrix. Subsequently, we leverage these DoF to introduce three distinct localization applications: (i) 3D orientation estimation, (ii) 2D AoD estimation, and (iii) mixed 2D position and 1D orientation estimation for vehicular scenarios. Furthermore, for the three localization applications we identify their regions of operation in terms of the ranges of the angles of interest, to avoid any ambiguity occurrence through the estimation process, thereby guaranteeing unique solutions. Finally, we derive the Cramér-Rao lower bounds and numerically establish the efficiency of the proposed estimators.

As 6G emerges, cellular systems are envisioned to integrate sensing with communication capabilities, leading to multi-faceted communication and sensing (JCAS). This paper presents a comprehensive cross-layer overview of the Hexa-X-II project's endeavors in JCAS, aligning 6G use cases with service requirements and pinpointing distinct scenarios that bridge communication and sensing. This work relates to these scenarios through the lens of the cross-layer physical and networking domains, covering models, deployments, resource allocation, storage challenges, computational constraints, interfaces, and innovative functions.

We propose a joint polarization and spatial modulation (JPSM) scheme using reconfigurable intelligent surface (RIS). In this scheme, a RIS equipped with dual-polarized (DP) reflecting elements is used to assist the communication between a transmitter of a single polarized antenna and a receiver equipped with a uniform linear array of DP antennas, while additionally encoding the reflected waves on the RIS to perform JPSM scheme. The information data is encoded in terms of the receiver DP antenna index as well as the polarization state of the received signal. Furthermore, we develop exhaustive and heuristic RIS phase shift design solutions to enable the RIS-JPSM scheme. Moreover, both an optimum maximum likelihood and a low complexity greedy detectors are formulated. The proposed scheme enhances the data rate by operating higher-order polarization modulation in comparison to the conventional RIS-based spatial modulation scheme.

Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for 6G networks. In this study, we explore a novel use case for RIS: passive localization and tracking of a RIS-equipped object using monostatic sensing, where the fixed transmitter and receiver share the same single antenna, using OFDM signals. We develop a low-complexity algorithm that achieves centimeter-level accuracy using only 6 MHz bandwidth, and by applying temporal coding to random RIS phase profiles, separating signals from undesired multipath sources. In addition, we evaluate the impact of model uncertainty on the performance of the algorithm.