Publications

This page features all publications developed and published as part of 6G-NTN, including journal, conference and white papers. You can explore (and download) the publications directly on this page or visit the 6G-NTN community on Zenodo.

Note: The 6G-NTN publications uploaded to the project community on Zenodo will be retained for the life of Zenodo, which is expected to continue for at least the next 20 years (as long as its host, CERN, continues its experimental programmes). ​

To the 6G-NTN community on Zenodo
NEW Scientific publication

Faster-Than-Nyquist Equalization with Convolutional Neural Networks

De Filippo, Bruno1; Amatetti, Carla1; Vanelli-Coralli, Alessandro1

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, inter-symbol 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 inter-symbol interference. To promote reproducibility in deep learning for wireless communications, our code is open source at the repository provided in the references. 

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NEW Scientific publication

Channel Prediction in 6G Non-Terrestrial Networks With Deep Learning: a Physical Layer Analysis

De Filippo, Bruno1; AMATETTI, CARLA1; VANELLI CORALLI, ALESSANDRO1

Channel prediction on the user equipment side has recently been identified as one of the main use cases for artificial intelligence in the radio access network. With advanced neural networks, complex channel dynamics can be predicted from current channel estimates, allowing for demodulation reference signals to be transmitted at a lower rate. Reducing reliance on pilot symbols for channel estimation frees valuable resources for data transmission, but this requires highly accurate predictions of future channel states. This paper introduces a lightweight convolutional neural network (CNN)-based encoder-decoder model designed to predict the time-frequency channel response matrix in orthogonal frequency division multiplexing systems. The proposed approach alternates pilot-full and pilot-free slots, leveraging deep learning to predict channel conditions over the latter while maintaining link quality. Through extensive simulations, we assess the model’s effectiveness on key physical layer metrics, with a particular focus on the effective throughput (TP). We show that the proposed predictor achieves TP gains of up to 8% with respect to channel estimation for high modulation orders; on the other hand, low modulation orders’ performance at low energy per bit over noise power spectral density suffer from inaccurate channel predictions. The predictor’s lightweight design, requiring only 160k multiply and accumulate operations and encompassing 5.5k parameters, ensures feasibility for real-time applications in next-generation networks.

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NEW Scientific publication

Open Datasets for Satellite Radio Resource Control

Shahid, Husnain1; Vázquez, Miguel Ángel1; Shaat, Musbah1; Henarejos, Pol2; Quijada, Nuria T.; Marinu, Enrique

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, inter-symbol 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 inter-symbol interference. To promote reproducibility in deep learning for wireless communications, our code is open source at the repository provided in the references. 

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NEW Scientific publication

Channel Prediction in 6G Non-Terrestrial Networks With Deep Learning: a Physical Layer Analysis

De Filippo, Bruno1; AMATETTI, CARLA1; VANELLI CORALLI, ALESSANDRO1

Channel prediction on the user equipment side has recently been identified as one of the main use cases for artificial intelligence in the radio access network. With advanced neural networks, complex channel dynamics can be predicted from current channel estimates, allowing for demodulation reference signals to be transmitted at a lower rate. Reducing reliance on pilot symbols for channel estimation frees valuable resources for data transmission, but this requires highly accurate predictions of future channel states. This paper introduces a lightweight convolutional neural network (CNN)-based encoder-decoder model designed to predict the time-frequency channel response matrix in orthogonal frequency division multiplexing systems. The proposed approach alternates pilot-full and pilot-free slots, leveraging deep learning to predict channel conditions over the latter while maintaining link quality. Through extensive simulations, we assess the model’s effectiveness on key physical layer metrics, with a particular focus on the effective throughput (TP). We show that the proposed predictor achieves TP gains of up to 8% with respect to channel estimation for high modulation orders; on the other hand, low modulation orders’ performance at low energy per bit over noise power spectral density suffer from inaccurate channel predictions. The predictor’s lightweight design, requiring only 160k multiply and accumulate operations and encompassing 5.5k parameters, ensures feasibility for real-time applications in next-generation networks.

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NEW Scientific publication

Uplink OFDM Channel Prediction with Hybrid CNN-LSTM for 6G Non-Terrestrial Networks

De Filippo, Bruno1; AMATETTI, CARLA1; VANELLI CORALLI, ALESSANDRO1

Wireless communications are typically subject to complex channel dynamics, requiring the transmission of pilot sequences to estimate and equalize such effects and correctly receive information bits. This is especially true in 6G nonterrestrial networks (NTNs) in low Earth orbit, where one end of the communication link orbits around the Earth at several kilometers per second, and a multi-carrier waveform, such as orthogonal frequency division multiplexing (OFDM), is employed. To minimize the pilot overhead, we remove pilot symbols every other OFDM slot and propose a channel predictor to obtain the channel frequency response (CFR) matrix in absence of pilots. The algorithm employs an encoder-decoder convolutional neural network and a long short-term memory layer, along with skip connections, to predict the CFR matrix on the upcoming slot based on the current one. We demonstrate the effectiveness of the proposed predictor through numerical simulations in tapped delay line channel models, highlighting the effective throughput improvement. We further assess the generalization capabilities of the model, showing minimal throughput degradation when testing under different Doppler spreads and in both line of sight (LoS) and non-LoS propagation conditions. Finally, we discuss computational-complexity-related aspects of the lightweight hybrid CNN-LSTM architecture.

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NEW Scientific publication

Location-based User Scheduling through Graph Coloring for Cell-Free MIMO NTN Systems

Riviello, Daniel Gaetano (Researcher)1; De Filippo, Bruno (Researcher)1; Ahmad, Bilal (Researcher)1; Guidotti, Alessandro (Researcher)2; Vanelli-Coralli, Alessandro (Project leader)1

Non-Terrestrial Networks (NTN) have recently grown in popularity, with Low Earth Orbit (LEO) mega-constellations delivering broadband services to households worldwide. Under the cell-free MIMO paradigm in full frequency reuse systems, meeting the increasing traffic demand requires complex scheduling and digital beamforming algorithms to minimize the excessive co-channel interference. In this paper, we present a user scheduling algorithm for LEO-based B5G NTNs with reduced computational complexity that does not require knowledge of downlink (DL) Channel State Information (CSI). In our proposed method, we perform user grouping by first solving the Minimum Clique Cover (MCC) problem on an inter-user distance adjacency matrix, avoiding the need for DL pilots for CSI estimation. We approach MCC as a graph coloring task on the complementary graph, using the DSatur algorithm to minimize the computational complexity of the scheduler. Users within the same group are served via space-division multiplexing by means of feed space digital beamforming, inferring the users’ channel vectors from their position. System-level analysis show that the proposed algorithm, named Distance-based MCC DSatur (D-MCC-DSatur), achieves a per-cluster sum-rate capacity gain of up to 1.8% with respect to the distance-based Multiple Antenna Downlink User Scheduling (D-MADOC) algorithm.

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NEW Scientific publication

Channel Prediction in 6G Non-Terrestrial Networks With Deep Learning: a Physical Layer Analysis

De Filippo, Bruno1; AMATETTI, CARLA1; VANELLI CORALLI, ALESSANDRO1

Channel prediction on the user equipment side has recently been identified as one of the main use cases for artificial intelligence in the radio access network. With advanced neural networks, complex channel dynamics can be predicted from current channel estimates, allowing for demodulation reference signals to be transmitted at a lower rate. Reducing reliance on pilot symbols for channel estimation frees valuable resources for data transmission, but this requires highly accurate predictions of future channel states. This paper introduces a lightweight convolutional neural network (CNN)-based encoder-decoder model designed to predict the time-frequency channel response matrix in orthogonal frequency division multiplexing systems. The proposed approach alternates pilot-full and pilot-free slots, leveraging deep learning to predict channel conditions over the latter while maintaining link quality. Through extensive simulations, we assess the model’s effectiveness on key physical layer metrics, with a particular focus on the effective throughput (TP). We show that the proposed predictor achieves TP gains of up to 8% with respect to channel estimation for high modulation orders; on the other hand, low modulation orders’ performance at low energy per bit over noise power spectral density suffer from inaccurate channel predictions. The predictor’s lightweight design, requiring only 160k multiply and accumulate operations and encompassing 5.5k parameters, ensures feasibility for real-time applications in next-generation networks.

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NEW Scientific publication

Cell-Free MIMO in 6G NTN with AI-predicted CSI

De Filippo, Bruno (Researcher)1; Campana, Riccardo (Researcher)1; Guidotti, Alessandro (Researcher)2; Vanelli-Coralli, Alessandro (Project leader)1

Non-Terrestrial Networks (NTNs) in 6G are expected to integrate the Terrestrial Networks (TNs) coverage and provide connectivity to a multitude of User Terminals (UTs). In order to cope with the traffic demand, NTNs can employ cell-free MIMO with full frequency reuse schemes to maximize the spectral efficiency of the system. However, such technique can be strongly affected by channel aging, impacting their application to Low Earth Orbit (LEO)-based NTNs. Aiming at counteracting this effect, we present in this paper a lightweight Artificial Intelligence (AI) model for Channel State Information (CSI) prediction. To predict the propagation channel, the proposed algorithm learns its temporal statistics, e.g., the Line-of-Sight (LoS) shadowing correlation, from data. The model then applies the corrections to the feedback CSI, minimizing the difference with respect to the propagation channel encountered at transmission time. System-level analyses on a LEO-based CF-MIMO system report an improvement of the per-user capacity by up to 15% and a reduction of the outage probability when predicted CSI are used instead of aged channel coefficients.

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NEW Scientific publication

Non-Uniform User Distribution in Non-Terrestrial Networks with Application to User Scheduling

De Filippo, Bruno (Researcher)1; Ahmad, Bilal (Researcher)1; Riviello, Daniel Gaetano (Researcher)1;Guidotti, Alessandro (Researcher)2;Vanelli-Coralli, Alessandro (Project leader)1

Non-Terrestrial Networks (NTNs) have recently seen a surge in interest from both the industry and consumers, attracted by the promise of a global coverage with drastically increased performance. Such coverage is achieved through the use of satellite mega-constellations which field of view can span from a few tens to hundreds of kilometers within each beam. Usually, the performance of NTN systems is assessed assuming uniformly distributed users. This approach, which has been extremely effective in the past when the satellite user densities were limited, neglects the human tendency to settle in clusters. Thus, a strong approximation on the real distribution of users is introduced when the subscriber density starts approaching the terrestrial one. This leads to results that do not reflect the actual system performance and, hence, might lead to suboptimal designs. In this paper, we therefore propose a Cluster-Process-based non-uniform distribution that better reflects the actual user density on ground, thus providing an improved tool for the design of NTN systems. To this aim, we propose a general procedure to extract statistics from population count datasets and tailor the user distribution model to the coverage area. To show the effectiveness and importance of considering non-uniform distributions, we empirically analyze the user scheduling performance in a B5G Low Earth Orbit High Throughput Satellite system. The per-cluster sum-rate capacity and user throughput achieved under non-uniform and uniform distribution are compared, highlighting the inaccuracies that the uniform assumption can introduce.

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NEW Scientific publication

Multidimensional Vector-Frequency Carriers for Enhanced Data Transmission

Ayebare.B, Gyavira (Researcher)

Traditional communication systems rely predominantly on temporal frequency multiplexing, where spectral separation along a single frequency axis ensures orthogonality between concurrent transmissions. Techniques such as Orthogonal Frequency Division Multiple Access (OFDMA) have therefore maximised spectrum efficiency by dividing the time–frequency plane into distinct, non-overlapping channels. However, this one-dimensional representation of frequency overlooks the latent degrees of freedom embedded in spatially oriented frequency domains, which could potentially carry independent information streams without violating orthogonality. In this paper, we introduce the concept of Multidimensional Frequency Multiplexing (MFM) — a framework where information is modulated simultaneously across orthogonal frequency axes distributed in spatial domains (e.g., x–y planes) in addition to the conventional temporal axis. Unlike OFDMA, which treats subcarriers as scalar frequencies in time, MFM defines subcarriers as vector frequencies characterised by direction and magnitude, allowing the
transmission to form spatially distributed oscillatory patterns rather than purely temporal waveforms.
Through mathematical modeling, we derive the multidimensional Fourier basis required to sustain independent subcarriers across two or more frequency axes, establishing their orthogonality and potential for parallel information encoding. Comparative analyses between OFDMA and MFM reveal that the latter exhibits a higher theoretical capacity, improved resilience against multipath fading, and enhanced spectral density through vectorised frequency allocation. The implications extend toward novel communication architectures where frequency,
space, and geometry coexist as unified resources for data transmission, forming a bridge between electromagnetic field geometry and information theory.

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NEW Scientific publication

A Novel Framework for Proactive CNF Orchestration in 6G NTN

Piemonti, Alice (Contact person)1; Campana, Riccardo2, 3; Cianchini, Vito1; AMATETTI, CARLA3; Neri, Massimo1; VANELLI CORALLI, ALESSANDRO3

The increasing complexity of 5G and beyond networks, particularly with the integration of Non-Terrestrial Networks (NTNs), demands more dynamic and intelligent approaches to Radio Access Network (RAN) optimization. This paper presents a novel framework for closed-loop NTN RAN optimization centered on proactive Containerized Network Function (CNF) orchestration. By leveraging Artificial Intelligence (AI) to anticipate virtual resource needs, the proposed framework enables efficient, real-time deployment and scaling of CNFs, significantly enhancing the resilience and adaptability of NTN systems. Our architecture leverages Machine Learning to predict crucial metrics such as CPU usage, memory, and bandwidth, and pre-allocate virtual resources, thereby reducing latency associated with reactive orchestration methods. This proactive approach ensures optimal allocation of limited NTN resources, improves network performance, and mitigates cold-start delays of containers. Through experimental analysis of AI-based forecasting models and their integration into a simulation of CNF orchestration, demonstrating its potential to enable sustainable, scalable, and efficient CNF optimization tailored for 6G NTN solutions.

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NEW Scientific publication

Performance Evaluation of Fractional Frequency Reuse in Multi-Beam Satellite System

Shahid, Husnain1; Vázquez, Miguel Ángel1; Artiga, Xavier1; Sánchez-Fernández, Matilde; Callejas-Ramos, Álvaro

The ever-growing demand for communication services poses significant challenges in efficiently utilizing the limited spectrum resources, particularly in non-terrestrial networks (NTN). One major challenge in broadband satellite communication is inter-beam interference, appears when a certain frequency bin is reused among the coverage area. In essence, this results in complications to meet the quality of service (QoS) requirements. Fractional Frequency Reuse (FFR) has been widely adopted in traditional terrestrial networks (TN) to address such challenges but not been extensively explored in NTN due to constraints posed by legacy satellite payloads constructed by array fed reflectors. In the forthcoming broadband satellite missions, direct radiating arrays (DRA) will enable a flexible operation of the beams, allowing irregular deployments and mimicking the FFR schemes. This advancements provides an opportunity to dynamically adjust the inner to outer beam radius ratios (IOR), enabling the application of FFR principles in NTN. By this perspective, this paper extends the FFR paradigm into multi-beam satellite system under uniform user traffic conditions. Specifically, this paper investigates two FFR strategies within NTN, namely Strict FFR (S-FFR) and introducing a novel technique coined as Partially Strict FFR (PS-FFR) and conducts the evaluation across varying IOS and bandwidth distributions. The performance analysis indicates that PS-FFR outperforms the Strict FFR in terms of overall system capacity while both show a performance gain against so-called Frequency Reuse-4.

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NEW Scientific publication

Emerging Advancements in 6G NTN Radio Access Technologies: An Overview

Husnain Shahid, Carla Amatetti, Riccardo Campana, Sorya Tong, Dorin Panaitopol, Alessandro Vanelli-Coralli, Abdelhamed Mohamed, Chao Zhang , Ebraam Khalifa, Eduardo Medeiros, Estefania Recayte, Fatemeh Ghasemifard, Ji Lianghai, Juan Bucheli, Karthik Anantha Swamy, Marius Caus, Mehmet Gurelli, Miguel A. Vazquez, Musbah Shaat, Nathan Borios∥, Per-Erik Eriksson, Sebastian Euler , Zheng Li, Xiaotian Fu

The efforts on the development, standardization and improvements to communication systems towards 5G Advanced and 6G are on track to provide benefits such as an unprecedented level of connectivity and performance, enabling a diverse range of vertical services. The full integration of non-terrestrial components into 6G plays a pivotal role in realizing this paradigm shift towards ubiquitous communication and global coverage. However, this integration into 6G brings forth a set of its own challenges, particularly in Radio Access Technologies (RATs). To this end, this paper comprehensively discusses those challenges at different levels of RATs and proposes the corresponding potential emerging advancements in the realm of 6G NTN. In particular, the focus is on advancing the prospective aspects of Radio Resource Management (RRM), spectral coexistence in terrestrial and non-terrestrial components and exible waveform design solutions to combat the impediments. This discussion with a specic focus on emerging advancements in 6G NTN RATs is critical for shaping the next generation networks and potentially relevant in contributing the part in standardization in forthcoming releases.

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White paper

Vision on Non-Terrestrial Networks in 6G system (or IMT-2030)

Use Cases, Requirements, and Possible Standardisation approach: A Perspective from the 6G-NTN Project

Thales Alenia Space France, Ericsson Sweden and Ericsson France, Qualcomm France, SES Techcom, Thales – SIX, Telit Cinterion, GreenerWave, Martel Innovate, Digital for Planet, CTTC, German Aerospace Center – DLR, Alma Mater Studiorum – University of Bologna

In this white paper, the 6G-NTN consortium partners provide a consolidated view on NTN in 6G.

The paper includes an identification of the targeted market segments, connectivity scenarios, general design principles for NTN in 6G followed by a proposed standardization approach.

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Scientific Publication

Federated Cell-Free MIMO in Non-Terrestrial Networks: Architectures and Performance

Alessandro Guidotti, Alessandro Vanelli-Coralli, Carla Amatetti

While 5G networks are being rolled out, the definition of the potential 5G-Advanced features and the identification of disruptive technologies for 6G systems are being addressed by the scientific and academic communities to tackle the challenges that 2030 communication systems will face, such as terabit-capacity and always-on networks. In this framework, it is globally recognised that Non-Terrestrial Networks (NTN) will play a fundamental role in support to a fully connected world, in which physical, human, and digital domains will converge. In this framework, one of the main challenges that NTN have to address is the provision of the high throughput requested by the new ecosystem. In this paper, we focus on Cell-Free Multiple Input Multiple Output (CF-MIMO) algorithms for NTN. In particular: i) we discuss the architecture design supporting centralised and federated CF-MIMO in NTN, with the latter implementing distributed MIMO algorithms from multiple satellites in the same formation (swarm); ii) propose a novel location-based CF-MIMO algorithm, which does not require Channel State Information (CSI) at the transmitter; and iii) design novel normalisation approaches for federated CF-MIMO in NTN, to cope with the constraints on non-colocated radiating elements. The numerical results substantiate the good performance of the proposed algorithm, also in the presence of non-ideal information.

DOI: 10.48550/arXiv.2302.00057

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Conference Paper

Assessment of Beamforming Algorithms with Subarrayed Planar Arrays for B5G/6G LEO Non-Terrestrial Networks

M. Rabih Dakkak, Daniel Gaetano Riviello, Alessandro Guidotti, Alessandro Vanelli-Coralli at European Wireless 2023 – 6G Driving a Sustainable Growth – ITA NTN: Workshop on Integrated Terrestrial and Non-Terrestrial-Networks, October 2023

Non-Terrestrial Networks (NTNs) in Beyond 5G (B5G) and 6G ecosystems are expected to play a crucial role in providing the requests of connections anywhere and anytime by offering wide-area coverage and ensuring service availability, continuity, and scalability. Full Frequency Reuse (FFR) schemes, which are able in cooperation with digital beamforming algorithms to cope with the substantial co-channel interference, are considered to be an efficient solution to meet the growing demand of high data rates in B5G/6G systems. In this paper, we propose a Limited Field Of View (LFOV) planar array architecture composed of smaller planar subarrays in order to increase the directivity of an on-board Low Earth Orbit (LEO) satellite antenna array and mitigate the interference. We evaluate the performance of feed-space beamforming schemes, including both full digital schemes based on Channel State Information (CSI) at the transmitter, such as Minimum Mean Square Error (MMSE), and full analog schemes that only require the users’ locations, such as Conventional Beamforming (CBF). The numerical results of the system performance, presented by means of spectral efficiency, demonstrate a remarkable improvement in the proposed beamforming design with subarraying w.r.t. the one with no subarrayed configuration; in particular, we show that an analog beamforming scheme with subarraying can outperform a full digital beamforming scheme with no subarraying.

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Scientific publication

Open Datasets for AI-Enabled Radio Resource Control in Non-Terrestrial Networks

Husnain Shahid, Miguel Angel V´azquez ´, Laurent Reynaud , Fanny Parzysz, Musbah Shaat at “Symposium on Non-terrestrial Communications in Future Networks” at IEEE Future Network World Forum 2023​ – November 13, 2023 – Baltimore, USA

By effectively implementing the strategies for resource allocation, the capabilities, and reliability of non-terrestrial networks (NTN) can be enhanced. This leads to enhance spectrum utilization performance while minimizing the unmet system capacity, meeting quality of service (QoS) requirements and overall system optimization. In turn, a wide range of applications and services in various domains can be supported. However, allocating resources in a multi-constellation system with heterogeneous satellite links and highly dynamic user traffic demand pose challenges in ensuring sufficient and fair resource distribution. To mitigate these complexities and minimize the overhead, there is a growing shift towards utilizing artificial intelligence (AI) for its ability to handle such problems effectively. This calls for the development of an intelligent decision-making controller using AI to efficiently manage resources in this complex environment. In this context, real-world open datasets play a pivotal role in the development of AI models addressing radio control optimization problems. As a matter of fact, acquiring suitable datasets can be arduous. Therefore, this paper identifies pertinent real-world open datasets representing realistic traffic pattern, network performances and demand for fixed and dynamic user terminals, enabling a variety of uses cases. The aim of gathering and publishing the information of these datasets are to inspire and assist the research community in crafting the advance resource management solutions. In a nutshell, this paper establishes a solid foundation of commercially accessible data, with the potential to set benchmarks and accelerate the resolution of resource allocation optimization challenges.

DOI: 10.48550/arXiv.2404.12813

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Conference Proceeding

Analysis of Graph-Based User Scheduling for KA-Band LEO NTN Systems

Bilal Ahmad , Daniel Gaetano Riviello, Bruno De Filippo, Alessandro Guidotti , Alessandro Vanelli-Coralli

This work focuses on the next generations of multi-beam (MB) Low Earth Orbit (LEO) based NonTerrestrial Network (NTN) architectures that exploit full spectrum reuse schemes and effective interference management techniques by employing user scheduling before precoding at the transmit side. Given a high density of user terminals (UTs) on-ground as compared to on-board LEO satellite antennas it is extremely challenging to schedule users. To improve the performance, optimal user scheduling is necessary. In this paper, we present the analysis of graph-based user scheduling algorithms for Ka-band, with a focus on feed space (FS), downlink (DL), and Frequency Division Duplex (FDD) mode. We propose both a distance-based iterative graph-based approach and a distance-based implementation of the Multiple Antenna Downlink Orthogonal User Clustering (MADOC) algorithm. Instead of considering the Coefficient of Correlation (CoC) as a dissimilarity measure to organize users into clusters, we consider the great circle distance between the users as a dissimilarity metric. Once users are organized into clusters, they are served by the LEO satellite using Space Division Multiplexing (SDM) through Minimum Mean Square Error (MMSE) beamforming applied per cluster. Subsequently, these clusters are allocated to different time slots using Time Division Multiplexing (TDM). To validate the effectiveness of our presented approach, we conduct a comparative analysis with the original Channel State Information (CSI)-based MADOC algorithm which is based on the CoC. The results are presented in terms of the achievable per-user throughput (Mbps), and signal-to-noise plus interference ratio (SINR). By considering user scheduling and beamforming together, this study provides valuable insights into the effective use of resources in LEO MB-NTN systems.

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Scientific publication

RAN Functional Splits in NTN: Architectures and Challenges

Riccardo Campana, Carla Amatetti, and Alessandro Vanelli-Coralli

While 5G networks are already being deployed for commercial applications, Academia and industry are focusing their effort on the development and standardization of the next generations of mobile networks, i.e., 5G-Advance and 6G. Beyond 5G networks will revolutionize communications systems providing seamless connectivity, both in time and in space, to a unique ecosystem consisting of the convergence of the digital, physical, and human domains. In this scenario, NonTerrestrial Networks (NTN) will play a crucial role by providing ubiquitous, secure, and resilient infrastructure fully integrated into the overall system. The additional network complexity introduced by the third dimension of the architecture requires the interoperability of different network elements, enabled by the disaggregation and virtualization of network components, their interconnection by standard interfaces and orchestration by data-driven network artificial intelligence. The disaggregation paradigm foresees the division of the radio access network in different virtualized block of functions, introducing the concept of functional split. Wisely selecting the RAN functional split is possible to better exploit the system resources, obtaining costs saving, and to increase the system performances. In this paper, we firstly provide a discussion of the current 6G NTN development in terms of architectural solutions and then, we thoroughly analyze the impact of the typical NTN channel impairments on the available functional splits. Finally, the benefits of introducing the dynamic optimization of the functional split in NTN are analyzed, together with the foreseen challenges.

DOI: 10.48550/arXiv.2309.14810

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Scientific publication

Improved Graph-Based User Scheduling for Sum-Rate Maximisation in LEO-NTN Systems

Bilal Ahmad, Daniel Gaetano Riviello, Alessandro Guidotti, and Alessandro Vanelli-Coralli – 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) – June 04-10 2023 – Rhodes island, Greece

In this paper, we study the problem of user scheduling for Low Earth Orbit (LEO) Multi-User (MU) Multiple-Input-Multiple-Output (MIMO) Non-Terrestrial Network (NTN) systems with the objective of maximizing the sum-rate capacity while minimizing the total number of clusters. We propose an iterative graph-based maximum clique scheduling approach with constant graph density. Users are grouped together based on the channel coefficient of correlation (CoC) as dissimilarity metric and served by the satellite via Space Division Multiple Access (SDMA) by means of Minimum Mean Square Error (MMSE) digital beamforming on a cluster basis. Clusters are then served in different time slots via Time Division Multiple Access (TDMA). The results, presented in terms of per-cluster sum-rate capacity and per-user throughput, show that the presented approach can significantly improve the system performance.

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Scientific publication

O-RAN based Non-Terrestrial Networks: Trends and Challenges

Riccardo Campana, Carla Amatetti, and Alessandro Vanelli-Coralli – EuCNC & 6G Summit 2023 – June 6, 2023 – Gothenburg, Sweden

While 5G networks are already being deployed for commercial applications, Academia and industry are focusing their effort on the development and standardization of the next generations of mobile networks, i.e., 5G-Advance and 6G.

Beyond 5G networks will revolutionize communications systems providing seamless connectivity, both in time and in space, to a unique ecosystem consisting of the convergence of the digital, physical, and human domains. In this scenario, Non-Terrestrial Networks (NTN) will play a crucial role by providing ubiquitous, secure, and resilient infrastructure fully integrated into the overall system. The NTN nodes will be organized into a Multi- Layer Multi-dimensional (ML-MD) architecture. This ML-MD network will rely on the interoperability of very different network elements, enabled by the disaggregation and virtualization of network components, their interconnection by open standard interfaces and orchestrated by data-driven network Artificial Intelligence. This paradigm, which has been standardized by the O-RAN Alliance, is now being implemented in Terrestrial Networks (TNs) but has not been fully addressed in NTN, yet. Therefore, this paper aims at exploring the possible implementation of an NTN infrastructure based on the O-RAN approach. By starting with the review of the State of the Art of O-RAN in TNs and flying platforms, we identify a possible architecture solution for an O-RAN-based NTN system and we foresee the ORAN implementation trends that will increase the NTN system efficiency.

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