Publications and Patents
Book Section
- Contribution to L. Correia, L. Ferreira, “Virtualized and Cloud-based Architectures”, in N. Cardona (editor), Cooperative Radio Communications for Green Smart Environments
Journal Articles
IEEE Wireless Communications, vol. 26, no. 5, pp. 134-141, October 2019 [Link].
Co-authored work proposing an AI-driven framework to elastically manage and orchestrate 5G network resources. Demonstrates how ML can dynamically adapt network slices and infrastructure to meet heterogeneous service requirements.
Wireless Communications and Mobile Computing, 2019 [Link].
Defines a programmable, flexible 5G architecture based on network slicing, service-based core, and softwarization—addressing key architectural gaps for diverse 5G services.
EURASIP Journal on Wireless Communications and Networking, vol. 2017, p. 128, July 2017 [Link].
Presents an analytical model and proof-of-concept implementation of virtual radio resource management for Cloud RAN, using OpenAirInterface as a virtualized LTE platform.
EURASIP Journal on Wireless Communications and Networking, Vol. 2017, No. 1, 2017, p. 73 [Link].
Introduces a model for virtual radio resource management across heterogeneous access networks, enabling capacity estimation and resource allocation in multi-RAT environments.
Earlier foundational work on virtual radio resource abstraction and optimization for virtual RAN deployments.
Selected International Conference Paper
Elastic Slice-Aware Radio Resource Management with AI-Traffic Prediction
IEEE EuCNC 2019.
Proposes a slice-aware radio resource management scheme that uses AI-based traffic prediction to dynamically adjust resources across network slices.Modelling of Computational Resources for 5G RAN
IEEE EuCNC 2018.
Introduces models for computational resource demands in virtualized 5G RAN, bridging cloud-compute planning with telco performance requirements.Slice-Aware Elastic Resource Management
EuCNC / 5G-MoNArch related work, 2018
Addresses elastic resource allocation for multiple slices over a shared infrastructure, supporting dynamic, service-aware scaling.
The Path Towards Resource Elasticity for 5G Network Architecture
IEEE WCNC Workshops, 2018
Explores architectural design and mechanisms for resource elasticity in 5G networks, supporting on-demand, programmable infrastructure.
The Effect of Channel Quality on Virtual Radio Resource Management
IEEE VTC 2015
This paper examined how varying radio channel conditions influence virtual resource allocation in Cloud-RAN environments. It quantified how channel quality affects abstraction layers, scheduler behavior, and user experience when resources are virtualized.
Modelling Virtual Radio Resource Management with Traffic Offloading Support
IEEE EuCNC 2015
This work introduced a model for virtual radio resource management that incorporates traffic offloading between heterogeneous networks. The model supports dynamic capacity management across LTE, WiFi, and small-cell layers, enabling operators to distribute load efficiently and reduce congestion during peak usage.
Modelling of Virtual Radio Resource Management for Cellular Heterogeneous Access Networks
IEEE PIMRC 2015
This work developed a model for coordinating radio resources across heterogeneous access networks, introducing analytical tools to manage distributed spectrum and capacity in multi-RAT environments.
Modelling of Virtual Radio Resource Management for Cellular Heterogeneous Access Networks
IEEE PIMRC 2015
This work developed a model for coordinating radio resources across heterogeneous access networks, introducing analytical tools to manage distributed spectrum and capacity in multi-RAT environments.
White Papers
The Path to 6G with Unparalleled Energy Savings – A 3GPP Standardization Perspective
Nokia Whitepaper, Dec. 2024 [Original Link][Download PDF]
This work developed a model for coordinating radio resources across heterogeneous access networks, introducing analytical tools to manage distributed spectrum and capacity in multi-RAT environments.
Technical Documents & EU Projects Deliverables
Technical Reports & EU Project Deliverables
I have contributed as author or editor to several major EU 5G research projects, including 5G-MoNArch, 5G-NORMA, and MobileCloud Networking. Examples:
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5G-MoNArch Deliverables (D2.1, D2.3, D4.1, D6.1, etc.)
– Overall 5G mobile network architecture, requirements/KPIs, and resource elasticity mechanisms. -
5G NORMA Deliverables (D3.x, D4.x)
– Network architecture, RAN components, and multi-service adaptability. -
MobileCloud Networking Deliverables
– Virtualization, infrastructure management foundations, and radio resource virtualization.
These deliverables helped define early flexible 5G architectures, slicing concepts, and cloud-native mobile network design adopted by later 3GPP and 5G-PPP work.
Selected Patents
Below is a non-confidential selection of my patent work, focused on AI-native networks, mobility, and AI system trustworthiness.
Beam Group Specific Mobility Robustness Optimization – US 11,894,906 B2 (2024)
Introduces optimized decision logic at the beam-group level in dense radio access networks, reducing handover failures and improving robustness through intelligent mobility orchestration.
Optimization of Mobility Robustness – EP 4,454,335 A1 (2024)
Develops parameters and mechanisms for autonomous decision-making in multi-cell environments to enhance mobility reliability and user experience in 5G/6G architectures.
UE Positioning-Aware Mobility Optimization – EP 4,454,336 A1 (2024)
Proposes a context-aware system that leverages UE positioning to guide mobility and handover decisions, enabling smarter, agent-driven network behaviour.
Method and Apparatus for Feasibility Checking of AI Pipeline Trustworthiness – US 2024/0348508 A1
Defines a framework for assessing readiness and trustworthiness of AI pipelines (data, model, operations) before deployment—supporting safe implementation of GenAI and agentic systems in network/cloud environments.
Controlling Re-Training of a Machine Learning Model at a Device – US 2024/119365 A1
Presents a method for managing model retraining at edge devices based on performance and context, optimizing resource use and maintaining model quality in distributed AI deployments.
Full Patent List
To view the complete list of my public patents (granted, pending, and family status), please visit my Espacenet Profile