R&I group

Software Networks

The Software Networks (SN) research group focuses on advancing the capabilities of next-generation networks through automation, intelligence, federation and integration. With the increasing shift from centralized to distributed infrastructures, the research group addresses resource management challenges. It focuses on combining AI, automation, and integration to simplify the management of complex networks, ensuring scalability, reliability, and efficiency for next-generation applications and services.

Research lines

Intent-Based Networking (IBN): This research line focuses on the intelligent orchestration of network resources through high-level business intent abstraction. IBN complements Network Function Virtualization (NFV) by abstracting the complexities of infrastructure configuration, much like NFV decouples services from physical hardware. At its core, IBN aims to translate business-level goals into network control policies via advanced generative AI (GenAI) and machine reasoning tools, which are learned and executed by low-level agentic systems, leveraging the Emergent Communication (EC) paradigm. This enables networks to adapt dynamically, respond to changing requirements, and self-optimize with minimal human intervention.
Cognitive Cloud Continuum: This research line is focused on the investigation and prototyping of cloud-native solutions for providing extended capabilities and intelligence across the edge-to-cloud continuum to improve latency, scalability, resource utilization, and context-awareness.Specific research topics include the integration and interoperation of NFV and MEC orchestrators, AI/ML-based analytics services for workload placement and re-allocation, federation of inter-operator edge platforms, and integration with a vertical-oriented service exposure layer.

Innovation lines

Connected Collaborative Computing (3C) Networks
Resource Exposure, GSMA Operator Platform, CAMARA, Federation and Open Gateway

Technologies

Intelligent Application/Service Orchestration for Cognitive Cloud Continuum [incl. extension to NTN]
Telco capabilities exposure (CAMARA, GSMA Open Gateway, Operator Platform) and network federation
Intent-based Networking
Intelligent slice management
Distributed and secure Marketplace for 5G/6G resources
Agentic Networking and Multi-agent communications protocols
Testbed & telco -focused virtualisation stacks

Group leader

Pouria Sayyad Khodashenas

Areas

Smart Networks & Services 5G/6G Space Communications

Software Networks

Projects

Software Networks

Publications

NetXRate: Network-Assisted Rate Recommendation for XR Service Optimization

A. Lopez Garcia, A. A. AbdelNabi, M. Catalan-Cid, M. Montagud, C. Sarathchandra, and D. Camps-Mur, “NetXRate: Network-assisted rate recommendation for XR service optimization,” IEEE Transactions on Broadcasting, 2026.

Dynamic Joint Scheduling of Movement and Data Processing Tasks using Extreme-Edge Computing in Multi-AGV Scenarios

M. Masoumi, E. Carmona-Cejudo, I. de Miguel, C. Torres-Pérez, R.J. Durán Barroso, “Dynamic Joint Scheduling of Movement and Data Processing Tasks using Extreme-Edge Computing in Multi-AGV Scenarios,” in IEEE Open Journal of the Industrial Electronics Society, in press.

Service Placement in dynamic multi-AGV environments for minimized energy consumption

C. Torres-Pérez, E. Carmona-Cejudo, C. Cervelló-Pastor, M. Masoumi, E. Coronado and M. S. Siddiqui, “Service Placement in Dynamic Multi-AGV Environments for Minimized Energy Consumption,” in IEEE Robotics and Automation Letters, vol. 11, no. 1, pp. 714-721, Jan. 2026, doi: 10.1109/LRA.2025.3636026

Journal of Network and Systems Management: Hardware-accelerated Edge AI Orchestration on the Multi-Tier Edge-Cloud Continuum

@article{palomares2025hardware, author = {Palomares, Javier and Coronado, Estefanía and Tzenetopoulos, Achilleas and Lentaris, George and Cervelló-Pastor, Cristina and Siddiqui, Muhammad Shuaib}, title = {Hardware-Accelerated Edge AI Orchestration on the Multi-Tier Edge-to-Cloud Continuum}, journal = {Journal of Network and Systems Management}, year = {2025}, doi = {10.1007/s10922-025-09959-4}, publisher = {Springer Nature} }

A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks

H. Chergui, F. Rezazadeh, M. Debbah, and C. Verikoukis, “A tutorial on cognitive biases in agentic AI-driven 6G autonomous networks,” IEEE Open Journal of the Communications Society (Accepted)

Paper OTT-MNO collaboration for a Network-layer ML-based QoE Prediction for Video Streaming over 5G O-RAN  

González, C. C., Pupo, E. F., Floris, A., Porcu, S., Murroni, M., & Atzori, L. (2026). OTT-MNO collaboration for a Network-layer ML-based QoE Prediction for Video Streaming over 5G O-RAN. Computer Networks, 112152.

View all

Software Networks