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Pol Henarejos

Publications and source records attributed to Pol Henarejos.

At least 19 recordsLinked to original sources

Vaulted Passkeys: A Device-Bound Proposal for Authenticated Credential Export and Import

Hardware authenticators deliberately resist private-key extraction, yet replacement, disaster recovery, and controlled migration create a legitimate need for portability. Existing guidance for device-bound credentials commonly reduces recovery risk by registering an additional authenticator before failure. That creates an independent credential registration and requires replacement hardware to exist in advance; it is redundancy, not a backup of the original credential. This paper addresses the resulting recovery gap by exporting protected credential state while the source is available and restoring it to hardware acquired later, without cloning a complete authenticator or exposing plaintext private keys to routine desktop software. We propose Vaulted Passkeys, a device-bound architecture in which a random 256-bit Kvault protects authenticated PKV1 credential envelopes through HKDF-separated keys and four explicit AEAD profiles. The design separates enrollment from export/import and the required vault from optional identity. We contribute a role-separated system model, wire format, threat analysis, implementation mapping, and falsifiable evaluation plan. The prototype demonstrates feasibility but is neither a formal security proof nor a proposed final standard.

cs.CR

From Design to Validation: Preparing a LEO-Capable UE for End-to-End System Evaluation

The extension of 5G connectivity through Low-Earth Orbit satellite systems introduces significant technical challenges, particularly due to time-varying propagation delays and high Doppler shifts resulting from satellite motion. While the Third Generation Partnership Project Release 17 established the initial framework for non-terrestrial networks, the ongoing developments in Release 19 further enhance this effort by introducing support for regenerative payload architectures, where part of the communication protocol stack is processed directly on board the satellite. In this work, we present the design of a 5G user equipment adapted for Low-Earth Orbit satellite connectivity, with specific focus on strategies for managing variable delay and Doppler compensation. Additionally, we describe a custom experimental platform based on a drone-mounted software-defined radio platform capable of emulating both transparent and regenerative satellite payloads. Although full end-to-end system validation is not yet complete, initial laboratory tests confirm the feasibility of the architecture and lay the groundwork for future experimental campaigns.

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Open Datasets for Satellite Radio Resource Control

In Non-Terrestrial Networks (NTN), achieving effective radio resource allocation across multi-satellite system, encompassing efficient channel and bandwidth allocation, effective beam management, power control and interference mitigation, poses significant challenges due to the varying satellite links and highly dynamic nature of user traffic. This calls for the development of an intelligent decision-making controller using Artificial Intelligence (AI) to efficiently manage resources in this complex environment. In this context, open datasets can play a crucial role in driving new advancement and facilitating research. Recognizing the significance, this paper aims to contribute the satellite communication research community by providing various open datasets that incorporate realistic traffic flow enabling a variety of uses cases. The primary objective of sharing these datasets is to facilitate the development and benchmarking of advanced resource management solutions, thereby improving the overall satellite communication systems. Furthermore, an application example focused on beam placement optimization via terminal clustering is provided. This assists in optimizing beam allocation task, enabling adaptive beamforming to effectively meet spatiotemporally varying user traffic demands and optimize resource utilization.

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From 5G to 6G: Revolutionizing Satellite Networks through TRANTOR Foundation

5G technology will drastically change the way satellite internet providers deliver services by offering higher data speeds, massive network capacity, reduced latency, improved reliability and increased availability. A standardised 5G ecosystem will enable adapting 5G to satellite needs. The EU-funded TRANTOR project will seek to develop novel and secure satellite network management solutions that allow scaling up heterogeneous satellite traffic demands and capacities in a cost-effective and highly dynamic way. Researchers also target the development of flexible 6G non-terrestrial access architectures. The focus will be on the design of a multi-orbit and multi-band antenna for satellite user equipment (UE), as well as the development of gNodeB (gNB) and UE 5G non-terrestrial network equipment to support multi-connectivity.

cs.NI

Polarization-Based Security: Safeguarding Wireless Communications at the Physical Layer

Physical layer security is a field of study that continues to gain importance over time. It encompasses a range of algorithms applicable to various aspects of communication systems. While research in the physical layer has predominantly focused on secrecy capacity, which involves logical and digital manipulations to achieve secure communication, there is limited exploration of directly manipulating electromagnetic fields to enhance security against eavesdroppers. In this paper, we propose a novel system that utilizes the Mueller calculation to establish a theoretical framework for manipulating electromagnetic fields in the context of physical layer security. We develop fundamental expressions and introduce new metrics to analyze the system's performance analytically. Additionally, we present three techniques that leverage polarization to enhance physical layer security.

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Smart Resource Allocation: Beyond the Optimum

Dealing with broadcast scenarios has become a relevant topic in the scientific community. Because of interference, resource management presents a challenge, specially when spatial diversity is introduced. Many researches presented theoretical benchmarks, simplifications and low complex schemes, but in fact it is difficult the real implementation. The major part of current works propose iterative solutions, which are far away of feasible results. Since the problem is not convex, iterative solutions are prohibitive. Moreover, they always require the full knowledge of the channel state information. Hence, the feedback channel is often unaffordable and makes impossible to carry a huge amount of information. The present work aims to fill this gap presenting a novel scheme, from the theoretical framework to realistic scheme. It introduces the solution of the maximization of the sum rate in the broadcast scenario with multiple antennas at the transmitter. This solution aims to be realistic and to distribute the complexity between the base station and user equipment. Also, due to its construction, it opens the door to be compatible with LTE standards with no relevant changes. Thus, it allows to combine the resource allocation with scheduling tasks in a LTE environment, fulfilling the feedback requirements and maximizing the sum rate of the system.

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Decoding 5G-NR Communications via Deep Learning

Upcoming modern communications are based on 5G specifications and aim at providing solutions for novel vertical industries. One of the major changes of the physical layer is the use of Low-Density Parity-Check (LDPC) code for channel coding. Although LDPC codes introduce additional computational complexity compared with the previous generation, where Turbocodes where used, LDPC codes provide a reasonable trade-off in terms of complexity-Bit Error Rate (BER). In parallel to this, Deep Learning algorithms are experiencing a new revolution, specially to image and video processing. In this context, there are some approaches that can be exploited in radio communications. In this paper we propose to use Autoencoding Neural Networks (ANN) jointly with a Deep Neural Network (DNN) to construct Autoencoding Deep Neural Networks (ADNN) for demapping and decoding. The results will unveil that, for a particular BER target, $3$ dB less of Signal to Noise Ratio (SNR) is required, in Additive White Gaussian Noise (AWGN) channels.

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On the Use of AI for Satellite Communications

This document presents an initial approach to the investigation and development of artificial intelligence (AI) mechanisms in satellite communication (SatCom) systems. We first introduce the nowadays SatCom operations which are strongly dependent on the human intervention. Along with those use cases, we present an initial way of automatizing some of those tasks and we show the key AI tools capable of dealing with those challenges. Finally, the long term AI developments in the SatCom sector is discussed.

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Llums que no són només llums

Visible Light Communications (VLC) is a new paradigm in wireless communications. The characteristics of this technology, which uses light-emitting diode-based lighting devices as transmitting elements, make it possible to be considered a complement to current wireless radio communication systems. ----- Les comunicacions per llum visible o 'Visible Light Communications' (VLC) són un nou paradigma en comunicacions sense fils. Les característiques que presenta aquesta tecnologia, que utilitza els dispositius d'il{\lgem{}}luminació basats en díodes emissors de llum com elements transmissors, fa que es pugui considerar un complement dels actuals sistemes de comunicació inal`ambrics.

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3D Polarized Modulation: System Analysis and Performance

In this paper we present a novel modulation technique for dual polarization communication systems, which reduces the error rate compared with the existent schemes. This modulation places the symbols in a 3D constellation, rather than the classic approach of 2D. Adjusting the phase of these symbols depending on the information bits, we are able to increase the bit rate. Hence, the proposed scheme conveys information by selecting both polarization state and the phase of radiated electromagnetic wave. We also analyse the performance of 3D Polarized Modulation (PMod) for different constellation sizes and we obtain a curve of rate adaptation. Finally, we compare the proposed 3D PMod with other existing schemes such as single polarization Phase Shift Keying (PSK) and double polarization Vertical Bell Laboratories Layer Space-Time (V-BLAST), both carrying the same number of information bits. The results show that 3D PMod always outperforms all other schemes, except for low order modulation. Therefore, we can conclude that 3D PMod is an excellent candidate for medium and high modulation order transmissions.

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Neural Network Aided Computation of Mutual Information for Adaptation of Spatial Modulation

Index Modulations, in the form of Spatial Modulation or Polarized Modulation, are gaining traction for both satellite and terrestrial next generation communication systems. Adaptive Index Modulation based links are needed to fully exploit the transmission capacity of time-variant channels. The adaptation of code and/or modulation requires a real-time evaluation of the channel achievable rates. Some existing results in the literature present a computational complexity which scales quadratically with the number of transmit antennas and the constellation order. Moreover, the accuracy of these approximations is low and it can lead to wrong Modulation and Coding Scheme selection. In this work we apply a Multilayer Feedforward Neural Network to compute the achievable rate of a generic Index Modulation link. The case of two antennas/polarizations is analyzed throughly showing the neural network not only a one-hundred fold decrement of the Mean Square Error in the estimation of the capacity compared with existing analytical approximations, but it also reduces fifty times the computational complexity. Moreover, the extension to an arbitrary number of antennas is explained and supported with simulations. More generally, neural networks can be considered as promising candidates for the practical estimation of complex metrics in communication related settings.

eess.SP

Deep Learning For Experimental Hybrid Terrestrial and Satellite Interference Management

Interference Management is a vast topic present in many disciplines. The majority of wireless standards suffer the drawback of interference intrusion and the network efficiency drop due to that. Traditionally, interference management has been addressed by proposing signal processing techniques that minimize their effects locally. However, the fast evolution of future communications makes difficult to adapt to new era. In this paper we propose the use of Deep Learning techniques to present a compact system for interference management. In particular, we describe two subsystems capable to detect the presence of interference, even in high Signal to Interference Ratio (SIR), and interference classification in several radio standards. Finally, we present results based on real signals captured from terrestrial and satellite networks and the conclusions unveil the courageous future of AI and wireless communications.

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Channel Dependent Mutual Information in Index Modulations

Mutual Information is the metric that is used to perform link adaptation, which allows to achieve rates near capacity. The computation of adaptive transmission modes is achieved by employing the mapping between the Signal to Noise Ratio and the Mutual Information. Due to the high complexity of the computation of the Mutual Information, this process is performed off-line via Monte Carlo simulations, whose results are stored in look-up tables. However, in Index Modulations, such as Spatial Modulation or Polarized Modulation, this is not feasible since the constellation and the Mutual Information are channel dependent and it would require to compute this metric at each time instant if the channel is time varying. In this paper, we propose different approximations in order to obtain a simple closed-form expression that allows to compute the Mutual Information at each time instant and thus, making feasible the link adaptation.

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Practical Implementation of Link Adaptation with Dual Polarized Modulation

The use of dual polarization in mobile satellite systems is very promising for increasing the channel capacity. Polarized Modulation is proposed in this paper for use in practical systems, by providing simple equations for computing its capacity and featuring a link adaptation algorithm. This scheme shows remarkable gains in the spectral efficiency when compared with single polarization and other multi-antenna techniques such as V-BLAST. Polarized Modulation is a particular instance of more general Index Modulations, which are being considered for 5G networks. Thus, the proposed link adaptation algorithm could find synergies with current activities for future terrestrial networks.

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Polarization and Index Modulations: a Theoretical and Practical Perspective

Radiocommunication systems have evolved significantly in recent years in order to meet present and future demands. Historically, time, frequency and more recently, spatial dimensions have been used to improve capacity and robustness. Paradoxically, radiocommunications that leverage the polarization dimension have not evolved at the same pace. In particular, these communications are widely used by satellites, where several streams are multiplexed in each orthogonal polarization. Current communication trends advocate for simplifying and unifying different frameworks in order to increase flexibility and address future needs. Due to this, systems that do not require channel information are progressively gaining traction. This dissertation aims at challenging this perspective and promoting the use of polarization in new radiocommunication systems. Consequently, the goal of this thesis is twofold: first, we aim at increasing the current capacity of point-to-point and point-to-multipoint links. Secondly, we introduce new mechanisms to increase the robustness of communications in particularly hostile environments. In this context, this thesis advocates for the use of polarization as a dimension to be exploited in radiocommunications. In addition to the use of polarization, index modulations help increase transmission rates whilst improving robustness against errors and imperfections with a low computational complexity. Thus, the study of polarization in these systems is essential. This dissertation explores primordial aspects in this area, such as channel capacity, transmitter and receiver design and performance benchmarking with current systems. Finally, we identify and discuss various characteristic aspects of polarization. In this thesis, the reader will navigate the mathematical foundations of the proposed concepts as well as their implementation in real-life scenarios.

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A Low Complexity Space-Frequency Multiuser Scheduling Algorithm

This work presents a resource allocation algorithm in K-user, M-subcarrier and NT-antenna systems for on-line scheduling. To exploit temporal diversity and to reduce complexity, the ergodic sum rate is maximized instead of the instantaneous one. Dual optimization is applied to further diminish complexity together with a stochastic approximation, which is more suitable for online algorithms. Weighted sum rate is considered so that users can be either prioritized by higher layers or differentiated by proportional rate constraints. The performance and complexity of this algorithm is compared with well-known benchmarks and also evaluated under real system conditions for the MIMO Broadcast channel.

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Link Adaptation Algorithms for Dual Polarization Mobile Satellite Systems

The use of dual polarization in mobile satellite systems is very promising as a means for increasing the transmission capacity. In this paper we study a system which uses simultaneously two orthogonal polarizations in order to communicate with the users. The application of MIMO signal processing techniques along with Adaptive Coding and Modulation in the forward link can provide remarkable throughput gains up to 100 % when compared with the single polarization system. The gateway is allowed to vary the MIMO and Modulation and Coding Schemes for each frame. The selection is done by means of a link adaptation algorithm which uses a tunable margin to achieve a prede ned target Frame Error Rate.

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Evolution of Spatial and Multicarrier Scheduling: Towards Multi-cell Scenario

OFDMA systems are considered as the promising multiple access scheme of next generation multi-cellular wireless systems. In order to ensure the optimum usage of radio resources, OFDMA radio resource management algorithms have to maximize the allocated power and rate of the different subchannels to the users taking also into account the generated co-channel interference between neighboring cells, which affects the received Quality of Service. This paper discusses various schemes for power distribution schemes in multiple co-channel cells. These schemes include centralized and distributed solutions, which may involve various degrees of complexity and related overhead and may employ procedures such as linear programming. Finally, the paper introduces a new solution that uses a network flow model to solve the maximization of the multi-cell system sum rate. The application of spatial beamforming at each cell is suggested in order to better cope with interference.

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