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Javier Gozalvez

Publications and source records attributed to Javier Gozalvez.

At least 19 recordsLinked to original sources
arXiv2026

Stochastic End-to-End Latency Modeling of the IoT-Edge-Cloud Continuum: Impact of Jitter and Traffic Variability on Deterministic Service Provisioning

6G will integrate communication and computing capabilities in a IoT-edge-cloud continuum, enabling nodes to distribute workloads across the continuum. To support time-sensitive services, both communications and computing latencies must be controlled. Two key sources of temporal variability are arrival-time jitter and traffic variability. They can both impact the timing at which data is generated, transmitted and processed, and the resulting fluctuations can propagate throughout the continuum, increasing latency uncertainty. This paper studies the impact of stochastic temporal variability on the ability to support end-to-end deterministic service levels across the continuum. To this end, we present a novel queueing-based end-to-end latency model for the continuum, which we openly release. The model jointly captures computing and communication latency, and characterizes the complete end-to-end latency distribution, including tail latency. Our analysis shows that services with stringent latency deadlines and larger computing demands are more sensitive to temporal variabilities, making local execution the preferred option. In contrast, services with more relaxed deadlines are more resilient to temporal variabilities when executed locally or at the edge despite higher average and tail latencies. Edge offloading is beneficial under good cellular connectivity and increasing local processing workloads, whereas cloud execution is more sensitive to traffic variabilities because of the additional communication latency. Our analysis also shows that services offloaded are more sensitive to traffic variability than jitter due to higher communication latencies. These findings highlight that effective service offloading must jointly consider service requirements and sources of temporal variability to guarantee deterministic service levels.

cs.NI↗
arXiv2026

Execution Timing Control for Deterministic Task Offloading in the IoT-Edge-Cloud Continuum

Latency-critical IoT applications, such as autonomous mobility and industrial automation, require deterministic guarantees to ensure that tasks are completed within strict deadlines. The 6G-enabled IoT-edge-cloud continuum can support such requirements by leveraging communication, computation and intelligence resources across devices, edge, and cloud infrastructures. However, existing task offloading strategies mainly focus on selecting where tasks are executed and typically assume immediate processing upon task arrival. This leads to transient congestion when multiple tasks coincide in time and results in inefficient resource utilization under dynamic workloads. This paper addresses these limitations by introducing an execution timing control strategy for deterministic task offloading that jointly determines where tasks are executed and when their execution starts, while guaranteeing deadline compliance. The key idea is to exploit the latency budget of tasks to control their execution timing, enabling a more balanced distribution of workload over time and reducing peak congestion across the continuum. Evaluation results show that, compared to existing benchmarks, the proposed approach achieves up to 70% higher satisfaction ratio, reduces the communication resources usage by 40%, lowers peak computing resource utilization by 15%, and decreases average execution time by up to 77%.

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arXiv2026

How the Fusion of Onboard Sensors and V2X Data can Improve (or not) the Cooperative Perception of Connected Automated Vehicles

Automated vehicles rely on onboard sensors to perceive their surroundings and navigate autonomously. However, sensor performance may degrade under adverse weather conditions or when line-of-sight is obstructed. Cooperative perception (or collective perception) is expected to mitigate these limitations by enabling Connected and Automated Vehicles (CAVs) to share sensor data and collaboratively enhance situational awareness. Several studies have analyzed the potential of cooperative perception, yet the fusion of V2X data with information from onboard sensors has received limited focus. V2X data may contain errors that affect the quality of the fused data, and hence the effectiveness of cooperative perception. This study analyzes the impact of sensing measurement errors, V2X packet losses, and GNSS inaccuracies on the effectiveness of cooperative perception. The results highlight the potential of cooperative perception to enhance perception levels and range compared to using onboard sensors alone. However, they also identify key challenges related to the generation of ghost vehicles during the fusion process, which must be addressed to prevent V2X data from introducing additional errors when fused with onboard sensor data.

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arXiv2026

Fusion or Confusion? Potential and Challenges in Fusion of Onboard Sensors and V2X Data in Cooperative Perception

Connected Automated Vehicles (CAVs) utilize their onboard sensors to perceive the environment. The perception range and accuracy can be affected by adverse weather or non-line-of-sight conditions. Cooperative perception or sensor sharing can overcome these limitations by enabling CAVs to exchange sensor data, thus collectively enhancing their perception capabilities. Previous studies have shown the potential of cooperative perception, but limited attention has been given to the fusion of V2X data received through cooperative perception messages with onboard sensor information. The fusion process can be influenced by the quantity and quality of the V2X data. An increased volume of V2X data can reduce uncertainty in the perceived environment; however, when the data is noisy, it may compromise the accuracy of the fusion results. This study investigates the fusion of onboard sensor and V2X data in cooperative perception, and demonstrates that while perception can significantly improve as the V2X penetration rate increases, it can introduce a significant number of false positives if V2X data is not highly accurate. False positives result in the detection of ghost objects that do not actually exist. These ghost objects can, in turn, compromise safety and driving efficiency. Our analysis found that false positives or ghost objects can appear even with accurate V2X data. These findings highlight the challenges in cooperative perception and the importance of developing robust data fusion methods to enhance the reliability of cooperative perception. This is particularly relevant in light of ongoing standardization efforts, such as ETSI TS 103 324 on collective perception.

cs.NI↗
arXiv2026

Latency-Sensitive 5G RAN Slicing for Industry 4.0

Network slicing is a novel 5G paradigm that exploits the virtualization and softwarization of networks to create different logical network instances over a common network infrastructure. Each instance is tailored for specific Quality of Service (QoS) profiles so that network slicing can simultaneously support several services with diverse requirements. Network slicing can be applied at the Core Network or at the Radio Access Network (RAN). RAN slicing is particularly relevant to support latency-sensitive or timecritical applications since the RAN accounts for a significant part of the end-to-end transmission latency. In this context, this study proposes a novel latency-sensitive 5G RAN slicing solution. The proposal includes schemes to design slices and partition (or allocate) radio resources among slices. These schemes are designed with the objective to satisfy both the rate and latency demands of diverse applications. In particular, this study considers applications with deterministic aperiodic, deterministic periodic and nondeterministic traffic. The latency-sensitive 5G RAN slicing proposal is evaluated in Industry 4.0 scenarios where stringent and/or deterministic latency requirements are common. However, it can be evolved to support other verticals with latency-sensitive or time-critical applications.

cs.NI↗
arXiv2026

Common Radio Resource Management Policy for Multimedia Traffic in Beyond 3G Heterogeneous Wireless Systems

Beyond 3G wireless systems will be composed of a variety of Radio Access Technologies (RATs) with different, but also complementary, performance and technical characteristics. To exploit such diversity while guaranteeing the interoperability and efficient management of the different RATs, common radio resource management (CRRM) techniques need to be defined. This work proposes and evaluates a CRRM policy that simultaneously assigns to each user an adequate combination of RAT and number of radio resources within such RAT to guarantee its QoS requirements. The proposed CRRM technique is based on linear objective functions and programming tools.

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arXiv2026

5G Configured Grant Scheduling for 5G-TSN Integration for the Support of Industry 4.0

Factories are evolving towards digitalized data-based ecosystems under the paradigm of the Industry 4.0 where new industrial services allow the implementation of more robust, resilient and customized manufacturing systems. Such services (e.g., digital twins, extended reality or cooperative robots) will require highly reliable and deterministic communication networks capable of supporting stringent latency and reliability requirements. 5G networks and their future evolution have the necessary capabilities to meet these requirements. However, the use of 5G in industrial environments requires its effective and efficient integration with Time Sensitive Networking (TSN), which is becoming the standard wired technology for Industry 4.0 environments. TSN provides unprecedented deterministic service levels with perfectly bounded latencies. The integration of the industrial 5G and TSN networks will be key to support the flexibility and determinism demanded by the Industry 4.0 paradigm. A critical aspect to achieve this integration is the coordination of the schedulers of both networks. TSN has information about the capabilities of the 5G-TSN integrated network, and it is in charge of deciding the path and scheduling for each TSN traffic flow. The scheduling in 5G must be done according to the scheduling decisions and information provided by TSN to guarantee the end-to-end latency requirements of TSN traffic. In this context, this paper proposes a novel Configured Grant scheduling scheme for 5G integrated into a TSN network that aims to meet the latency requirements of the different TSN flows. The proposed scheme exploits the information provided by TSN about the characteristics of the TSN traffic to coordinate its decision with the scheduling of TSN. This study demonstrates that the proposed scheduling scheme considerably increases the number of TSN flows that can be satisfactorily served.

cs.NI↗
arXiv2026

Latency-Sensitive 5G RAN Slicing for Deterministic Aperiodic Traffic in Smart Manufacturing

5G and beyond networks will support the digitalization of smart manufacturing thanks to their capacity to simultaneously serve different types of traffic with distinct QoS requirements. This can be achieved using Network Slicing that creates different logical network partitions (or slices) over a common infrastructure, and each can be tailored to support a particular type of traffic. The configuration of the Radio Access Network (RAN) slices strongly impacts the capacity of 5G and beyond to support critical services with stringent QoS requirements, and in particular deterministic requirements. Existing RAN Slicing solutions only consider the transmission rate (or bandwidth) requirements of the different services to partition the radio resources. This study demonstrates that this approach is not suitable to guarantee the stringent latency requirements of deterministic aperiodic traffic that is characteristic of industrial critical applications. We then propose designing RAN slices using descriptors that consider both the services' transmission rate and latency requirements, and demonstrate that this approach can support critical services that generate deterministic aperiodic traffic.

cs.NI↗
arXiv2026

Configured Grant Scheduling for the Support of TSN Traffic in 5G and Beyond Industrial Networks

5G and beyond networks can facilitate the digital transformation of manufacturing and support more flexible and reconfigurable factories with ubiquitous mobile connectivity. This requires integrating 5G networks with industrial networks that increasingly rely on TSN (Time Sensitive Networking) to support deterministic communications with bounded latencies. Deterministic communications are critical for many industrial applications, but 5G does not natively support deterministic communications. To address this limitation, this study proposes the coordination of the 5G and TSN schedulers and presents a novel 5G configured grant scheduling scheme to support TSN traffic. The scheme uses information about the characteristics of the TSN traffic (packet size, periodicity, and arrival time) to coordinate its scheduling decisions with the TSN scheduler. The study demonstrates that the proposed scheme outperforms the state-of-the-art in the capacity to support multiple TSN traffic flows with different periodicities.

cs.NI↗
arXiv2026

Support of Teleoperated Driving with 5G Networks

Teleoperated driving (ToD) can support autonomous driving under complex or unexpected traffic scenarios that an autonomous vehicle may not understand or be able to handle. In ToD, autonomous vehicles transmit video feeds and perception data to the remote control center. The operator uses this data to understand the driving environment and remotely control the vehicle that can take over the control once the scenario is resolved. ToD requires reliable and low latency communications between the vehicle and the ToD control center. This study analyzes the feasibility to support ToD with 5G networks. The study demonstrates that the feasibility strongly depends on the bandwidth and the Time Division Duplexing (TDD) frame structure that conditions how the bandwidth is distributed between uplink and downlink transmissions. The study also shows that scaling the number of 5G-supported ToD vehicles requires the vehicles to reduce the video bitrates. The study also shows that traditional centralized 5G network deployments may be challenged by some of the most stringent ToD latency requirements due to the latency introduced by the Internet connection to the ToD control center.

cs.NI↗
arXiv2026

FORESEE: A Cooperative Lane Change Model for Connected and Automated Driving

This paper presents FORESEE, a novel cooperative lane change model for connected and automated driving. FORESEE leverages Vehicle-to-Everything (V2X) data to anticipate traffic conditions and effectively organize lane changes. Specifically, it uses V2X data to organize vehicles into lanes based on their desired speeds, which helps to homogenize traffic flow and reduce disturbances caused by speed differences among vehicles within the same lane. The study demonstrates that implementing cooperative lane changes with FORESEE enhances average vehicle speed and energy efficiency compared to non-cooperative lane changes, which typically rely on short-term and local information about the ego vehicle and its immediate neighbors. This is achieved through fewer but more effective lane changes. Additionally, vehicles can maintain speeds closer to their desired speeds, resulting in fewer fluctuations in speed and acceleration and enhanced driving comfort. Moreover, cooperative lane changes can better manage road traffic disturbances, such as obstacles, by anticipating traffic conditions and organizing lane changes ahead. FORESEE serves as a valuable framework for the future design and testing of V2X-based maneuver coordinations as their effectiveness depends on how vehicles change lanes and their ability to plan and organize maneuvers in consideration of the upcoming traffic conditions.

cs.NI↗
arXiv2026

Importance of Intent-Sharing for V2X-based Maneuver Coordination

This paper examines the critical role of intent-sharing in enabling effective maneuver coordination for connected and automated vehicles (CAVs). Successful maneuver coordinations require vehicles to accurately know other vehicles' driving intentions. Intent-sharing can be achieved by the remote vehicles directly communicating their plans with the ego vehicle, as opposed to the ego vehicle predicting the trajectory on the remote vehicles' behalf. In this paper, we investigate the potential of intent-sharing on maneuver coordination effectiveness by quantifying the percentage of successful coordinations. We analyze the potential of intent-sharing by comparing its effectiveness for coordinated lane changes in a highway scenario with the effectiveness of a trajectory prediction method based on current kinematic data. Our analysis demonstrates in two scenarios substantial improvements in maneuver coordination when CAVs have direct access to the nearby vehicles' driving intentions through intent sharing. These findings highlight the importance of including intent-sharing in the maneuver coordination protocol.

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arXiv2026

When Cooperation Should End: Maneuver Coordination Cancellation for Connected Automated Driving

Maneuver coordination is essential for cooperative connected automated driving, enabling vehicles to negotiate maneuvers and interactions through V2X communication. While prior work has largely focused on how to initiate and execute coordinations, considerably less attention has been given to how ongoing coordinations should be terminated when they become unsuitable. This paper introduces the first complete design and implementation of maneuver coordination cancellation, including a state machine, message set, and decision-making logic. Our evaluation shows that cancellation significantly reduces the time vehicles spend in coordinations that cannot succeed, allowing them to become available for new maneuvers sooner. This increases the number of triggered coordinations and improves the number of successful maneuver coordinations. Overall, the study demonstrates that maneuver coordination cancellation improves cooperative driving, and establishes a foundation for further refinements that can enhance the efficiency and robustness of connected automated driving.

cs.NI↗
arXiv2026

Multi-Target Maneuver Coordinations: Unlocking Coordination Opportunities in Connected Automated Driving

Maneuver coordination is a key enabler of connected and automated driving, allowing vehicles to negotiate and execute maneuvers that would otherwise be difficult, inefficient or unsafe. Existing approaches and use cases typically assume coordination with a single predefined target vehicle, which limits the number of coordination opportunities. This paper introduces a maneuver coordination approach based on multi-target selection, which allows a vehicle to identify and select among multiple potential coordination vehicles for a given maneuver. Multi-target maneuver coordination does not require modifications to the maneuver execution logic or to the underlying coordination protocol. Instead, it extends the decision-making process preceding coordination, enabling vehicles to exploit a broader set of feasible cooperative interactions. Results show that multi-target maneuver coordination significantly increases triggered and successfully executed coordinations while maintaining a low computational cost, as the proposed approach achieves these gains without requiring the analysis of a large number of potential target vehicles. These improvements preserve coordination success rates while enabling earlier maneuver initiation.

cs.NI↗
arXiv2026

Mind the Noise: Sensitivity of Transformer-based Interaction-Aware Trajectory Prediction Models to Noisy Data

Trajectory prediction allows autonomous vehicles to anticipate the future behavior of surrounding objects (or agents) and, accordingly, maximize the safety and efficiency of their driving. State-of-the-art Transformed-based interaction-aware trajectory prediction models, which rely on attention mechanisms to capture multi-agent interactions and maximize prediction accuracy, are commonly trained and evaluated on long-range high-quality datasets. These datasets are typically obtained by aggregating data from multiple vehicles or drones and removing any object detection or tracking noise offline. Yet, information about a surrounding object's state (its position, speed, heading) is far from being noiseless in real-world deployments. Object state estimation is affected by perception uncertainties and localization errors that can be particularly large for objects received via Vehicle-to-Everything (V2X) communications. In this paper, we analyze the impact of noisy object state information on the trajectory prediction accuracy of a state-of-the-art Transformer-based interaction-aware trajectory prediction model. Our study demonstrates that trajectory prediction accuracy can rapidly deteriorate as the noise intensity increases. Numerical results show that the prediction accuracy can reduce by a 1.3x factor under small noise levels and by as much as a 3.9x factor under the highest (yet realistic) noise conditions. These findings reveal the strong sensitivity of trajectory prediction models to noisy data, underscoring the need for more realistic training and evaluation datasets as well as noise mitigation strategies.

cs.AI↗
arXiv2026

5G UE and Network Asset Administration Shells for the Integration of 5G and Industry 4.0 Systems

5G is a fundamental technology for the full digitalization of smart manufacturing. The use of Asset Administration Shells (AAS) can facilitate the integration of 5G with Industry 4.0 systems and applications while minimizing the complexities associated with the 5G network management. This study presents the design and implementation of the first full 5G system AAS that is openly released to the community [1]. It includes a 5G UE (User Equipment) AAS and a 5G NW (network) AAS that have been designed following the 5G-ACIA guidelines as well as the Plattform Industrie 4.0 and 3GPP standards. The AASs have been defined to provide and expose the data and capabilities of 5G necessary to facilitate the integration of 5G with Industry 4.0 systems and applications.

cs.NI↗
arXiv2026

Predictive Configured Grant Scheduling for Deterministic Wireless Communications

Future wireless networks must enhance their capacity to sustain deterministic service levels and support emerging time-sensitive services in key verticals. The ability to guarantee bounded latencies heavily depends on efficient radio resource management. Configured Grant (CG) scheduling can reduce latency by pre-allocating resources, but its effectiveness and efficiency decrease under variable traffic patterns. This study presents a novel predictive CG scheduling scheme that pre-allocates resources based on traffic predictions while accounting for prediction inaccuracies. By considering these inaccuracies, the scheme significantly improves the ability to meet bounded latency requirements, which are essential for supporting deterministic service levels. Our evaluations show that the proposed scheme significantly enhances the capacity to support deterministic service levels while improving resource utilization, even in scenarios with variable and mixed traffic flows with diverse requirements.

cs.NI↗
arXiv2026

Predictive Dynamic Scheduling for Deterministic Communications in Beyond 5G

Next generation wireless networks must sustain deterministic service levels to support emerging time-sensitive applications. The ability to guarantee bounded latencies depends on the efficient management of radio resources. Several studies propose leveraging the native intelligence of future networks to develop predictive schedulers capable of efficiently managing resources. However, existing proposals focus on semi-static scheduling, where resources are reserved based on traffic predictions, and these reservations are susceptible to inefficiencies due to prediction inaccuracies. This study advances the state of the art with a novel predictive dynamic scheduling scheme that avoids such inefficiencies, and leverages traffic predictions to allocate resources to incoming requests that meet their latency requirements while avoiding resources likely to be needed by future predicted packets. Our results demonstrate that the proposed predictive dynamic scheduling effectively supports deterministic communications in scenarios with mixed traffic flows and varying QoS requirements.

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