Global Navigation Satellite Systems (GNSS) provide the backbone of Positioning, Navigation, and Timing (PNT) but remain vulnerable to interference. Low Earth Orbit (LEO) constellations within Fifth-Generation (5G) Non-Terrestrial Networks (NTN) can enhance resilience by jointly supporting communication and navigation. This paper presents the first quantitative analysis of GNSS tracking and navigation message demodulation under a hybrid waveform where a low-power Direct-Sequence Spread Spectrum (DSSS) component is overlaid on an Orthogonal Frequency-Division Multiplexing (OFDM) 5G downlink. We evaluate a minimally modified GNSS receiver that tracks a legacy Global Positioning System (GPS) L1 Coarse/Acquisition (C/A) overlay aligned with 5G frames while treating the 5G waveform as structured interference. Using Monte Carlo simulations under realistic LEO Doppler dynamics, we analyze the Bit Error Rate (BER) of GPS L1 C/A navigation bits and the subframe decoding probability versus Signalto- Interference-plus-Noise Ratio (SINR) for multiple Signalto- Interference Ratios (SIR) and dynamic classes. Results show reliable demodulation across wide SINR ranges for low and medium dynamics, whereas high dynamics impose strict lock limits. These findings confirm the feasibility of Joint Communication and Positioning (JCAP) using a near-legacy GNSS chipset with minimal receiver modifications.

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 paper presents an intelligent device selection framework for Federated Learning (FL) in Low Earth Orbit (LEO) satellite constellation networks. In particular, the high mobility, intermittent connectivity and long communication delays in LEO satellite networks significantly impact FL convergence and model performance. To address these challenges, we formulate a device selection optimization problem that determines the optimal subset of LEO satellites to participate in global model aggregation at each round. The objective is to minimize the average loss while satisfying latency constraints. To solve the formulated problem, we develop a deep reinforcement learning (DRL)-based solution that enables adaptive satellite selection considering the stochastic nature of satellite connectivity and channel variability. The proposed framework selects participating satellites dynamically based on their communication latency and computational capabilities to improve the overall efficiency of the FL process. Simulation results show that the proposed approach accelerates convergence compared to conventional synchronous FL while maintaining model accuracy.

High peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) signals presents a persistent challenge in satellite communications (SatCom), impacting signal quality and causing adjacent channel interference. This paper introduces a novel framework that combines the elastic net-based machine learning (ML) model with the partial transmit sequence (PTS) technique to effectively reduce PAPR. Additionally, the potential of artificial intelligence (AI) approaches are investigated, specifically swarm intelligence and ML methods, for high-performance, low-complexity solutions. In this regard, ML models are applied to mitigate PAPR in SatCom networks under the presence of a traveling wave tube amplifier (TWTA) model and a land mobile satellite (LMS) channel, employing 16-quadrature amplitude modulation (16-QAM). Compared with the baseline schemes, simulation results demonstrate that the proposed ML framework, integrating principal component analysis (PCA) with the elastic net learning model, achieves comparable PAPR performance and minimal computational complexity.

Low Earth Orbit (LEO) satellite constellations consist of numerous satellites orbiting at different altitudes to serve diverse terrestrial users. Efficient resource management in such dynamic and large-scale networks presents a significant challenge. This paper studies Resource Blocks (RBs) and transmit power allocation at each LEO satellite, aiming at enhancing network performance while meeting Quality of Service (QoS) requirements. A stochastic optimization problem is formulated where the required QoS by each user is expressed as a chance constraint of the minimum data rate requirements considering network dynamics and uncertainties in channel conditions and traffic demands. The Conditional Value at Risk (CVaR) is employed to reformulate the chance constraint into a convex form and achieve a robust solution. Then, an alternating optimization approach is applied to solve the optimization problem. Through simulations using real data from the Starlink constellation, we demonstrate the efficacy of the proposed approach in improving network data rates while maintaining the required QoS levels.

Satellite communications (SatComs) have recently been through a renaissance, both technologically and entrepreneurially. Ambitious plans have already come into fruition with the operation of low-Earth orbit (LEO) constellations including thousands of satellites and supported by state of the art but proprietary technologies, such as active antenna arrays and intersatellite links (ISLs). In this context, this article aims to provide a forward-looking vision of use cases and a deep dive into technological enablers that will be prominent in space communications beyond 2030. In parallel, it motivates how open standards can play a role in delivering affordable communication services in space. Starting from the 5G plans for nonterrestrial networks, we provide a survey and roadmap toward artificial intelligence (AI)-supported satellite systems, space-enabled quantum networks, and joint communications and positioning (JCAP) for space missions and interplanetary exploration.

The 3rd Generation Partnership Project (3GPP) has integrated a positioning service into the 5G standard since its release 16 and has subsequently introduced support for Non-Terrestrial Networks (NTN). This paper explores the integration of these advancements within a unified framework to create a novel Low Earth Orbit (LEO)-based Positioning, Navigation, and Timing (PNT) service complementary to the existing Global Navigation Satellite System (GNSS). One major challenge in implementing this service is ensuring adequate coverage for users while minimizing interference between the signals from different LEO satellites, as the user requires to receive at least four signals to estimate its position.The Positioning Reference Signal (PRS) is a valuable positioning tool, particularly in scenarios with multiple transmitters. This study focuses on analyzing the interference issues arising when these transmitters are placed onboard LEO satellites. In this satellite context, the coverage area of the beam exceeds that of terrestrial environments, resulting in differential delay. Additionally, multiplexing PRS signals from multiple satellites within the same carrier reduces the Signal-to-Interference-plus-Noise ratio (SINR) owing to the orthogonality loss. To address these challenges, this study proposes a solution based on a Cell Averaging (CA)-Constant False Alarm Rate (CFAR) algorithm. The results of this study show the relationship between the minimum elevation angle of a satellite and the degradation of the SINR owing to other satellites.

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.

Passive Microwave remote sensing is a very energy-efficient and time-proven technology for remote sensing. Microwave Radiometry and Global Navigation Satellite System (GNSS)-Reflectometry are two widely-used examples. The increasing effects of Radio-Frequency Interferences (RFI) on the performance of these receivers have sparked serious concerns due to their proliferation. Detection and mitigation of RFI heavily rely on the identification and location of the interference sources. Due to the vast amount of degrees of freedom found in these RFIs, classification using classical signal processing techniques becomes problematic, especially if combined RFIs are found within the same bandwidth. In this work, continuous recordings of real RFI events from a real-time system to detect and locate RFI sources are used as training data for an automated classification system, the description and performance of which are presented and analyzed.

Enhancing reliability in high-throughput satellites (HTS) operating in geostationary orbit (GEO) is critical, particularly under adverse channel conditions. This study investigates the potential of multi-connectivity (MC) enabled by carrier aggregation (CA) to improve the data rate, ensuring a high level of reliability of multi-band $\mathrm{K a} / \mathrm{Ku}$ GEO HTS systems. A system and channel model for the multi-band GEO satellite system is developed, and an inter-band CA algorithm is proposed. This algorithm dynamically adjusts the user link transmission scheme based on channel quality and user requirements, ranging from a single Ka-band connectivity to MC, utilizing both bands with CA via packet duplication or packet splitting. The numerical results in various weather scenarios validate the effectiveness of the algorithm, demonstrating significant improvements in system performance, reduced outage probability, and improved overall system reliability. These findings highlight the importance of MC and multi-band technologies in future $\mathrm{6 G}$ networks.

During natural disasters such as earthquakes, wildfires, hurricanes, landslides, tsunamis, CBRNE (chemical, medical, radiological, nuclear, or explosive) incidents, or terrorist threats, critical infrastructure can be severely damaged, with rescue workers facing immense obstacles. Providing help and reaching affected areas can be hazardous for human rescue personnel. To overcome this issue, 5G-enabled UAVs can play a significant role in deploying disaster-resilient networks. Such networks imply multiple challenges, including end-to-end communication reliability and low latency for UAV real-time control. In this paper, we focus on the end-to-end communication aspects of such networks. We present and implement our disaster-resilient network based on a combination of 5G-UAVs and satellite networks. We highlight the related challenges and define solutions based on 5G, Multi-access Edge Computing, UAVs, and dynamic geofencing. We integrate these solutions in an end-to-end network architecture and ultimately deploy it on a national joint 5G-satellite infrastructure to assess its performance. It performs well and demonstrates a recovery time of less than 30 seconds with $\mathbf{9 9 \%}$ network availability.

The Internet of Vehicles (IoV) is undergoing swift advancements in capability and intelligence, poised to facilitate a diverse range of innovative applications. Within the IoV, edge learning empowers intelligent applications and services by leveraging data -driven tasks. Therefore, in this paper, we propose optimizing the edge learning error prediction within an edge-supported Non-Orthogonal Multiple Access (NOMA) in task-oriented communications. Specifically, we consider three autonomous vehicle perception tasks, called: object detection, traffic sign, and weather classification. For this purpose, we propose a novel approach based on the Particle Swarm Optimization (PSO) algorithm to jointly minimize the edge learning error and optimize power allocation variables. Moreover, we investigate alternative benchmark schemes, including Quantum Particle Swarm Optimization, Cuckoo Search, and Butterfly Op-timization algorithms. Satisfactorily, our simulations substantiate the superiority of the PSO algorithm over the baseline schemes, delivering superior performance with reduced computation time.

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 reliability of command and control (C2) operation of the UAV is one of the crucial aspects for the success of UAV applications beyond 5G wireless networks. In this paper, we focus on the short-packet communication to maximize the coverage range of reliable UAV control. We quantify the reliability performance of the C2 transmission from a multi-antenna ground control station (GCS), which also leverages maximal-ratio transmission beamforming, by deriving the closed-form expression for the average block error rate (BLER). To obtain additional insights, we also derive the asymptotic expression of the average BLER in the high-transmit power regime and subsequently analyze the possible UAV configuration space to find the optimum altitude. Based on the derived average BLER, we formulate a joint optimization problem to maximize the range up to which a UAV can be reliably controlled from a GCS. The solution to this problem leads to the optimal resource allocation parameters including blocklength and transmit power while exploiting the vertical degrees of freedom for UAV placement. Finally, we present numerical and simulation results to corroborate the analysis and to provide various useful design insights.

Proliferating applications of unmanned aerial vehicles (UAVs) impose new service requirements, leading to several challenges. One of the crucial challenges in this vein is to facilitate the autonomous navigation of UAVs. Concretely, the UAV needs to individually process the visual data and subsequently plan its trajectories. Since the UAV has limited onboard storage constraints, its computational capabilities are often restricted and it may not be viable to process the data locally for trajectory planning. Alternatively, the UAV can send the visual inputs to the ground controller which, in turn, feeds back the command and control signals to the UAV for its safe navigation. However, this process may introduce some delays, which is not desirable for autonomous UAVs’ safe and reliable navigation. Thus, it is essential to devise techniques and approaches that can potentially offer low-latency solutions for planning the UAV’s flight. To this end, this paper analyzes a multi-access edge computing aided UAV and aims to minimize the latency of the task processing. More specifically, we propose an offloading strategy for a UAV by optimally designing the offloading parameter, local computational resources, and altitude of the UAV. The numerical and simulation results are presented to offer various design insights, and the benefits of the proposed strategy are also illustrated in contrast to the other baseline approaches.

The new era of space exploration demands a significant increase in the number of human and robotic missions, thus resulting in novel communication and service requirements. To satisfy such requirements, the fifth generation of mobile communication systems (5G), despite providing connectivity on Earth, has the potential to serve as a communication standard for space resource missions, particularly the ones targeting the Moon. In fact, 5G non-terrestrial networks (NTNs) are already in the standardization process and new techniques are being proposed in order to counteract the peculiarities of the non-terrestrial channel. However, going one step ahead and deploying constellations of satellites around the Earth or the Moon, requires first a detailed analysis and testing of the validity of the proposed techniques. Therefore, in this paper, we introduce the 5G Space Communications Lab, which has been developed with the purpose of simulating space-based 5G communications. The designed testbed proposed here increases the technology readiness level (TRL) of NTN-based 5G systems, demonstrating over a laboratory environment successful 5G communication via space links.

This article presents a closed-loop differential phase compensation system for a precoding-enabled multibeam satellite forward link and its validation by live experiments on a GEO satellite scenario. The precoding operation avoids inter-beam interference and maximizes the spectrum efficiency by full frequency reuse as an alternative to the traditional two-color or four-color reuse methods proposed in the DVB-S2 standard. However, the satellite payload introduces differential phase and frequency impairments, which can degrade the precoding performance. This work describes the implementation of the differential phase and frequency tracking and compensation loop in an end-to-end testbed over a multibeam satellite system with independent local oscillators. The developed system performs end-to-end real-time communication over the satellite link, including channel measurements and precompensation. Results are validated by an over-the-air demonstration using two beams of the SES-14 multibeam satellite. Each beam is transmitted by independent transponders, which results in differential frequency and phase offsets due to the transponder undisciplined local oscillators. This phase offset makes it impossible to use precoding without the phase compensation loop. We prove that the implemented system can successfully track and compensate the differential phase and frequency to improve precoding performance.

This paper address the design of a phase tracking block for the DVB-S2X user terminals in a satellite precoding system. The spectral characteristics of the phase noise introduced by the oscillator, the channel, and the thermal noise at the receiver are taken into account. Using the expected phase noise mask, the optimal parameters for a second-order PLL intended to track channel variations from the pilots are calculated. To validate the results a Simulink model was implemented considering the characteristics of the hardware prototype. The performance of the design was evaluated in terms of the accuracy and stability for the frame structure of superframe Format 2, as described in Annex E of DVB-S2X.

Radio Frequency Interference (RFI) is a major concern for both real and synthetic aperture radiometers. After the lessons learnt from SMOS, ESA is preparing the next generation of L-band interferometric radiometers with RFI mitigation integrated into the cross-correlators. This work presents a preliminary design and results of a pre-correlation RFI mitigation algorithm tailored for interferometric radiometers. The results show that the correlation error introduced by the RFI is reduced on average to the half, with peaks of 20 dB of mitigation.

Distributed antenna arrays, fractionated payloads and cooperative platforms can provide unprecedented performance in the next generation of spaceborne communications and remote sensing systems. Remote phase synchronization of physically separated oscillators is the first step towards a coherent operation of distributed systems. This work shows the preliminary results of a TDD remote phase synchronization algorithm with a master-follower architecture. Herein, we describe the implementation and validation of the proposed algorithm. The implementation has been conducted in a Cubesat-ready software defined radio and validated at the end-to-end satellite communications testbed available at the University of Luxembourg.

Satellite Communications offer complementary benefits to terrestrial 5G/6G infrastructure, covering a wide range of use cases in need of ubiquitous coverage and reliability. However, to be as competitive as the terrestrial counterpart in terms of supplied throughput, satellite communications require a highly efficient use of the limited available spectrum. Linear precoding has demonstrated the ability to boost the spectral efficiency in the satellite domain, but raising a new issue: the bandwidth requirements of the feeder link. Deployment of several gateways, each of which precoding an independent cluster of beams causes performance degradation. Therefore, in this paper, we investigate the centralized gateway concept, where all digital baseband processes (including precoding) are implemented in a remote server connected via high speed fibers to the distributed remote gateways responsible for the downlink and uplink of the satellite radio frequency signals. In particular, we highlight the main technical challenges and provide a preliminary vision of potential solutions.

ESA's Soil Moisture and Ocean Salinity (SMOS) mission was launched 2 Nov 2009 and, to date, is still in good health, providing valuable L-band observations of the Earth surface [1]. A number of products are obtained from these, including thin sea ice [2], frost/thaw soils [3], high winds [4], ocean surface wind [5] and Sun brightness temperature [6], besides the main mission measurements of soil moisture and sea surface salinity [7] [8]. This paper deals with the description and early results of some technology activities conducted by ESA applying the lessons learnt by SMOS and in preparation of an advanced L-band radiometer mission.

Remote Sensing from distributed platforms has become attractive for the community in the last years. Phase, frequency, and time synchronization are a crucial requirement for many such applications as multi-static remote sensing and also for distributed beamforming for communications. The literature on the field is extensive, and in some cases, the requirements an complexity of the proposed synchronization solution may surpass the ones set by the application itself. Moreover, the synchronization solution becomes even more challenging when the nodes are flying or hovering on aerial or space platforms. In this work, we discuss the synchronization considerations for the implementation of distributed remote sensing applications. The general framework considered is based on a distributed collection of autonomous nodes that synchronize their clocks with a common reference using inter-satellite links. For this purpose, we implement a synchronization link between two nodes operating in a full-duplex fashion. The experimental testbed uses commercially available SDR platforms to emulate two satellites, two targets, and the communication channel. The proposal is evaluated considering phase and frequency errors for different system parameters.

Global Navigation Satellite System (GNSS) signals are used in Earth Observation for Radio Occultation and Reflectometry. The increasing effects of Radio-Frequency Interferences (RFI) on the performance of these receivers and navigation have suddenly sparked serious concerns due to their proliferation. Detection and mitigation of RFI heavily relies on the nature and location of the interfering sources. In some cases, null-steering or shielding can be used to mitigate RFI effects. In this work, a system to detect and locate RFI sources is presented, including signal classification and recording for countermeasure-related decision-making.

Phase, frequency and time synchronization is a crucial requirement for many applications as such as multi-static remote sensing and distributed beamforming for communications. The literature on the field is very wide, and in some cases, the requirements of the proposed synchronization solution may surpass the ones set by the application itself. Moreover, the synchronization solution becomes even more challenging when the nodes are flying or hovering on aerial or space platforms. In this work, we compare and classify the synchronization technologies available in the literature according to a common proposed framework, and we discuss the considerations of an implementation for distributed remote sensing applications. The general framework considered is based on a distributed collection of autonomous nodes that try to synchronize their clocks with a common reference. Moreover, they can be classified in non-overlapping, adjacent and overlapping frequency band scenarios.

The number of applications based on Global Navigation Satellite Systems (GNSS) has been increasing in the last years. With its proliferation, Radio-Frequency Interference (RFI) has become one of the most concerning topics on GNSS-based devices for navigation, positioning and timing, but also for Earth Observation purposes such as GNSS-Reflectometry and Radio Occultations due to the corruption of the received signal and the corresponding geophysical measurements. The Front-End GNSS Interference eXcisor (FENIX) mitigates the problem of RFI by providing a plug and play solution to excise interferences out of virtually any system. The new version of FENIX is also capable of operating with large signal bandwidths (~50 MHz) and dual-frequency applications. This paper shows the performance of the system for commercial GNSS receivers and GNSS-reflectometry applications. A discussion of the impact of the new architecture on its future capabilities is presented as well.

As the TDS-1 and CYGNSS spaceborne GNSS-R missions progress, a large number of Delay-Doppler Maps (DDM) is being collected, from the land scattered as well as from the ocean. Based on those data, the feasibility study on the retrieval of land geophysical parameters such as soil moisture and vegetation is being studied. This work presents the E2E simulator that generates the synthesized DDM reflected over land. For the efficient development, the existing simulator for ocean scattering has been upgraded to accommodate the specific functionalities for land scattering: topography, surface roughness, vegetation effects, in addition to surface soil moisture, obviously. The developed end-to-end (E2E) simulator is used to conceive the retrieval algorithms and assess their performance. In this work, the upgraded items are described and illustrated with some simulation results.

The3Cat-3/MOTS mission is a joint initiative between the Institut Cartogràfic i Geològic de Catalunya (ICGC) and the Universitat Politècnica de Catalunya-BarcelonaTech (UPC) to foster new and innovative technologies on Earth Observation (EO) based on the data fusion of Global Navigation Satellite Systems Reflectometry (GNSS-R) and optical payloads on a 6U CubeSat platform. The final objective of the mission is to provide soil moisture mapping through the GNSS-R payload and improve its resolution by data fusion with the multi-spectral optical data acquired. This technique has been successfully applied to SMOS, but never performed from a 6U CubeSat carrying both payloads. An airborne optical and GNSS-R field experiment has been carried out to test/develop the algorithm and to analyze the results both separately (GNSS-R + optical data) and as a soil moisture product, once performed the data fusion.

Global Navigation Satellite Systems - Reflectometry (GNSS-R) has shown his potential for remote sensing. Since new wider band signals are being broadcasted, and new systems are becoming available, a performance comparison in fair conditions between signals and system is required. For this purpose, the Universitat Politecnica de Catalunya has been developing the MIR instrument, an airborne GNSS reflec-to meter that mimics PARIS-IOD, and that also aims to better understand some GNSS-R techniques, the RFI analysis and mitigation, and the use of beamforming techniques to improve the GNSS-R capabilities. This work presents the advances in the instrument, the end-to-end tests, and the field campaign being planned in Australia for Spring, 2018.

In order to provide high resolution soil moisture maps, the Institut Cartografic i Geológic de Catalunya (ICGC) in colab-oration with Universitat Politecnica de Catalunya are planing to deploy the3Cat-3 picosatellite, which aims to estimate soil moisture from space using Global Navigation Satellite Systems - Reflectometry (GNSS-R) and to downscale it using data fusion with a multi-spectral camera. As a proof of concept, a field campaign is being carried out in Lleida, Catalunya. MALYGNSS is a GNSS reflectometer flying as an opportunity sensor that will validate the main reflectometer of the project: the CORTO instrument.

The maximum coherent integration time of GNSS reflected (GNSS-R) signals over sea depends on the sea state, as well as on the geometry formed by the receiver, the GNSS satellite and the reflection point. In this paper, real data obtained with a dedicated airborne instrument named MALYGNSS (Multi-band Airborne L-Band reflectometrY with GNSS) is used to acquire GPS L1 C/A and L2C, and Galileo E1OS signals. The direct signals are used as a reference to detect any phase shift due to data bits changes and the presence of a residual Doppler frequency. These phase transitions are removed in the reflected signals, and coherent correlations of different lengths are performed in order to study sea correlation time.

Radio-Frequency Interference (RFI) is one of the most concerning topics in Global Navigation Satellite Systems (GNSS) and GNSS - Reflectometry (GNSS-R) because any undesired signal present at the antenna can corrupt the received signal and the eventual geophysical measurements. The purpose of this paper is two-fold. First, an RFI analysis is performed to the raw data obtained from an airborne dual-band GNSS reflectometer. Finally, the Front-End GNSS Interference eXcisor (FENIX) mitigation algorithm is applied to the raw data, and then the Signal-to-Noise Ratio (SNR) improvement at the Delay-Doppler Map (DDM) is obtained. From the analysis of one of the datasets, two RFI signals were detected and characterized. Moreover, FENIX output signal was revealed to be much cleaner than before. The mean SNR improvement with coherent DDMs of 1 ms is 0.72 dB, whereas the SNR improvement increases to 1.49 dB when considering 50 incoherent averages. This improvement represents a reduction in the geophysical parameter estimation uncertainty.

Global Navigation Satellite Systems - Reflectometry (GNSS-R) has opened promising results for remote sensing applications. The broadcast of new signals with wider bandwidth provides an opportunity to increase the performance of GNSS reflectometry applications. The Microwave Interferometric Reflectometer (MIR) is an instrument being developed to compare different GNSS-R techniques, namely the conventional and interferometric ones, and assess the capabilities of these new signals and bands of the GPS and Galileo systems. This work describes the tests to validate the beamforming system.

The demonstration of the feasibility of navigation signals signals for remote sensing has lead to a significant increase of new GNSS-R instruments in the recent years. Despite of the type instrument, the observables are based on the cross-correlations of the received GNSS signals. Most of these instruments combine the signals received by multiple chains, in such a way that they need a proper calibration. This work proposes a calibration procedure for GNSS-R instruments by using a calibration signal with similar statistics to the GNSS signals. The method is tested and validated with the Microwave Interferometric Reflectometer (MIR) instrument, which contains 152 chains. The test results will be presented at the conference.

Radio-Frequency Interference (RFI) is a growing problem for applications based on the Global Navigation Satellite Systems (GNSS), including Earth observation using GNSS - Reflectometry (GNSS-R). Nowadays, many efforts are being concentrated to develop RFI signal detectors with high sensitivity, which can help to control the proliferation of RFI generators or jammers. This work aims at designing and testing an instrument capable of detecting and localizing RFI signals at GNSS bands using a microwave radiometer architecture, and a combination of statistical and time-frequency digital detection techniques. The instrument has been tested using different kinds of RFI signals, and its performance has been evaluated as a function of the RFI power. A sensitivity of -135 dBm for continuous wave and chirp signals is reported.

This work shows a technique to estimate the vegetation opacity and infer the vegetation water content using a single GPS receiver located under the vegetation layer being analyzed. It is based on the measurement of the received signal power when the GPS satellite is at the zenith, and when the GPS satellite is with an elevation of 30 degrees. Assuming a horizontally stratified vegetation layer, the attenuation on the GPS signal due to vegetation doubles at 30° elevation angle compared to the zenith. This attenuation is directly related to the vegetation opacity. Then, the vegetation opacity is related to the vegetation water content by a linear parameter. Initial results relating the measured GPS signal power to canopy parameters obtained from hemispheric photos are shown.

The GNSS rEflectometry, Radio Occultation and Scatterometry onboard the International Space Station (GEROS-ISS) is an innovative experiment for climate research, proposed in 2011 within a call of the European Space Agency (ESA) for installation at the ISS. This international proposal was the only one selected for further studies by ESA out of ~25 submitted ones. In this work, an updated assessment of the instrument performance for the near-nadir altimetry (GNSS-R) mode and scatterometry is assessed, including the effect of the inter-modulation signals, and the expected degradation due to presence of radio-frequency interference coming from DME/TACAN systems. These results correspond to the study lead by Airbus Defence and Space-Spain, one of the two parallel Phase A studies conducted by ESA.

Global Navigation Satellite Systems Reflectometry (GNSSR) is now being recognized as a powerfull tool for remote sensing. With the addition of new frequency bands and codes, it is possible to increase the resolution and accuracy of the measurements. GPS L5 and Galileo E5 bands are becoming popular because of the open codes, with wider bandwidths than the L1 band ones. However, these bands are shared with two radio navigation systems (DME and TACAN) that transmit with high powers and might interfere them. The use of mitigation techniques may affect the power of the received signal, which will affect the GNSS-R observables. This work presents several the mitigation techniques, and evaluates the performance and impact of the selected ones for ESA's GEROS experiment onboard the International Space Station.

This work presents an improvement of the P2EPS end-to-end simulator to assess the performances of Global Navigation Satellite System Reflectometry (GNSS-R) space missions over land. Beyond the single specular reflection model, topography effects are included. In order to avoid the computational burden of facet approach based on the bi-static radar model, the Phong reflection model is employed. By calculating the delay, Doppler frequency, and relative intensity of scattered points, the topography effects can be accounted for as a sort of impulse response in the delay, and Doppler domains to be convolved with the Woodward Ambiguity function.

The GNSS rEflectometry, Radio Occultation and Scatterometry onboard the International Space Station (GEROS-ISS) experiment consists of the installation of a GNSS reflectometry (GNSS-R) instrument on the International Space Station (ISS). Due to the complex structure of ISS, the GEROS-ISS instrument suffers from multipath effects. The collected signals are not only from received directly, but also after multiple reflections in the ISS structure. This work analyzes the multipath effect of the GEROS-ISS instrument based on the ISS structure. Using the ray tracing simulation, all rays reaching the instrument after one or two reflections are computed, as well as the corresponding additional delays. This information, together with the direction of arrival to the antenna arrays (up and down-looking), and the co- and cross-polar antenna patterns is used to assess the impact of multipath on the waveforms are demonstrated.

This work presents the first Delay Doppler Maps (DDMs) obtained with the Microwave Interferometric Reflectometer (MIR) airborne instrument. MIR is a GNSS Reflectometer (GNSS-R) which uses the conventional (cGNSS-R) and interferometric (iGNSS-R) techniques for ocean and land observation. It computes real-time sample-to-sample complex DDMs with averaging and alignment, of up to 4 simultaneous GPS/Galileo satellites at the L1/E1, and L5/E5a bands. Satellites are tracked with high directivity antennas with beam steering. The MIR instrument is designed to validate and compare both GNSS-R techniques with the new available signals, with special focus in the GEROS-ISS payload, and to contribute to the development of geophysical retrieval algorithms.

The number of applications based on Global Navigation Satellite Systems (GNSS) has been increasing in last years. In the passive remote sensing field, GNSS-Reflectometry (GNSS-R) systems have been revealed as cost-effective solutions to retrieve a number of geophysical parameters. However, GNSS-based devices are prone to suffer from Radio-Frequency Interference (RFI) effects due to the extreme low power of involved signals. This paper shows the preliminary results of the Front-End GNSS Interference eXcisor (FENIX), a system designed to mitigate the RFI problem in GNSS-based systems. FENIX is composed by two stages: the RF stage and the SP stage. The former conditions the signal from the antenna, throw the SP stage, and to the receiver; and the latter excises the RFI from the GNSS signal combining thresholding with the Multiresolution Fourier Transform (MFT). Preliminary results show that FENIX can mitigate up to 30 dB a chirp RFI signal generated from commercial jammers.

The empirical results of a global navigation satellite systems reflectometry (GNSS-R) experiment onboard the Balloon EXperiments for University Students (BEXUS) 17 stratospheric balloon performed north of Sweden over boreal forests show that the power of the reflected signals is nearly independent of the platform height for a high coherent integration time Tc= 20 ms. This experimental evidence shows a strong coherent component in the forward scattered signal, as compared with the incoherent component, that allows to be tracked. The bistatic coherent reflectivity is also evaluated as a function of the elevation angle, showing a decrease of ~6 dB when the elevation angle increases from 35° to 70°. The received power presents a clearly multimodal behavior, which also suggests that the coherent scattering component may be taking place in different forest elements, i.e., soil, canopy, and through multiple reflections canopy-soil and soil-trunk. This experiment has provided the first GNSS-R data set over boreal forests. The evaluation of these results can be useful for the feasibility study of this technique to perform biomass monitoring that is a key factor to analyze the carbon cycle.

This work presents two field campaigns performed between December 2014 and June 2015 in order to better understand the behavior of Global Navigation Satellite Systems (GNSS) reflected signals over land. The instruments used in both field campaigns are briefly introduced. One of them is focused on circular polarization, whereas the other is focused on linear polarization (horizontal and vertical). In one field experiment there is evidence of multipath due to the antenna performance. A method to mitigate it is proposed in this work. Finally, some preliminary soil moisture estimations using vertical polarization are shown.

The MIR (Microwave Interferometric Reflectometer) is a new dual frequency Global Navigation Satellite Systems Reflectometry (GNSS-R) instrument that implements conventional and interferometric reflectometry, both with GPS and Galileo signals. This paper presents the instrument development progress, mainly in the control part, where a methodology to determine how much the platform attitude will affect the beam steering and a satellite selection algorithm have been developed.

Radio-Frequency Interference (RFI) is a growing problem specially for those systems that work with low power signals such as passive remote sensing instruments. Consequently, RFI mitigation techniques are currently under development. This works aims at evaluating back-end mitigation algorithms in terms of their probability of detection and mitigation performance. Results show that Wavelet Denoising (WD), and Multiresolution Fourier Transform (MFT) are the best techniques in most scenarios, specially for GNSS-based instruments.

Reflectometry using Global Navigation Satellite System's signals of opportunity (GNSS-R) was originally conceived in the early 90s for mesoscale altimetry, and since then, many studies have shown its applicability to other remote sensing applications. In 2002, the Remote Sensing Lab at the Universitat Politècnica de Catalunya started working in Global Navigation Satellite Reflectometry (GNSS-R) to perform a more accurate correction of the sea state when retrieving sea surface salinity from L-band microwave radiometer data (e.g. SMOS). During these 12 years, the lab has developed ground-based, airborne, balloon, and spaceborne GNSS-R payloads for a number of different applications. This paper provides an overview of the families of instruments, and the design choices made.

Universitat Politècnica de Catalunya (UPC) - BarcelonaTech Remote Sensing Laboratory focus on the development of breakthrough concepts. The3Cat-2 mission is on the synergy of GNSS reflectometry and the CubeSat concept. Scientifically valuable mission data will improve our understanding on the Earth's environment. In particular, ocean currents need further investigation because of the spatio-temporal evolution of the mesoscale phenomena. The spacecraft development work includes two stratospheric flights in the frame of the REXUS/BEXUS programme coordinated by the European Space Agency (ESA). Coherent and incoherent scattering of Earth-reflected GNSS signals as sensed by the P(Y) & C/A ReflectOmeter (PYCARO) payload will be evaluated using the main state-of-the art Global Navigation Satellite Systems Reflectometry (GNSS-R) methodologies. Future spaceborne activities could take advantage of it, including the GNSS REflectometry, Radio Occultation and Scatterometry on-board International Space Station (GEROS-ISS) experiment.

RFI (Radio Frequency Interference) signals are a threat for GNSS-R (Global Navigation Satellite Systems - Reflectometry) due to the very low power of the navigation signals. Interference signals coming from computers or high-speed buses may affect GNSS-R measurements introducing a bias or even corrupting them completely. These RFI may be in-band or near-band and can desensitize GNSS and GNSS-R receivers. A number of techniques are currently under development to mitigate the effect of RFI signals.

A powerful real time Radio-frequency interference (RFI) mitigation back-end system is presented in this work. The interfering signals are estimated by using the wavelet transform to utilize its effective denoising capability. The estimated RFI signals are then subtracted from the total received signal, and ultimately the RFI-mitigated signal is obtained. Here, the hardware implementation and the system tests are demonstrated, showing sinusoid, chirp, and PRN interference mitigation under different Interference to Noise Ratios (INRs).

Microwave radiometers are very sensitive passive sensors that measure the power of the thermal noise within a determined bandwidth. Therefore, any other signal present in the band modifies the value of the measured power, and the corresponding estimated antenna temperature, from which the geophysical parameters are retrieved. Due to the high sensitivity and accuracy required for these instruments, radio frequency interference (RFI) is becoming more and more a serious problem. On one hand, ground-based or global RFI surveys are helping to understand the occurrence and types of RFI sources. If RFI does not necessarily affect the whole bandwidth, or it is not present during the whole integration time, the application of either frequency blanking, time blanking or signal spectrogram techniques can be applied. However, it would be desirable to apply techniques to estimate the RFI signal so that it can be subtracted from the received signal itself so that some useful measurements are still possible. Such a real-time system is currently being developed for RFI detection and mitigation. This work focuses however in the description and performance of a wavelet-based RFI-mitigation technique implemented in a FPGA hardware back-end. The interfering signal is estimated by using the powerful denoising capabilities of the wavelet transform, and it is then subtracted from the total received signal to obtain a RFI-mitigated noise signal.