Xavier Costa Perez

Filip Lemic, Jakob Struye, Thomas Van Onsem et al.

The advancement of Virtual Reality (VR) technology is focused on improving its immersiveness, supporting multiuser Virtual Experiences (VEs), and enabling users to move freely within their VEs while remaining confined to specialized VR setups through Redirected Walking (RDW). To meet their extreme data-rate and latency requirements, future VR systems will require supporting wireless networking infrastructures operating in millimeter Wave (mmWave) frequencies that leverage highly directional communication in both transmission and reception through beamforming and beamsteering. We propose the use of predictive context-awareness to optimize transmitter and receiver-side beamforming and beamsteering. By predicting users' short-term lateral movements in multiuser VR setups with Redirected Walking (RDW), transmitter-side beamforming and beamsteering can be optimized through Line-of-Sight (LoS) "tracking" in the users' directions. At the same time, predictions of short-term orientational movements can be utilized for receiver-side beamforming for coverage flexibility enhancements. We target two open problems in predicting these two context information instances: i) predicting lateral movements in multiuser VR settings with RDW, and ii) generating synthetic head rotation datasets for training orientational movements predictors. Our experimental results demonstrate that Long Short-Term Memory (LSTM) networks feature promising accuracy in predicting lateral movements, and context-awareness stemming from VEs further enhances this accuracy. Additionally, we show that a TimeGAN-based approach for orientational data generation can create synthetic samples that closely match experimentally obtained ones.

Ethan Sanchez Hidalgo, Diego Zafra Bono, Guillermo Encinas Lago et al.

Efficient entanglement distribution is the foundational challenge in realizing large-scale Quantum Networks. However, state-of-the-art solutions are frequently limited by restrictive operational assumptions, prohibitive computational complexities, and performance metrics that misalign with practical application needs. To overcome these barriers, this paper addresses the entanglement distribution problem by introducing four pivotal advances. First, recognizing that the primary application of quantum communication is the transmission of private information, we derive the Ensemble Capacity (EC), a novel metric that explicitly quantifies the secure classical information enabled by the entanglement distribution. Second, we propose a generalized mathematical formulation that removes legacy structural restrictions in the solution space. Our formulation supports an unconstrained, arbitrary sequencing of entanglement swapping and purification. Third, to efficiently navigate the resulting combinatorial optimization space, we introduce a novel Dynamic Programming (DP)-based hypergraph generation algorithm. Unlike prior methods, our approach avoids artificial fidelity quantization, preserving exact, continuous fidelities while proactively pruning sub-optimal trajectories. Finally, we encapsulate these algorithmic solutions into CODE, a system-level, two-tiered orchestration framework designed to enable near-real-time network responsiveness. Extensive evaluations confirm that our DP-driven architecture yields superior private classical information capacity and significant reductions in computational complexity, successfully meeting the strict sub-second latency thresholds required for dynamic QN operation.

Filip Lemic, Gerard Calvo Bartra, Arnau Brosa López et al.

Nanodevices with Terahertz (THz)-based wireless communication capabilities are providing a primer for flow-guided localization within the human bloodstreams. Such localization is allowing for assigning the locations of sensed events with the events themselves, providing benefits along the lines of early and precise diagnostics, and reduced costs and invasiveness. Flow-guided localization is still in a rudimentary phase, with only a handful of works targeting the problem. Nonetheless, the performance assessments of the proposed solutions are already carried out in a non-standardized way, usually along a single performance metric, and ignoring various aspects that are relevant at such a scale (e.g., nanodevices' limited energy) and for such a challenging environment (e.g., extreme attenuation of in-body THz propagation). As such, these assessments feature low levels of realism and cannot be compared in an objective way. Toward addressing this issue, we account for the environmental and scale-related peculiarities of the scenario and assess the performance of two state-of-the-art flow-guided localization approaches along a set of heterogeneous performance metrics such as the accuracy and reliability of localization.