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In this paper, optimal linear precoding for the multibeam geostationary earth orbit (GEO) satellite with the multi-user (MU) multiple-input-multiple-output (MIMO) downlink scenario is addressed. Multiple-user interference is one of the major issues faced by the satellites serving the multiple users operating at the common time-frequency resource block in the downlink channel. To mitigate this issue, the optimal linear precoders are implemented at the gateways (GWs). The precoding computation is performed by utilizing the channel state information obtained at user terminals (UTs). The optimal linear precoders are derived considering beamformer update and power control with an iterative per-antenna power optimization algorithm with a limited required number of iterations. The efficacy of the proposed algorithm is validated using the In-Lab experiment for 16X16 precoding with multi-beam satellite for transmitting and receiving the precoded data with digital video broadcasting satellite-second generation extension (DVB- S2X) standard for the GW and the UTs. The software defined radio platforms are employed for emulating the GWs, UTs, and satellite links. The validation is supported by comparing the proposed optimal linear precoder with full frequency reuse (FFR), and minimum mean square error (MMSE) schemes. The experimental results demonstrate that with the optimal linear precoders it is possible to successfully cancel the inter-user interference in the simulated satellite FFR link. Thus, optimal linear precoding brings gains in terms of enhanced signal-to-noise-and-interference ratio, and increased system throughput and spectral efficiency.

The careful planning and safe deployment of 5G technologies will bring enormous benefits to society and the economy. Higher frequency, beamforming, and small-cells are key technologies that will provide unmatched throughput and seamless connectivity to 5G users. Superficial knowledge of these technologies has raised concerns among the general public about the harmful effects of radiation. Several standardization bodies are active to put limits on the emissions which are based on a defined set of radiation measurement methodologies. However, due to the peculiarity of 5G such as dynamicity of the beams, network densification, Time Division Duplexing mode of operation, etc, using existing EMF measurement methods may provide inaccurate results. In this context, we discuss our experimental studies aimed towards the measurement of radiation caused by beam-based transmissions from a 5G base station equipped with an Active Antenna System(AAS). We elaborate on the shortcomings of current measurement methodologies and address several open questions. Next, we demonstrate that using user-specific downlink beamforming, not only better performance is achieved compared to non-beamformed downlink, but also the radiation in the vicinity of the intended user is significantly decreased. Further, we show that under weak reception conditions, an uplink transmission can cause significantly high radiation in the vicinity of the user equipment. We believe that our work will help in clearing several misleading concepts about the 5G EMF radiation effects. We conclude the work by providing guidelines to improve the methodology of EMF measurement by considering the spatiotemporal dynamicity of the 5G transmission.

The Artificial Intelligence Satellite Telecommunications Testbed (AISTT), part of the ESA project SPAICE, is focused on the transformation of the satellite payload by using artificial intelligence (AI) and machine learning (ML) methodologies over available commercial off-the-shelf (COTS) AI-capable chips for onboard processing. The objectives include validating artificial intelligence-driven SATCOM scenarios such as interference detection, spectrum sharing, radio resource management, decoding, and beamforming. The study highlights hardware selection and payload architecture. Preliminary results show that ML models significantly improve signal quality, spectral efficiency, and throughput compared to conventional payload. Moreover, the testbed aims to evaluate the performance and the use of AI-capable COTS chips in onboard SATCOM contexts., Comment: 6 pages, 4 figures

Accurate asset localization holds paramount importance across various industries, ranging from transportation management to search and rescue operations. In scenarios where traditional positioning equations cannot be adequately solved due to limited measurements obtained by the receiver, the utilization of Non-Terrestrial Networks (NTN) based on Low Earth Orbit (LEO) satellites can prove pivotal for precise positioning. The decision to employ NTN in lieu of conventional Global Navigation Satellite Systems (GNSS) is rooted in two key factors. Firstly, GNSS systems are susceptible to jamming and spoofing attacks, thereby compromising their reliability, where LEO satellites link budgets can benefit from a closer distances and the new mega constellations could offer more satellites in view than GNSS. Secondly, 5G service providers seek to reduce dependence on third-party services. Presently, the NTN operation necessitates a GNSS receiver within the User Equipment (UE), placing the service provider at the mercy of GNSS reliability. Consequently, when GNSS signals are unavailable in certain regions, NTN services are also rendered inaccessible., Comment: Our ongoing research, closely related to our manuscript, promises crucial insights enhancing our findings. Committed to scientific quality and integrity, we refrain from submitting incomplete work to avoid misleading the scientific community

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.

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 paper

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 conference

This paper delves into the application of Machine Learning (ML) techniques in the realm of 5G Non-Terrestrial Networks (5G-NTN), particularly focusing on symbol detection and equalization for the Physical Broadcast Channel (PBCH). As 5G-NTN gains prominence within the 3GPP ecosystem, ML offers significant potential to enhance wireless communication performance. To investigate these possibilities, we present ML-based models trained with both synthetic and real data from a real 5G over-the-satellite testbed. Our analysis includes examining the performance of these models under various Signal-to-Noise Ratio (SNR) scenarios and evaluating their effectiveness in symbol enhancement and channel equalization tasks. The results highlight the ML performance in controlled settings and their adaptability to real-world challenges, shedding light on the potential benefits of the application of ML in 5G-NTN.

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 conference

Interest in the integration of Terrestrial Networks (TN) and Non-Terrestrial Networks (NTN); primarily satellites; has been rekindled due to the potential of NTN to provide ubiquitous coverage. Especially with the peculiar and flexible physical layer properties of 5G-NR, now direct access to 5G services through satellites could become possible. However, the large Round-Trip Delays (RTD) in NTNs require a re-evaluation of the design of RLC and PDCP layers timers ( and associated buffers), in particular for the regenerative payload satellites which have limited computational resources, and hence need to be optimally utilized. Our aim in this work is to initiate a new line of research for emerging NTNs with limited resources from a higher-layer perspective. To this end, we propose a novel and efficient method for optimally designing the RLC and PDCP layers' buffers and timers without the need for intensive computations. This approach is relevant for low-cost satellites, which have limited computational and energy resources. The simulation results show that the proposed methods can significantly improve the performance in terms of resource utilization and delays.

Sophisticated antenna technologies are constantly evolving to meet the escalating data demands projected for 6G and future networks. The characterization of these emerging antenna systems poses challenges that necessitate a reevaluation of conventional techniques, which rely solely on simple measurements conducted in advanced anechoic chambers. In this study, our objective is to introduce a novel endeavour for antenna pattern characterization (APC) in next-generation multiple-input-multiple-output (MIMO) systems by utilizing the potential of signal processing tools. In contrast to traditional methods that struggle with multi-path scenarios and require specialized equipment for measurements, we endeavour to estimate the antenna pattern by exploiting information from both line-of-sight (LoS) and non-LoS contributions. This approach enables antenna pattern characterization in complex environments without the need for anechoic chambers, resulting in substantial cost savings. Furthermore, it grants a much wider research community the ability to independently perform APC for emerging complex 6G antenna systems, without relying on anechoic chambers. Simulation results demonstrate the efficacy of the proposed novel approach in accurately estimating the true antenna pattern.

Intelligent reflecting surfaces (IRS) have emerged as a promising technology to enhance the performance of wireless communication systems. By actively manipulating the wireless propagation environment, IRS enables efficient signal transmission and reception. In recent years, the integration of IRS with full-duplex (FD) communication has garnered significant attention due to its potential to further improve spectral and energy efficiencies. IRS-assisted FD systems combine the benefits of both IRS and FD technologies, providing a powerful solution for the next generation of cellular systems. In this manuscript, we present a novel approach to jointly optimize active and passive beamforming in a multiple-input-multiple-output (MIMO) FD system assisted by an IRS for weighted sum rate (WSR) maximization. Given the inherent difficulty in obtaining perfect channel state information (CSI) in practical scenarios, we consider imperfect CSI and propose a statistically robust beamforming strategy to maximize the ergodic WSR. Additionally, we analyze the achievable WSR for an IRS-assisted MIMO FD system under imperfect CSI by deriving both the lower and upper bounds. To tackle the problem of ergodic WSR maximization, we employ the concept of expected weighted minimum mean squared error (EWMMSE), which exploits the information of the expected error covariance matrices and ensures convergence to a local optimum. We evaluate the effectiveness of our proposed design through extensive simulations. The results demonstrate that our robust approach yields significant performance improvements compared to the simplistic beamforming approach that disregards CSI errors, while also outperforming the robust half-duplex (HD) system considerably, Comment: arXiv admin note: substantial text overlap with arXiv:2308.08016

In this paper, a novel robust beamforming for an intelligent reflecting surface (IRS) assisted FD system is presented. Since perfect channel state information (CSI) is often challenging to acquire in practice, we consider the case of imperfect CSI and adopt a statistically robust beamforming approach to maximize the ergodic weighted sum rate (WSR). We also analyze the achievable WSR of an IRS-assisted FD with imperfect CSI, for which the lower and the upper bounds are derived. The ergodic WSR maximization problem is tackled based on the expected Weighted Minimum Mean Squared Error (WMMSE), which is guaranteed to converge to a local optimum. The effectiveness of the proposed design is investigated with extensive simulation results. It is shown that our robust design achieves significant performance gain compared to the naive beamforming approaches and considerably outperforms the robust Half-Duplex (HD) system.

The paradigm of joint communications and sensing (JCAS) envisions a revolutionary integration of communication and radar functionalities within a unified hardware platform. This novel concept not only opens up unprecedented possibilities, but also presents unique challenges. Its success is highly dependent on efficient full-duplex (FD) operation, which has the potential to enable simultaneous transmission and reception within the same frequency band. While ongoing research explores the potential of JCAS, there are related avenues of investigation that hold tremendous potential to profoundly transform the sixth generation (6G) and beyond cellular networks. This article sheds light on the new opportunities and challenges presented by JCAS by taking into account the key technical challenges of FD systems. Unlike simplified JCAS scenarios, we delve into the most comprehensive configuration, encompassing uplink (UL) and downlink (DL) users, as well as monostatic and bistatic radars, all harmoniously coexisting to jointly push the boundaries of both the communications and sensing performance. The performance improvements introduced by this advancement bring forth numerous new challenges, each meticulously examined and expounded upon.

The full-duplex (FD) technology has the potential to radically evolve wireless systems, facilitating the integration of both communications and radar functionalities into a single device, thus, enabling joint communication and sensing (JCAS). In this paper, we present a novel approach for JCAS that incorporates a reconfigurable intelligent surface (RIS) in the near-field of an FD multiple-input multiple-output (MIMO) node, which is jointly optimized with the digital beamformers to enable JSAC and efficiently handle self-interference (SI). We propose a novel problem formulation for FD MIMO JCAS systems to jointly minimize the total received power at the FD node's radar receiver while maximizing the sum rate of downlink communications subject to a Cram\'{e}r-Rao bound (CRB) constraint. In contrast to the typically used CRB in the relevant literature, we derive a novel, more accurate, target estimation bound that fully takes into account the RIS deployment. The considered problem is solved using alternating optimization, which is guaranteed to converge to a local optimum. The simulation results demonstrate that the proposed scheme achieves significant performance improvement both for communications and sensing. It is showcased that, jointly designing the FD MIMO beamformers and the RIS phase configuration to be SI aware can significantly loosen the requirement for additional SI cancellation.

This paper proposes a joint optimization framework for energy-efficient linear precoding and feeder-link-beam matching design in a multi-gateway multi-beam bent-pipe satellite communication system. The proposed scheme jointly optimizes the precoding vectors at the gateway antennas and amplifying-and-matching mechanism at the satellite to maximize the system-weighted energy efficiency under the transmit power budget constraint. The technical designs are formulated into a non-convex sparsity problem consisting of a fractional-form objective function and sparsity-related constraints. To address these challenges, two iterative efficient designs are proposed by utilizing the concepts of Dinkelbach's method and the compressed-sensing approach. The simulation results demonstrate the effectiveness of the proposed scheme compared to another benchmark method., Comment: ICC 2024

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.

Cohesive Distributed Satellite Systems (CDSS) is a key enabling technology for the future of remote sensing and communication missions. However, they have to meet strict synchronization requirements before their use is generalized. When clock or local oscillator signals are generated locally at each of the distributed nodes, achieving exact synchronization in absolute phase, frequency, and time is a complex problem. In addition, satellite systems have significant resource constraints, especially for small satellites, which are envisioned to be part of the future CDSS. Thus, the development of precise, robust, and resource-efficient synchronization techniques is essential for the advancement of future CDSS. In this context, this survey aims to summarize and categorize the most relevant results on synchronization techniques for DSS. First, some important architecture and system concepts are defined. Then, the synchronization methods reported in the literature are reviewed and categorized. This article also provides an extensive list of applications and examples of synchronization techniques for DSS in addition to the most significant advances in other operations closely related to synchronization, such as inter-satellite ranging and relative position. The survey also provides a discussion on emerging data-driven synchronization techniques based on ML. Finally, a compilation of current research activities and potential research topics is proposed, identifying problems and open challenges that can be useful for researchers in the field., Comment: submitted to IEEE Access

Full duplex (FD) is an auspicious wireless technology that holds the potential to double data rates through simultaneous transmission and reception. This paper proposes two innovative designs of hybrid beamforming (HYBF) for a multicell massive multiple-input-multiple-output (mMIMO) millimeter wave (mmWave) FD system. Initially, we introduce a novel centralized HYBF (C-HYBF) scheme, which employs the minorization-maximization (MM) method. However, while centralized beamforming designs offer superior performance, they suffer from high computational complexity, substantial communication overhead, and demand expensive computational resources. To surmount these challenges, we present a framework that facilitates per-link parallel and distributed HYBF (P&D- HYBF) in the mmWave frequency band. This cooperative approach enables each base station (BS) to independently solve its local, low-complexity sub-problems in parallel, resulting in a substantial reduction in communication overhead and computational complexity. Simulation results demonstrate that P&D-HYBF achieves comparable performance to C-HYBF, and with only a few radio-frequency (RF) chains, both designs surpass the capabilities of fully digital half duplex (HD) systems.

In this paper we demonstrate end-to-end precoded multi-user multiple-input single-output (MU-MISO) communications over a live GEO satellite link. Precoded communications enable full frequency reuse (FFR) schemes in satellite communications (SATCOM) to achieve broader service availability and higher spectrum efficiency than with the conventional four-color (4CR) and two-color (2CR) reuse approaches. In this scope, we develop an over-the-air test-bed for end-to-end precoding validations. We use an actual multi-beam satellite to transmit and receive precoded signals using the DVB-S2X standard based gateway and user terminals. The developed system is capable of end-to-end real-time communications over the satellite link including channel measurements and precompensation. It is shown, that by successfully canceling inter-user interference in the actual satellite FFR link precoding brings gains in terms of enhanced SINR and increased system goodput., Comment: Submitted to IEEE Access Journal

Phase, frequency, and time synchronization are crucial requirements for many applications, such as multi-static remote sensing and communication systems. Moreover, the synchronization solution becomes even more challenging when the nodes are orbiting or flying on airborne or spaceborne platforms. This paper compares the available technologies used for the synchronization and coordination of nodes in distributed remote sensing applications. Additionally, this paper proposes a general system model and identifies preliminary guidelines and critical elements for implementing the synchronization mechanisms exploiting the inter-satellite communication link. The distributed phase synchronization loop introduced in this work deals with the self-interference in a full-duplex point to point scenario by transmitting two carriers at each node. All carriers appear with different frequency offsets around a central frequency, called the application central-frequency or the beamforming frequency. This work includes a detailed analysis of the proposed algorithm and the required simulations to verify its performance for different phase noise, AWGN, and Doppler shift scenarios.

Future generation wireless networks are targeting the convergence of fixed, mobile and broadcasting systems with the integration of satellite and terrestrial systems towards utilizing their mutual benefits. Satellite Communications (Sat- Com) is envisioned to play a vital role to provide integrated services seamlessly over heterogeneous networks. As compared to terrestrial systems, the design of SatCom systems require a different approach due to differences in terms of wave propagation, operating frequency, antenna structures, interfering sources, limitations of onboard processing, power limitations and transceiver impairments. In this regard, this letter aims to identify and discuss important modeling and design aspects of the next generation High Throughput Satellite (HTS) systems. First, communication models of HTSs including the ones for multibeam and multicarrier satellites, multiple antenna techniques, and for SatCom payloads and antennas are highlighted and discussed. Subsequently, various design aspects of SatCom transceivers including impairments related to the transceiver, payload and channel, and traffic-based coverage adaptation are presented. Finally, some open topics for the design of next generation HTSs are identified and discussed., Comment: submitted to IEEE Journal