Non-Coherent Massive MIMO Integration in Satellite Communication

Massive Multiple Input-Multiple Output (mMIMO) technique has been considered an efficient standard to improve the transmission rate significantly for the following wireless communication systems, such as 5G and beyond. However, implementing this technology has been facing a critical issue of acquiring much channel state information. Primarily, this problem becomes more criticising in the integrated satellite and terrestrial networks (3GPP-Release 15) due to the countable high transmission delay. To deal with this challenging problem, the mMIMO-empowered non-coherent technique can be a promising solution. To our best knowledge, this paper is the first work considering employing the non-coherent mMIMO in satellite communication systems. This work aims to analyse the challenges and opportunities emerging with this integration. Moreover, we identified the issues in this conjunction. The preliminary results presented in this work show that the performance measured in bit error rate (BER) and the number of antennas are not far from that required for terrestrial links. Furthermore, thanks to mMIMO in conjunction with the non-coherent approach, we can work in a low signal-to-noise ratio (SNR) regime, which is an excellent advantage for satellite links

Non-Coherent Massive MIMO Integration in Satellite Communication

Massive Multiple Input-Multiple Output (mMIMO) technique has been considered an efficient standard to improve the transmission rate significantly for the following wireless communication systems, such as 5G and beyond. However, implementing this technology has been facing a critical issue of acquiring much channel state information. Primarily, this problem becomes more criticising in the integrated satellite and terrestrial networks (3GPP-Release 15) due to the countable high transmission delay. To deal with this challenging problem, the mMIMO-empowered non-coherent technique can be a promising solution. To our best knowledge, this paper is the first work considering employing the non-coherent mMIMO in satellite communication systems. This work aims to analyse the challenges and opportunities emerging with this integration. Moreover, we identified the issues in this conjunction. The preliminary results presented in this work show that the performance measured in bit error rate (BER) and the number of antennas are not far from that required for terrestrial links. Furthermore, thanks to mMIMO in conjunction with the non-coherent approach, we can work in a low signal-to-noise ratio (SNR) regime, which is an excellent advantage for satellite links

Harnessing Supervised Learning for Adaptive Beamforming in Multibeam Satellite Systems

In today's ever-connected world, the demand for fast and widespread connectivity is insatiable, making multibeam satellite systems an indispensable pillar of modern telecommunications infrastructure. However, the evolving communication landscape necessitates a high degree of adaptability. This adaptability is particularly crucial for beamforming, as it enables the adjustment of peak throughput and beamwidth to meet fluctuating traffic demands by varying the beamwidth, side lobe level (SLL), and effective isotropic radiated power (EIRP). This paper introduces an innovative approach rooted in supervised learning to efficiently derive the requisite beamforming matrix, aligning it with system requirements. Significantly reducing computation time, this method is uniquely tailored for real-time adaptation, enhancing the agility and responsiveness of satellite multibeam systems. Exploiting the power of supervised learning, this research enables multibeam satellites to respond quickly and intelligently to changing communication needs, ultimately ensuring uninterrupted and optimized connectivity in a dynamic world.Comment: under review for conferenc

Genetic Algorithm-based Beamforming in Subarray Architectures for GEO Satellites

The incorporation of subarrays in Direct Radiating Arrays for satellite missions is fundamental in reducing the number of Radio Frequency chains, which correspondingly diminishes cost, power consumption, space, and mass. Despite the advantages, previous beamforming schemes incur significant losses during beam scanning, particularly when hybrid beamforming is not employed. Consequently, this paper introduces an algorithm capable of compensating for these losses by increasing the power, for this, the algorithm will activate radiating elements required to address a specific Effective Isotropic Radiated Power for a beam pattern over Earth, projected from a GeoStationary satellite. In addition to the aforementioned compensation, other beam parameters have been addressed in the algorithm, such as beamwidth and Side Lobe Levels. To achieve these objectives, we propose employing the array thinning concept through the use of genetic algorithms, which enable beam shaping with the desired characteristics and power. The full array design considers an open-ended waveguide, configured to operate in circular polarization within the Ka-band frequency range of 17.7-20.2 GHz

Flexible Payload Configuration for Satellites using Machine Learning

Satellite communications, essential for modern connectivity, extend access to maritime, aeronautical, and remote areas where terrestrial networks are unfeasible. Current GEO systems distribute power and bandwidth uniformly across beams using multi-beam footprints with fractional frequency reuse. However, recent research reveals the limitations of this approach in heterogeneous traffic scenarios, leading to inefficiencies. To address this, this paper presents a machine learning (ML)-based approach to Radio Resource Management (RRM). We treat the RRM task as a regression ML problem, integrating RRM objectives and constraints into the loss function that the ML algorithm aims at minimizing. Moreover, we introduce a context-aware ML metric that evaluates the ML model's performance but also considers the impact of its resource allocation decisions on the overall performance of the communication system.Comment: in review for conferenc

Supervised Learning Based Real-Time Adaptive Beamforming On-board Multibeam Satellites

Satellite communications (SatCom) are crucial for global connectivity, especially in the era of emerging technologies like 6G and narrowing the digital divide. Traditional SatCom systems struggle with efficient resource management due to static multibeam configurations, hindering quality of service (QoS) amidst dynamic traffic demands. This paper introduces an innovative solution - real-time adaptive beamforming on multibeam satellites with software-defined payloads in geostationary orbit (GEO). Utilizing a Direct Radiating Array (DRA) with circular polarization in the 17.7 - 20.2 GHz band, the paper outlines DRA design and a supervised learning-based algorithm for on-board beamforming. This adaptive approach not only meets precise beam projection needs but also dynamically adjusts beamwidth, minimizes sidelobe levels (SLL), and optimizes effective isotropic radiated power (EIRP).Comment: conference pape

Study of the correlation between columnar aerosol burden, suspended matter at ground and chemical components in a background European environment

Although routinely monitored by ground based air quality networks, the particulate matter distribution could be eventually better described with remote sensing techniques. However, valid relationships between ground level and columnar ground based quantities should be known beforehand. In this study we have performed a comparison between particulate matter measurements at ground level at different cut sizes (10, 2.5 and 1.0 mm), and the aerosol optical depth obtained by means of a ground based sunphotometer during a multiinstrumental field campaign held in El Arenosillo (Huelva, Spain) from 28 June to 4 July 2006. All the PM fractions were very well correlated with AOD with correlation coefficients that ranged from 0.71 to 0.81 for PM10, PM2.5 and PM1. Furthermore, the influence of the mixing layer height in the correlations was explored. The improvement in the correlation when the vertical distribution is taken into account was significant for days with a homogeneous mixing layer. Moreover, the chemical analysis of the individual size fractions allowed us to study the origin of the particulate matter. Secondary components were the most abundant and also well correlated in the three size fractions; but for PM10 fraction, chemical species related to marine origin were best correlated. Finally, we obtained a relationship between MODIS L3 AOD from collection 5.1 and the three PM cut sizes. In spite of being a relatively clean environment, all the techniques were able to capture similar day to day variations during this field campaign.Peer ReviewedPostprint (published version

Prevalence of Pathogenic Variants in Cardiomyopathy-Associated Genes in Myocarditis.

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Metodología docente y nuevos recursos en Arqueología Prehistórica

La idea ha sido crear un instrumento capaz de albergar información y documentación docente que irá aumentando progresivamente, en función de las necesidades didácticas que puedan ir surgiendo en las asignaturas citadas, y que periódicamente debe ser revisado, por las constantes actualizaciones que sufran los enlaces seleccionados