Jorge Querol

Cooperation between satellite and mobile network operators enables the integration of non-terrestrial satellite networks (NTNs) with terrestrial networks (TNs), allowing users to seamlessly switch from Mobile Network Operators (MNOs) to Satellite Network Operators (SNOs) in areas with limited MNO coverage or during high-speed travel. However, mobility issues, such as connection failures, the ping-pong effect, and high interruption times, can occur during these transitions. This paper examines various cooperation models, including roaming and handover protocols, and proposes key enhancements to these models to reduce service interruption time. It also discusses the use of machine learning (ML) techniques to reduce the above mobility issue and increase the success rate of cooperation among network operators.

Accessing non-terrestrial networks (NTN) via existing 4G/5G handheld devices without positioning information presents significant challenges in achieving precise synchronization between a gNodeB and hundreds of users dispersed across an NTN cell. While terrestrial communication standards limit the relative delay between the center and edge of coverage to 200 km, multilayered NTN can introduce user-specific delay variation spanning thousands of kilometers. To address this challenge, we reeingeer the receiver’s Random Access (RA) preamble detector to achieve robust detection and Transmission Arrival (TA) estimation of scattered users with unknown but deterministic differential delays. The proposed Physical layer algorithm, termed Mirage detector, aligns with current 3GPP NTN standardization efforts and is forward/backward-compatible with future/earlier releases. Mirage detector leverages a dynamic Striding mechanism for multiple hypothesis testing, making it resilient to high false alarm rates caused by signals arriving beyond their nominal duration. Mirage also efficiently supports multiuser detection, enabling rapid resynchronization during frequent handovers—approximately twice per minute per Connected UE—while minimizing computational load of the transceivers. Extensive simulations results validate the advantage of the proposed Mirage detector that enables reliable multilayer connectivity for hundreds of legacy devices within the access occasion periodicity, without requiring hardware upgrades or any self-localization capability.

In this article, we propose the integration of the holographic multiple-input multiple-output (HMIMO) as a transformative solution for next-generation nonterrestrial networks (NTNs), addressing key challenges, such as high hardware costs, launch expenses, and energy inefficiency. Traditional NTNs are constrained by the financial and operational limitations posed by bulky and costly antenna systems, alongside the complexities of maintaining effective communications in space. HMIMO offers a novel approach utilizing compact and lightweight arrays of densely packed radiating elements with real-time reconfiguration capabilities, thus capable of optimizing system performance under dynamic conditions such as varying orbital dynamics and Doppler shifts. By replacing conventional antenna systems with HMIMO, the number of required radio-frequency (RF) chains can be cut to roughly one-tenth while achieving about threefold lower power consumption and an order-of-magnitude decrease in hardware cost. Thus, manufacturing and launch expenses can be substantially reduced, paving the way for more streamlined and cost-effective NTN deployments. This advancement holds significant potential to democratize space communications, making them accessible to a broader range of stakeholders, including smaller nations and commercial enterprises. Moreover, the inherent capabilities of HMIMO in enhancing energy efficiency, scalability, and adaptability position this technology as a key enabler of new use cases and sustainable satellite operations.

This paper pioneers the domain of multi-user holographic unmanned aerial vehicle (UAV) communications, establishing a robust foundation for future advancements in next-generation aerial wireless networks. It investigates the joint design of hybrid holographic beamforming and three-dimensional (3D) positioning for a UAV equipped with a reconfigurable holographic surface (RHS), with the objective of maximizing the network’s sum rate. To tackle this inherently complex and non-convex optimization problem, a novel alternating optimization framework is proposed. The solution leverages zero-forcing (ZF) digital beamforming and a gradient ascent strategy to iteratively update the holographic beamforming weights and the UAV’s 3D location, while satisfying key system constraints. This framework is tailored to efficiently navigate the trade-offs between hybrid transceiver design and UAV mobility limitations, ensuring both adaptability and performance scalability. Simulation results confirm that the proposed approach achieves substantial gains in sum rate and system robustness compared to conventional methods, validating its effectiveness under diverse channel and deployment conditions.

This article proposes a novel Multiple Access (MA) scheme called Ramanujan Periodic-subspace Division Multiple Access (RPDMA) for subcarrier sizes $N = 2^{m},\ m\in \mathbb {N}$, to address the high Peak-to-Average Power Ratio (PAPR) in Orthogonal Frequency Division Multiple Access (OFDMA). Building on the properties of Ramanujan subspaces, we design transmitter and receiver models that allocate users on a subspace-wise basis, ensuring zero inter-user interference, providing inherent frequency diversity. We analyze the computational complexity of OFDMA, SC-FDMA, and RPDMA, and find that RPDMA has substantially lower per-user transmitter complexity than both OFDMA and SC-FDMA, while its receiver complexity is comparable to SC-FDMA and higher than that of OFDMA. We further introduce a generalized framework, termed Nested Periodic-subspace Division Multiple Access (NPDMA), which unifies both RPDMA and OFDMA under a common family of multi-carrier MA schemes. We derive the theoretical PAPR of RPDMA and demonstrate its superiority over OFDMA. The analysis is validated through numerical simulations under two multi-user scenarios with diverse Quality of Service (QoS) requirements. The results demonstrate that RPDMA achieves lower PAPR than both OFDMA and SC-FDMA, with users assigned larger subspaces benefiting from even greater PAPR reduction. We prove that both the sum and per-user Spectral Efficiency (SE) of RPDMA are identical to those of OFDMA and SC-FDMA. In terms of Bit Error Rate (BER), SC-FDMA achieves the best performance, while RPDMA still outperforms OFDMA as the SNR increases.

The integration of Joint Communications and Sensing (JCAS) and Reconfigurable Intelligent Surfaces (RIS) is poised to be a cornerstone of Sixth-Generation (6G) Non-Terrestrial Networks (NTNs), enabling unprecedented capabilities for ubiquitous connectivity and high-resolution environmental monitoring. While the synergy between RIS and JCAS in terrestrial networks has been explored, its application in the unique and stratified environment of NTNs presents distinct opportunities and challenges across different propagation domains. This paper provides a comprehensive analysis of RIS-enabled JCAS for 6G NTNs, with a specific focus on differentiating the operational landscape between the space layer and the atmospheric layer. We identify novel key opportunities in the atmospheric layer, including uncrewed aerial vehicles (UAVs) and high-altitude platforms (HAPSs), and the space layer, including, low-Earth orbit, medium Earth orbit, and Geostationary orbit (GEO), and present and discuss the future challenges to be addressed to realize the integration of RIS a reality for the next-generation JCAS for NTNs.

Global Navigation Satellite Systems (GNSS) are critical infrastructure components in modern Positioning, Navigation, and Timing (PNT) services, playing a vital role in both civilian and defense applications. These systems operate in specific frequency bands that are also utilized by other Earth Observation technologies, such as GNSS-Radio Occultations and GNSS-Reflectometry. Other passive microwave remote sensing techniques such as microwave radiometers, work with very faint signals in nearby frequency bands within the L-Band. However, the increasing prevalence of Radio-Frequency Interferences (RFI) poses a significant threat, potentially compromising the integrity and reliability of PNT services, and corrupting geophysical observations. Effective RFI mitigation relies on accurate detection and classification of interference sources, a task that becomes increasingly challenging due to the complexity and diversity of RFI signals. This work presents an automated classification system for RFI detection and characterization in GNSS bands. The methodology employs advanced digital signal processing techniques and statistical algorithms to improve RFI detection and classification. RFI events are then stored in a long-term database to provide insights into the local spectrum, and to aid in mitigation and law enforcement efforts. This study provides a description of the classification system, including its architecture, implementation, and performance analysis. The results highlight the potential of this system to enhance the resilience of GNSS PNT services against RFI.

The integration of Non-Terrestrial Network (NTN) within the Fifth Generation (5G) and Sixth Generation (6G) ecosystem offers new opportunities for global Positioning, Navigation, and Timing (PNT) services, particularly in environments where Global Navigation Satellite System (GNSS) signals are obstructed or degraded. This work investigates the positioning performance of Time of Arrival (ToA)-based techniques using both Positioning Reference Signal (PRS) and Synchronization Signal Block (SSB) signals in 5G NTN scenarios. A comprehensive User Equivalent Range Error (UERE) model is developed, incorporating satellite, receiver, and propagation-related error sources across FR1 and FR2 frequency bands. The influence of satellite geometry is assessed through a Geometrical Dilution of Precision (GDOP) analysis, while a minimum satellite visibility evaluation quantifies the likelihood of observing at least four satellites above various elevation masks for representative LEO constellations at 600 km and 1200 km altitudes. Monte Carlo simulations quantify the impact of UERE on positioning accuracy, revealing that the 95th-percentile horizontal errors range from approximately 8 m to 26 m, depending on signal type, frequency band, and orbital altitude. The results demonstrate that ToA-based positioning using PRS can surpass typical urban GNSS-level accuracy, highlighting its potential in GNSS-challenged environments. In comparison, SSB-based positioning remains a viable alternative, offering reduced complexity at the cost of moderate accuracy trade-offs. The study offers a unified framework for understanding positioning accuracy in 5G/6G NTN, bridging UERE modeling, satellite visibility, and signal design considerations, and supporting the ongoing development of reliable global PNT services.