Raúl Parada

Raúl Parada, Ebrahim Abu-Helalah, Jordi Serra et al.

The increasing complexity and dynamic nature of 5G open radio access networks (O-RAN) pose significant challenges to maintaining low latency, high throughput, and resource efficiency. While existing methods leverage machine learning for latency prediction and resource management, they often lack real-world scalability and hardware validation. This paper addresses these limitations by presenting an artificial intelligence-driven latency forecasting system integrated into a functional O-RAN prototype. The system uses a bidirectional long short-term memory model to predict latency in real time within a scalable, open-source framework built with FlexRIC. Experimental results demonstrate the model's efficacy, achieving a loss metric below 0.04, thus validating its applicability in dynamic 5G environments.

Victor Monzon Baeza, Raúl Parada, Laura Concha Salor et al.

The integration of Artificial Intelligence (AI) in military communications and networking is reshaping modern defense strategies, enhancing secure data exchange, real-time situational awareness, and autonomous decision-making. This survey explores how AI-driven technologies improve tactical communication networks, radar-based data transmission, UAV-assisted relay systems, and electronic warfare resilience. The study highlights AI applications in adaptive signal processing, multi-agent coordination for network optimization, radar-assisted target tracking, and AI-driven electronic countermeasures. Our work introduces a novel three-criteria evaluation methodology. It systematically assesses AI applications based on general system objectives, communications constraints in the military domain, and critical tactical environmental factors. We analyze key AI techniques for different types of learning applied to multi-domain network interoperability and distributed data information fusion in military operations. We also address challenges such as adversarial AI threats, the real-time adaptability of autonomous communication networks, and the limitations of current AI models under battlefield conditions. Finally, we discuss emerging trends in self-healing networks, AI-augmented decision support systems, and intelligent spectrum allocation. We provide a structured roadmap for future AI-driven defense communications and networking research.

Jordi Serra, Anton Aguilar, Ebrahim Abu-Helalah et al.

This paper deals with the multi-object detection and tracking problem, within the scope of open Radio Access Network (RAN), for collision avoidance in vehicular scenarios. To this end, a set of distributed intelligent agents collocated with cameras are considered. The fusion of detected objects is done at an edge service, considering Open RAN connectivity. Then, the edge service predicts the objects trajectories for collision avoidance. Compared to the related work a more realistic Open RAN network is implemented and multiple cameras are used.