Bin Han, Vincenzo Sciancalepore, Yihua Xu, Di Feng, Hans D. Schotten: “Impatient Queuing for Intelligent Task Offloading in Multi-Access Edge Computing”, IEEE Transactions on Wireless Communications (TWC) 2022
Multi-access edge computing (MEC) emerges as an essential part of the upcoming Fifth Generation (5G) and future beyond-5G mobile communication systems. It adds computational power towards the edge of cellular networks, much closer to energy-constrained user devices, and there with allows the users to offload tasks to the edge computing nodes for low-latency applications with very-limited battery consumption. However, due to the high dynamics of user demand and server load, task congestion may occur at the edge nodes resulting in long queuing delay. Such delays can significantly degrade the quality of experience (QoE) of some latency-sensitive applications, raise the risk of service outage, and cannot be efficiently resolved by conventional queue management solutions.
In this article, we study a latency-outage critical scenario, where users intend to limit the risk of latency outage. We propose an impatience-based queuing strategy for such users to intelligently choose between MEC offloading and local computation, allowing them to rationally renege from the task queue. The proposed approach is demonstrated by numerical simulations to be efficient for generic service model, when a perfect queue status information is available. For the practical case where the users obtain only imperfect queue status information, we design an optimal online learning strategy to enable its application in Poisson service scenarios.
Publlished in: IEEE Transactions on Wireless Communications
Paper available at: IEEE Explore®
Research partners: Technical University of Kaiserslautern, University of Lausanne
Full paper download: Impatient_Queuing_for_Intelligent_Task_Offloading_in_Multi-Access_Edge_Computing_pre-print.pdf
Rreze Halili, F. Zarrar Yousaf, Nina Slamnik-Krijestorac, Girma M. Yilma, Marco Liebsch, Rafael Berkvens, Maarten Weyn: “Self-correcting Algorithm for Estimated Time of Arrival of Emergency Responders”, IEEE Transactions on Vehicular Technology, 2022
Edge computing is one of the key features of the 5G technology-scape that is realizing new and enhanced automotive use cases for improving road safety and emergency response management. Back Situation Awareness (BSA) is such a use case that provides advance notification to the vehicles of an arriving emergency vehicle (EmV). This paper presents an algorithm for enhancing the accuracy of the advance Estimated Time of Arrival (ETA) notification of an approaching emergency vehicle EmV towards vehicles, ensuring timely reaction by the vehicles to create a clear corridor for the EmV to pass through unhindered, thereby saving precious time to reach the emergency event in a safe manner. Features of the presented solution are: I. the algorithm self-correction approach, II. adaptive or dynamic dissemination area size allocation in reaction to traffic changes, and III. evaluation of the ETA accuracy. Based on real travel time data measurements, the performance of the algorithm has been evaluated and compared using Kalman filter, Filter-less method, Moving Average, and Exponential Moving Average filters. It is observed that the Kalman filter provides better accuracy on the ETA estimation, by reducing the estimation error by around 14% on average.
Published in: IEEE Transactions on Vehicular Technology
Research partners: University of Antwerp
Full paper download: Self-correcting_Algorithm_for_ETA_of_Emergency_Responders_Accepted_vers.pdf
Farhad Rezazadeh, Lanfranco Zanzi, Francesco Devoti, Hatim Chergui, Xavier Costa-Pérez, Christos Verikoukis: “On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration”, IEEE Transactions on Vehicular Technology 2022
Network slicing enables multiple virtual networks to be instantiated and customized to meet heterogeneous use case requirements over 5G and beyond network deployments. However, most of the solutions available today face scalability issues when considering many slices, due to centralized controllers requiring a holistic view of the resource availability and consumption over different networking domains. In order to tackle this challenge, we design a hierarchical architecture to manage network slices resources in a federated manner. Driven by the rapid evolution of deep reinforcement learning (DRL) schemes and the Open RAN (O-RAN) paradigm, we propose a set of traffic-aware local decision agents (DAs) dynamically placed in the radio access network (RAN). These federated decision entities tailor their resource allocation policy according to the long-term dynamics of the underlying traffic, defining specialized clusters that enable faster training and communication overhead reduction. Indeed, aided by a traffic-aware agent selection algorithm, our proposed Federated DRL approach provides higher resource efficiency than benchmark solutions by quickly reacting to end-user mobility pat- terns and reducing costly interactions with centralized controllers.
Accepted at: IEEE Transactions on Vehicular Technology
Research partners: Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), University of Patras
"The 6G Architecture Landscape – European Perspective", 5PPP Architecture Working Group, Dec 2022
The 5G Architecture Working Group as part of the 5G PPP Initiative is identifying capturing novel trends and key technological enablers for the realization of the 5G and 6G architecture. It also targets at presenting in a harmonized way the architectural concepts developed in various projects and initiatives (not limited to 5G PPP projects only) so as to provide a consolidated view on the technical directions for the architecture design in the 5G/6G era.
The first version of the white paper was released in July 2016, which captured novel trends and key technological enablers for the realization of the 5G architecture vision along with harmonized architectural concepts from 5G PPP Phase 1 projects and initiatives. Capitalizing on the architectural vision and framework set by the first version of the white paper, the Version 2.0 of the white paper was released in January 2018 and Version 3.0 in February 2020, presented the latest findings and analyses of 5G PPP Phase I projects along with the concept evaluations. The last version 4.0 was released in October 2021, presented the outcome of the projects from 5G PPP phase II and III.
The work has continued with the 5G PPP Phase II, III and now Phase IV. Phase IV includes the projects that are defining the architecture for 6G. The results of the Architecture Working Group are now captured in this version of the white paper, which presents the consolidated European view on the 6G architecture design.
Full paper download: 6G-Arch-Whitepaper_v1.0-final.pdf
Antonio Albanese, Vincenzo Sciancalepore, Albert Banchs, Xavier Costa-Perez: “LOKO: Localization-aware Roll-out Planning for Future Mobile Networks”, IEEE Transactions on Mobile Computing (TMC) 2022
The roll-out phase of the next generation of mobile networks (5G) has started and operators are required to devise deployment solutions while pursuing localization accuracy maximization. Enabling location-based services is expected to be a unique selling point for service providers now able to deliver critical mobile services, e.g., autonomous driving, public safety, remote operations. In this paper, we propose a novel roll-out base station placement solution that, given a Throughput-Positioning Ratio (TPR) target, selects the location of new-generation base stations (among available candidate sites) such that the throughput and localization accuracy are jointly maximized. Moving away from the canonical position error bound (PEB) analysis, we develop a realistic framework in which each positioning measurement is affected by errors depending upon the actual wireless channel between the measuring base station and the target device. Our solution, referred to as LOKO, is a fast-converging algorithm that can be readily applied to current 5G (or future) roll-out processes. LOKO is validated by means of an exhaustive simulation campaign considering real existing deployments of a major European network operator as well as synthetic scenarios.
Presented at: IEEE Transactions on Mobile Computing
Research partners: Universidad Carlos III de Madrid
Full paper download: LOKO_Localization-aware_Roll-out_Planning_for_Future_Mobile_Networks.pdf
Placido Mursia, Italo Atzeni, Laura Cottatellucci, David Gesbert: “Enforcing Statistical Orthogonality in Massive MIMO Systems via Covariance Shaping”, IEEE Transactions on Wireless Communications, 2022
This paper tackles the problem of downlink data transmission in massive multiple-input multiple-output (MIMO) systems where user equipment exhibits high spatial correlation and channel estimation is limited by strong pilot contamination. Signal subspace separation among user equipment is, in fact, rarely realized in practice and is generally beyond the control of the network designer (as it is dictated by the physical scattering environment). In this context, we propose a novel statistical beamforming technique, referred to as MIMO covariance shaping, that exploits multiple antennas at user equipment and leverages the realistic non-Kronecker structure of massive MIMO channels to target a suitable shaping of the channel statistics performed at the user equipment side. To optimize the covariance shaping strategies, we propose a low-complexity block coordinate descent algorithm that is proved to converge to a limit point of the original nonconvex problem. For the two user equipment cases, this is shown to converge to a stationary point of the original problem. Numerical results illustrate the sum-rate performance gains of the proposed method with respect to spatial multiplexing, in scenarios where the spatial selectivity of the base station is not sufficient to separate closely spaced user equipment.
Published in: IEEE Transactions on Wireless Communications
Xavier Costa and Andres Garcia-Saavedra: "O-RAN: Disrupting the Virtualized RAN Ecosystem", IEEE Communications Standards Magazine (2021)
The O-RAN Alliance is a worldwide effort to reach new levels of openness in next-generation virtualized radio access networks (vRANs). Initially launched by five major mobile carriers a couple of years ago, it is nowadays supported by over 160 companies (including 24 mobile operators across 4 continents) representing an outstand-ing example of how operators and suppliers around the world can constructively collaborate to define novel technical standards. In this article, we provide a summary of the O-RAN Alliance RAN architecture along with its main building blocks. Then a practical use case exploiting the AI/ML-based innovations enabled by O-RAN is presented, showcasing its disrupting potential. Based on this, the defined interfaces and services are described. Finally, a discussion on the pros and cons of O-RAN is provided along with the conclusions.
Full paper download: O-RAN_Disrupting_the_Virtualized_RAN_Ecosystem_2021.pdf
Gines Garcia-Aviles, Andres Garcia-Saavedra, Marco Gramaglia, Xavier Costa Pérez, Pablo Serrano, Albert Banchs: “Nuberu: Reliable RAN Virtualization in Shared Platforms”, ACM MobiCom 2021 (accepted)
Virtualized RAN will become a key technology for the last mile of next-generation mobile networks driven by initiatives such as the O-RAN alliance. However, considering the computing fluctuations inherent to wireless dynamics and resource contention in shared infrastructure, the price to migrate from dedicated to cloudified platforms may be too high. Recent solutions to virtualize some digital signal processing (DSP) tasks certainly help but do not tackle the problem at its core: a DSP pipeline that requires predictable computing. We present Nuberu, a novel pipeline architecture that is specifically engineered for 4G/5G PHYs virtualized over clouds. Our design follows two objectives: (?) resiliency upon unpredictable computing; and (??) parallelization to increase efficiency in multi-core clouds. To this end, we employ techniques such as tight deadline control, jitter-absorbing buffers, predictive HARQ, and congestion control. Using an experimental prototype, we show that Nuberu attains >95% of the theoretical spectrum efficiency in hostile environments—where state-of-art architectures collapse—thanks to its purposeful cloud-friendly design, and at least 80% resource sav-ings thanks to more efficient use of multi-core infrastructure.
Conference: ACM MobiCom 2021
This research is a collaboration between NEC Laboratories Europe, I2CAT, IMDEA Networks and Universidad Carlos III de Madrid
Nina Slamnik-Kriještorac, Girma Mamuye Yilma, Faqir Zarrar Yousaf, Marco Liebsch, Johann M. Marquez-Barja: "Multi-domain MEC orchestration platform for enhanced Back Situation Awareness", IEEE Conference on Computer Communications (INFOCOM) 2021
Network Function Virtualization (NFV) and Multi-Access Edge Computing (MEC) are among the key technology pillars of 5G systems & beyond for fostering and enhancing the performance of new and existing use cases. In the context of public safety, 5G offers great opportunities towards enhancing mission-critical services, by running network functions at the network edge to provide reliable & low-latency services. This demo introduces an on-demand Back Situation Awareness (BSA) application service, in a multi-domain scenario, enabling early notification for vehicles of an approaching Emergency Vehicle (EmV), indicating its Estimated Time of Arrival (ETA). The application provides the drivers ample time to create a safety corridor for the EmV to pass through unhindered in a safe manner thereby increasing the mission success. For this demo, we have developed an orchestrated MEC platform on which we have implemented the BSA service following modern cloud-native principles, based on Docker and Kubernetes.
Index Terms—MEC, 5G, multi-domain back situation awareness, NFV orchestration, vehicular communications
Presented at: IEEE Conference on Computer Communications (INFOCOM) 2021
Paper available at: https://ieeexplore.ieee.org/abstract/document/9484632
Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Perez, George Iosifidis: "Demonstrating a Bayesian Online Learning for Energy-Aware Resource Orchestration in vRANs", IEEE Conference on Computer Communications (INFOCOM), 2021
Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We demonstrate a novel machine learning approach to solve resource orchestration problems in energy-constrained vRANs. Specifically, we demonstrate two algorithms: (i) BP-vRAN, which uses Bayesian online learning to balance performance and energy consumption, and (ii) SBP-vRAN, which augments our Bayesian optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient—converge an order of magnitude faster than other machine learning methods—and have provably performance, which is paramount for carrier-grade vRANs. We demonstrate the advantages of our approach in a testbed comprised of fully-fledged LTE stacks and a power meter, and implementing our approaching into O-RAN’s non-real-time RAN Intelligent Controller (RIC).
Presented at: IEEE Conference on Computer Communications (INFOCOM), 2021
Full paper download: Demonstrating a Bayesian Online Learning for Energy-Aware Resource Orchestration in vRANs (pdf)
F.W.Murti, J.A.Romero, A.Garcia-Saavedra, X.C.Pérez, G.Iosifidis: "Optimal Deployment Framework for Multi-Cloud Virtualized Radio Access Networks", IEEE Trans. Wireless Communications, 2021.
Virtualized radio access networks (vRAN) are emerging as a key component of wireless cellular networks, and it is therefore imperative to optimize their architecture. vRANs are decentralized systems where the Base Station (BS) functions can be split between the edge Distributed Units (DUs) and Cloud computing Units (CUs); hence they have many degrees of design freedom. We propose a framework for optimizing the number and location of CUs, the function split for each BS, and the association and routing for each DU-CU pair. We combine a linearization technique with a cutting-planes method to expedite the exact problem solution. The goal is to minimize the network costs and balance them with the criterion of centralization, i.e., the number of functions placed at CUs. Using data-driven simulations we find that multi-CU vRANs achieve cost savings up to 28% and improve centralization by 77%, compared to single-CU vRANs. Interestingly, we see non-trivial trade-offs among centralization and cost, which can be aligned or conflicting based on the traffic and network parameters. Our work sheds light on the vRAN design problem from a new angle, highlights the importance of deploying multiple CUs, and offers a rigorous optimization tool for balancing costs and performance.
Published in:IEEE Transactions on Wireless Communications
Full paper download: Optimal_Deployment_Framework_for_Multi-Cloud_vRANs_pre-print_2021.pdf
Antonio Albanese, Vincenzo Sciancalepore, Xavier Costa-Pérez: “SARDO: An Automated Search-and-Rescue "Drone-based Solution for Victims Localization", accepted to IEEE Transactions on Mobile Computing
Natural disasters affect millions of people every year. Finding missing persons in the shortest possible time is of crucial importance to reduce the death toll. This task is especially challenging when victims are sparsely distributed in large and/or difficult-to-reach areas and cellular networks are down. In this paper, we present SARDO, a drone-based search and rescue solution that leverages the high penetration rate of mobile phones in the society to localize missing people. SARDO is an autonomous, all-in-one drone-based mobile network solution that does not require infrastructure support or mobile phones modifications. It builds on novel concepts such as pseudo-trilateration combined with machine-learning techniques to efficiently locate mobile phones in a given area. Our results, with a prototype implementation in a field-trial, show that SARDO rapidly determines the location of mobile phones (~3 min/UE) in a given area with an accuracy of few tens of meters and at a low battery consumption cost (~5%). State-of-the-art localization solutions for disaster scenarios rely either on mobile infrastructure support or exploit onboard cameras for human/computer vision, IR, thermal-based localization. To the best of our knowledge, SARDO is the first drone-based cellular search-and-rescue solution able to accurately localize missing victims through mobile phones.
Accepted to: IEEE Transactions on Mobile Computing
To be published: 2021
Jose A. Ayala-Romero, Andres Garcia-Saavedra, Marco Gramaglia, Xavier Costa-Perez, Albert Banchs, Juan J. Alcaraz: "vrAIn: Deep Learning based Orchestration for Computing and Radio Resources in vRANs", IEEE Transactions on Mobile Computing (TMC), 2021
The virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complex relationship between computing and radio dynamics make vRAN resource control particularly daunting. We present vrAIn, a resource orchestrator for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data (traffic and channel quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithm based on an actor-critic neural network structure and a classifier to map contexts into resource control decisions.
We have evaluated vrAIn experimentally, using an open-source LTE stack over different platforms, and via simulations over a production RAN. Our results show that: (i) vrAIn provides savings in computing capacity of up to 30% over CPU-agnostic methods;(ii) it improves the probability of meeting QoS targets by 25% over static policies; (iii) upon computing capacity under-provisioning, vrAIn improves throughput by 25% over state-of-the-art schemes; and (iv) it performs close to an optimal offline oracle. To our knowledge, this is the first work that thoroughly studies the computational behavior of vRANs and the first approach to a model-free solution that does not need to assume any particular platform or context.
Published in: IEEE Transactions on Mobile Computing (TMC), 2021
Full paper download: vrAIn_Deep_Learning_based_Orchestration_for_Computing_and_Radio_Resources_in_vRANs.pdf
U. Fattore, M. Liebsch, C. Bernardos, “UPFlight – An enabler for Avionic MEC in a Drone-extended 5G Mobile Network”, VTC 2020, May 2020
D. Bega, M. Gramaglia, M. Fiore, A. Banchs, X. Costa, “AZTEC: Anticipatory Capacity Allocation for Zero-Touch Network Slicing”, INFOCOM 2020, November 2019
F. Devoti, V. Sciancalepore, I. Filippini, X. Costa Pérez: "PASID: Exploiting Indoor mmWave Deployments for Passive Intrusion Detection", INFOCOM 2020, November 2019
JA. Ayala-Romero, A. Garcia-Saavedra, M.Gramaglia, X. Costa-Perez, A. Banchs, J.J. Alcaraz: “vrAIn: A Deep Learning Approach Tailoring Computing and Radio Resources in Virtualized RANs”, ACM MOBICOM 2019. July 2019
C. Marquez, M. Gramaglia, M. Fiore, A. Banchs, X. Costa-Perez, “Resource Sharing Efficiency in Network Slicing”, IEEE Transactions on Network and Service Management. June 2019
L. Zanzi, F. Cirillo, S. Mangiante, V. Sciancalepore, F. Giust, X CostaPerez and G. Klas, “Evolving Multi-Access Edge Computing to support enhanced IoT deployments”, IEEE Communications Standards Magazine. May 2019
D.Bega, M.Gramaglia, A.Banchs, V.Sciancalepore, X.Costa-Perez, “A Machine Learning approach to 5G Infrastructure Market optimization” IEEE Transactions Journal on Mobile Computing (TMC). Accepted Date March 2019
T. Taleb, A. Ibrahim, S. Konstantinos, F. Zarrar Yousaf, “On Multi-domain Network Slicing Orchestration Architecture & Federated Resource Control”, IEEE Network Magazine, Feb 2019
B. Han, V. Sciancalepore, D. Feng, X. Costa-Perez, H. D. Schotten, "A Utility-driven MultiQueue Admission Control Solution for Network Slicing", IEEE INFOCOM 2019 Accepted Date December 2018
D. Bega, M. Gramaglia, M. Fiore, A. Banchs, X. Costa-Perez, "DeepCog: Cognitive Network Management in Sliced 5G Networks with Deep Learning", IEEE INFOCOM 2019 Accepted Date December 2018