arXiv subjects

David Gomez-Barquero

Publications and source records attributed to David Gomez-Barquero.

6 recordsLinked to original sources

arXiv2026

Passive AoA Estimation of COTS 5G NR Handsets from Uplink SRS: A Practical USRP-B210 Implementation

This paper presents a passive network-side framework for estimating the uplink angle of arrival (AoA) of unmodified commercial 5G handsets from native Sounding Reference Signal (SRS) transmissions. The proposed system operates on an srsRAN Project gNB and estimates AoA directly from the per-antenna SRS channel estimates using a calibrated two-element Universal Software Radio Peripheral (USRP) B210 receiver and a MUSIC-based estimator, requiring neither protocol modifications nor user-equipment cooperation. Independent SRS channel estimates enable simultaneous AoA estimation for up to four commercial handsets. The framework is evaluated through indoor and outdoor measurement campaigns. Indoor measurements on bands n40 and n78 achieve a root-mean-square error of 1.5 on n40 over a 30 broadside sector. Outdoor experiments in a live 5G standalone deployment demonstrate simultaneous multi-user operation and show that estimation accuracy is primarily governed by propagation conditions and received signalto-interference-plus-noise ratio (SINR) rather than transmitter distance. These results demonstrate the feasibility of passive uplink AoA estimation using native 5G signaling, low-cost SDR hardware, and commercial handsets.

eess.SP↗

arXiv2026

Physics-Aware Conditional SetGAN for Spatially Consistent Multi-User TR 38.901 Channel Generation

TR 38.901-based channel models such as Sionna are reliable, but generating many multi-user channel realizations remains expensive. This paper asks a practical question: can a trained generative model produce multi-user TR 38.901 channels faster than Sionna without losing the spatial correlations imposed by user geometry? To answer this question, we propose a physics-aware, geometry-conditioned SetGAN trained on Sionna reference data. The method separates large-scale received power from normalized small-scale fading, compresses the latter with principal component analysis, and learns the conditional channel distribution in a latent space while preserving geometry-dependent correlations. On the UMa/NLoS benchmark, the model keeps the received-power distributions close to the reference, with about 0.41 dB Wasserstein distance, and reproduces spatial-consistency profiles with mean deviations below 0.03 on median curves versus distance. In addition, it reduces elapsed generation time by a factor of 3.45 and CPU-total cost by a factor of 6.15 relative to Sionna under matched user positions in the fixed-position CPU-vs-CPU benchmark. These results show that a trained generative model can substantially accelerate TR 38.901 channel generation without breaking the spatial consistency needed to evaluate multi-user systems.

cs.LG↗

arXiv2025

AIORA: An AI-Native Multi-Stakeholder Orchestration Architecture for 6G Continuum

This paper elaborates on a novel AI-native architecture for emerging 6G systems harnessing open APIs, along with supporting mechanisms to empower intelligent and coordinated orchestration of edge-cloud continuum resources. The AIORA architecture facilitates a seamless creation, life-cycle management, and exposure of services in multi-segment heterogeneous environments. It integrates new breeds of tools and advanced technologies to enable zero-touch management of an edge-cloud continuum, building on top of the 3GPP Edge Enablement Layer and the respective connectivity models, allowing to cater to the high flexibility, availability, efficiency, reliability, and resilience needs of the future 6G services and applications. Several ongoing industry initiatives -- such as ETSI MEC for edge computing platforms, the GSMA Operator Platform for multi-operator service federation, and CAMARA for cross-operator API standardization -- demonstrate the growing momentum towards integrated frameworks where edge, cloud, and network resources can be seamlessly orchestrated. Our proposed AIORA architecture not only aligns with these initiatives but also extends them by leveraging a multi-segment virtual continuum concept and nested AI-driven closed loops for real-time optimization.

cs.NI↗

arXiv2023

Choose, not Hoard: Information-to-Model Matching for Artificial Intelligence in O-RAN

Open Radio Access Network (O-RAN) is an emerging paradigm, whereby virtualized network infrastructure elements from different vendors communicate via open, standardized interfaces. A key element therein is the RAN Intelligent Controller (RIC), an Artificial Intelligence (AI)-based controller. Traditionally, all data available in the network has been used to train a single AI model to be used at the RIC. This paper introduces, discusses, and evaluates the creation of multiple AI model instances at different RICs, leveraging information from some (or all) locations for their training. This brings about a flexible relationship between gNBs, the AI models used to control them, and the data such models are trained with. Experiments with real-world traces show how using multiple AI model instances that choose training data from specific locations improve the performance of traditional approaches following the hoarding strategy.

cs.NI↗

arXiv2020

5G Radio Access Network Architecture for Terrestrial Broadcast Services

The 3rd Generation Partnership Project (3GPP) has defined based on the Long Term Evolution (LTE) enhanced Multicast Broadcast Multimedia Service (eMBMS) a set of new features to support the distribution of Terrestrial Broadcast services in Release 14. On the other hand, a new 5th Generation (5G) system architecture and radio access technology, 5G New Radio (NR), are being standardised from Release 15 onwards, which so far have only focused on unicast connectivity. This may change in Release 17 given a new Work Item set to specify basic Radio Access Network (RAN) functionalities for the provision of multicast/broadcast communications for NR. This work initially excludes some of the functionalities originally supported for Terrestrial Broadcast services under LTE e.g. free to air, receive-only mode, large-area single frequency networks, etc. This paper proposes an enhanced Next Generation RAN architecture based on 3GPP Release 15 with a series of architectural and functional enhancements, to support an efficient, flexible and dynamic selection between unicast and multicast/broadcast transmission modes and also the delivery of Terrestrial Broadcast services. The paper elaborates on the Cloud-RAN based architecture and proposes new concepts such as the RAN Broadcast/Multicast Areas that allows a more flexible deployment in comparison to eMBMS. High-level assessment methodologies including complexity analysis and inspection are used to evaluate the feasibility of the proposed architecture design and compare it with the 3GPP architectural requirements.

cs.NI↗

arXiv2020

Demonstrating Immersive Media Delivery on 5G Broadcast and Multicast Testing Networks

This work presents eight demonstrators and one showcase developed within the 5G-Xcast project. They experimentally demonstrate and validate key technical enablers for the future of media delivery, associated with multicast and broadcast communication capabilities in 5th Generation (5G). In 5G-Xcast, three existing testbeds: IRT in Munich (Germany), 5GIC in Surrey (UK), and TUAS in Turku (Finland), have been developed into 5G broadcast and multicast testing networks, which enables us to demonstrate our vision of a converged 5G infrastructure with fixed and mobile accesses and terrestrial broadcast, delivering immersive audio-visual media content. Built upon the improved testing networks, the demonstrators and showcase developed in 5G-Xcast show the impact of the technology developed in the project. Our demonstrations predominantly cover use cases belonging to two verticals: Media & Entertainment and Public Warning, which are future 5G scenarios relevant to multicast and broadcast delivery. In this paper, we present the development of these demonstrators, the showcase, and the testbeds. We also provide key findings from the experiments and demonstrations, which not only validate the technical solutions developed in the project, but also illustrate the potential technical impact of these solutions for broadcasters, content providers, operators, and other industries interested in the future immersive media delivery.

cs.NI↗