https://hdl.handle.net/20.500.12761/2 2026-04-20T09:08:03Z 2026-04-20T09:08:03Z Alcalá-Marín, Sergi Bega, Dario Gramaglia, Marco Banchs, Albert Costa-Perez, Xavier Fiore, Marco https://hdl.handle.net/20.500.12761/2027 2026-04-18T00:00:15Z 2025-10-01T00:00:00Z

AZTEC+: Long- and Short-Term Resource Provisioning for Zero-Touch Network Management Alcalá-Marín, Sergi; Bega, Dario; Gramaglia, Marco; Banchs, Albert; Costa-Perez, Xavier; Fiore, Marco In the past few years, network infrastructures have transitioned from prominently hardware-based models to networks of functions, where software components provide the required functionalities with unprecedented scalability and flexibility. However, this new vision entails a completely new set of problems related to resource provisioning and the network function operation, making it difficult to manage the network function lifecycle management with traditional, human-in-the-loop approaches. Novel zero-touch management solutions promise autonomous network operation with limited human interactions. However, modeling network function behavior into compelling variables and algorithm is an aspect that such solutions must take into account. In this paper, we propose AZTEC+, a data-driven solution for anticipatory resource provisioning in network slicing scenarios. By leveraging a hybrid and modular deep learning architecture, AZTEC+ not only forecasts the future demands for target services but also identifies the best trade-offs to balance the costs due to the instantiation and reconfiguration of such resources. Our experimental evaluation, based on real-world network data, shows how AZTEC+ can outperform state-of-the-art management solutions for a large set of metrics.

2025-10-01T00:00:00Z Alcalá-Marín, Sergi Wu, Weili Raman, Aravindh Bagnulo, Marcelo Alay, Ozgu Bustamante, Fabián Fiore, Marco Lutu, Andra https://hdl.handle.net/20.500.12761/2026 2026-04-18T00:00:13Z 2025-10-01T00:00:00Z

A Comparative Analysis of Global Mobile Network Aggregators Alcalá-Marín, Sergi; Wu, Weili; Raman, Aravindh; Bagnulo, Marcelo; Alay, Ozgu; Bustamante, Fabián; Fiore, Marco; Lutu, Andra The mobile telecommunication industry is undergoing continuous evolution to cope with ever increasing service requirements and expectations of end users. This has recently led to the rise of Mobile Network Aggregators (MNAs), a new type of global virtual operators that deliver mobile communication services by utilizing multiple Mobile Network Operators (MNOs), dynamically connecting to the one that best meets their customers’ needs based on location and time. MNAs can then offer optimized global coverage by connecting to local MNOs that have limited (e.g., national) geographic service. In this paper, we provide a first in-depth analysis of the operations of three major MNAs: Google Fi, Twilio, and Truphone. We conduct performance measurements across these MNAs for critical applications spanning DNS, web browsing, and video streaming, and compare their performance against that of a traditional MNO from two very diverse geographical locations, US and Spain. We find that MNAs may introduce some delay compared to local MNOs in the region where the user is roaming, yet they offer significant performance improvements over the traditional MNOs roaming model, such as home-routed roaming. To fully assess the potential benefits of the MNA model, we also carry out emulation studies assessing the potential performance gains that MNAs could achieve by deploying both control and user plane functions of open-source 5G implementations across different Amazon Web Services locations.

2025-10-01T00:00:00Z Bhalli, Zaman Rossi, Michele Widmer, Joerg Canil, Marco https://hdl.handle.net/20.500.12761/2025 2026-04-16T00:00:14Z 2026-06-01T00:00:00Z

Estimating Target Doppler in Unsynchronized Multistatic ISAC Deployments with Mobile Nodes Bhalli, Zaman; Rossi, Michele; Widmer, Joerg; Canil, Marco Integrated Sensing And Communication (ISAC) is recognized as a key enabler for future 6th Generation (6G) networks, combining communication capabilities with pervasive sensing. In such systems, the estimation of the Doppler shift plays a crucial role for target characterization. However, typical real-world ISAC scenarios largely involve bistatic or multistatic configurations and mobile ISAC nodes. Under these conditions, Doppler estimation becomes particularly challenging, as clock asynchrony between the Transmitter (TX) and the Receivers (RXs), combined with their mobility, introduces additional Doppler components and phase offsets that distortor disrupt the target-induced frequency shift. Existing works have considered these challenges separately or relied on external reference reflectors. In this paper, we present the first method to estimate the Doppler frequency of a target with mobile and asynchronous ISAC nodes in a multistatic configuration, considering the case of a mobile TX and multiple static RXs, and without leveraging any external reflector. By leveraging the invariance of the phase offsets across multipath components and exploiting geometrical relationships, we show that the problem is solvable if at least 4 RXs are present. We evaluate the proposed solution through numerical simulations in various scenarios, showing that it is a valid approach for estimating target Doppler shifts in unsynchronized multistatic ISAC deployments with mobile nodes.

2026-06-01T00:00:00Z Dogani, Javad Khastkhodaei, Mostafa Khunjush, Farshad Laoutaris, Nikolaos https://hdl.handle.net/20.500.12761/2024 2026-04-15T00:00:12Z 2026-06-01T00:00:00Z

SemanticDFL: Similarity-Aware Pull-based Personalized Decentralized Federated Learning Dogani, Javad; Khastkhodaei, Mostafa; Khunjush, Farshad; Laoutaris, Nikolaos Personalized decentralized federated learning (PDFL) seeks to tailor models to heterogeneous clients without a central coordinator, yet gossip-style mixing on large graphs dilutes minority signals and assumes any-to-any connectivity. We present SemanticDFL, a fully decentralized, pull-based personalization layer that organizes peers into a hierarchical semantic overlay network (SON). Each client publishes a compact top-P model signature; proximity-bounded discovery forms zones that are clustered using affinity propagation and stewarded by replica-backed super-peers that route bounded-fanout similarity queries. Clients then pull only the K most similar models for personalized aggregation, concentrating communication and computation where they matter most. We prove a lower bound that links spectral mixing and data heterogeneity to an irreducible mis-aggregation penalty for graph-oblivious, push-based overlays, thereby motivating the proposed similarity-aware pull method. A prototype and large-scale evaluation on FMNIST, Tiny ImageNet, Google Speech Commands, and 20 Newsgroups under Dirichlet and pathological splits (50--400 peers on the EU SLICES testbed) show that SemanticDFL improves final accuracy by 3--12% over strong decentralized personalized baselines, reaches target accuracy with 2.5x fewer rounds than FedAvg, and requires 1.3X fewer rounds than the best DPFL alternative. It adds only .7--12.6% per-round overhead across all settings while maintaining Recall@K 0.88--1.00, positioning similarity-aware pull over semantic overlays as a scalable path to high-quality personalization in decentralized FL.

2026-06-01T00:00:00Z