Arnau Romero, Carmen Delgado, Lanfranco Zanzi, Raul Suarez, Xavier Costa Pérez: “Cellular-enabled Collaborative Robots Planning and Operations for Search-and-Rescue Scenarios”, the IEEE International Conference on Robotics and Automation (ICRA) 2024
Mission-critical operations, particularly in the context of Search-and-Rescue (SAR) and emergency response situations, demand optimal performance and efficiency from every component involved to maximize the success probability of such operations. In these settings, cellular-enabled collaborative robotic systems have emerged as invaluable assets, assisting first responders in several tasks, ranging from victim localization to hazardous area exploration. However, a critical limitation in the deployment of cellular-enabled collaborative robots in SAR missions is their energy budget, primarily supplied by batteries, which directly impacts their task execution and mobility. This paper tackles this problem, and proposes a search-and-rescue framework for cellular-enabled collaborative robots use cases that, taking as input the area size to be explored, the robots fleet size, their energy profile, exploration rate required and target response time; finds the minimum number of robots able to meet the SAR mission goals and the path they should follow to explore the area. Our results show that i) first responders can rely on a SAR cellular-enabled robotics framework when planning mission-critical operations to take informed decisions with limited resources and ii) illustrate the number of robots versus explored area and response time trade-off depending on the type of robot: wheeled vs quadruped.
Federico Mungari, Corrado Puligheddu, Andres Garcia-Saavedra, Carla Fabiana Chiasserini: “OREO: O-RAN Intelligence Orchestration of xApp-based Network Services”, IEEE International Conference on Computer Communications (INFOCOM) 2024
The Open Radio Access Network (O-RAN) architecture aims to support a plethora of network services, such as beam management and network slicing, through the use of third-party applications called xApps. To efficiently provide network services at the radio interface, it is thus essential that the deployment of the xApps is carefully orchestrated. In this paper, we introduce OREO, an O-RAN xApp orchestrator, designed to maximize the offered services. OREO’s key idea is that services can share xApps whenever they correspond to semantically equivalent functions, and the xApp output is of sufficient quality to fulfill the service requirements. By leveraging a multi-layer graph model that captures all the system components, from services to xApps, OREO implements an algorithmic solution that selects the best service configuration, maximizes the number of shared xApps, and efficiently and dynamically allocates resources to them. Numerical results as well as experimental tests performed using our proof-of-concept implementation, demonstrate that OREO closely matches the optimum, obtained by solving an NP-hard problem. Further, it outperforms the state of the art, deploying up to 35% more services with an average of 30% fewer xApps and a similar reduction in the resource consumption.
Full paper download: OREO_O-RAN_intElligence_Orchestration_of_xApp-based_network_services_pre-print.pdf
Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Perez: “Risk-Aware Continuous Control with Neural Contextual Bandits”, the 38th Annual AAAI Conference on Artificial Intelligence 2024
Recent advances in learning techniques have garnered attention for their applicability to a diverse range of real-world sequential decision-making problems. Yet, many practical applications have critical constraints for operation in real environments. Most learning solutions often neglect the risk of failing to meet these constraints, hindering their implementation in real-world contexts. In this paper, we propose a risk-aware decision-making framework for contextual bandit problems, accommodating constraints and continuous action spaces. Our approach employs an actor multi-critic architecture, with each critic characterizing the distribution of performance and constraint metrics. Our framework is designed to cater to various risk levels, effectively balancing constraint satisfaction against performance. To demonstrate the effectiveness of our approach, we first compare it against state-of-the-art baseline methods in a synthetic environment, highlighting the impact of intrinsic environmental noise across different risk configurations. Finally, we evaluate our framework in a real-world use case involving a 5G mobile network where only our approach consistently satisfies the system constraint (a signal processing reliability target) with a small performance toll (8.5% increase in power consumption).
In collaboration with: i2CAT Foundation, Catalan Institution for Research and Advanced Studies (ICREA)
Full paper download: Risk_Aware_Decision_Making_for_Continuous_Control_pre-print.pdf
Jose A. Ayala Romero, Leonardo Lo Schiavo, Andres Garcia-Saavedra, Xavier Costa Pérez: “Mean-Field Multi-Agent Contextual Bandit for Energy-Efficient Resource Allocation in vRANs”, IEEE International Conference on Computer Communications (INFOCOM) 2024
Radio Access Network (RAN) virtualization, key for new-generation mobile networks, requires Hardware Accelerators (HAs) that swiftly process wireless signals from Base Stations (BSs) to meet stringent reliability targets. However, HAs are expensive and energy-hungry, which increases costs and has serious environmental implications. To address this problem, we gather data from our experimental platform and compare the performance and energy consumption of a HA (NVIDIA GPU V100) vs. a CPU (Intel Xeon Gold 6240R, 16 cores) for energy-friendly software processing. Based on the insights obtained from this data, we devise a strategy to offload workloads to HAs opportunistically to save energy while preserving reliability. This offloading strategy, however, needs to be configured in near-real-time for every BS sharing common computational resources. This renders a challenging multi-agent collaborative problem in which the number of involved agents (BSs) can be arbitrarily large and can change over time. Thus, we propose an efficient multi-agent contextual bandit algorithm called ECORAN, which applies concepts from mean field theory to be fully scalable. Using a real platform and traces from a production mobile network, we show that ECORAN can provide up to 40% energy savings with respect to the approach used today by the industry.
Oscar Adamuz Hinojosa, Lanfranco Zanzi, Vincenzo Sciancalepore, Andres Garcia-Saavedra, Xavier Costa Pérez: “ORANUS: Latency-tailored Orchestration via Stochastic Network Calculus in 6G O-RAN”, IEEE International Conference on Computer Communications (INFOCOM) 2024
The Open-Radio Access Network (O-RAN) Alliance has introduced a new architecture to enhance the 6th generation (6G) RAN. However, existing O-RAN-compliant solutions lack crucial details to perform effective control loops at multiple time scales. In this vein, we propose ORANUS, an O-RAN-compliant mathematical framework to allocate radio resources to multiple ultra Reliable Low Latency Communication (uRLLC) services at different time scales. In the near-RT control loop, ORANUS relies on a novel Stochastic Network Calculus (SNC)-based model to compute the amount of guaranteed radio resources for each uRLLC service. Unlike traditional approaches as queueing theory, the SNC-based model allows ORANUS to ensure the probability the packet transmission delay exceeds a budget, i.e., the violation probability, is below a target tolerance. ORANUS also utilizes a RT control loop to monitor service transmission queues, dynamically adjusting the guaranteed radio resources based on detected traffic anomalies. To the best of our knowledge, ORANUS is the first O-RAN-compliant solution which benefits from SNC to carry out near-RT and RT control loops. Simulation results show that ORANUS significantly improves over reference solutions, with an average violation probability 10× lower.
Paper (pre-print): https://arxiv.org/abs/2401.03812
Marco Rossanese, Andres Garcia-Saavedra, Andra Elena Lutu, Xavier Costa Perez: “Data-driven Analysis of the Cost-Performance Trade-off of Reconfigurable Intelligent Surfaces in a Production Network”, ACM CoNEXT 2023
This paper presents a comprehensive study on the deployment of Reconfigurable Intelligent Surfaces (RIS) in urban environments with poor radio coverage. We focus on the city of London, a large metropolis where radio network planning presents unique challenges due to diverse geographical and structural features. Using crowd-sourced datasets, we analyze the Reference Signal Received Power (RSRP) from end-user devices to understand the existing radio coverage landscape of a major Mobile Network Operator (MNO). Our study identifies areas with poor coverage and proposes the deployment of RIS to enhance signal strength and coverage. We selected a set of potential sites for RIS deployment and, combining data from the MNO, data extracted from a real RIS prototype, and a ray-tracing tool, we analyzed the gains of this novel technology with respect to deploying more conventional technologies in terms of RSRP, coverage, and cost-efficiency.
To the best of our knowledge, this is the first data-driven analysis of the cost-efficiency of RIS technology in the production of urban networks. Our findings provide compelling evidence about the potential of RIS as a cost-efficient solution for enhancing radio coverage in complex urban mobile networks. More specifically, our results indicate that large-scale RIS technology, when applied in real-world urban mobile network scenarios, can achieve 72% of the coverage gains attainable by deploying additional cells with only 22% of their Total Cost of Ownership (TCO) over a 5-year timespan. Consequently, RIS technology offers around 3x higher cost-efficiency than other more conventional coverage-enhancing technologies.
Presented at: ACM CoNEXT 2023
Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Pérez, George Iosifidis: “EdgeBOL: A Bayesian Learning Approach for the Joint Orchestration of vRANs and Mobile Edge AI”, IEEE/ACM Transactions on Networking 2023
Future mobile networks need to support intelligent services which collect and process data streams at the network edge, so as to offer real-time and accurate inferences to users. However, the widespread deployment of these services is hindered by the unprecedented energy cost they induce to the network, and by the difficulties in optimizing their end-to-end operation. To address these challenges, we propose a Bayesian learning framework for jointly configuring the service and the Radio Access Network (RAN), aiming to minimize the total energy consumption while respecting accuracy and latency service requirements. Using a fully-fledged prototype with a software-defined base station (vBS) and a GPU-enabled edge server, we profile a typical video analytics service and identify new performance trade-offs and optimization opportunities. Accordingly, we tailor the proposed learning framework to account for the (possibly varying) network conditions, user needs and service metrics, and apply it to a range of experiments with real traces. Our findings suggest the efficacy of this approach that adapts to different hardware platforms and service requirements, and outperforms state-of-the-art benchmarks based on neural networks.
Published in: IEEE/ACM Transactions on Networking
In collaboration with: i2CAT Foundation, Catalan Institution for Research and Advanced Studies (ICREA), Delft University of Technology
Adrian Lendinez, Lanfranco Zanzi, Sandra Moreno, Guillem Garí, Xi Li, Renxi Qiu, Xavier Costa-Pérez: “Enhancing 5G-enabled Robots Autonomy by Radio-Aware Semantic Maps”, IEEE/RJS International Conference on Intelligent Robots and Systems (IROS) 2023 (accepted)
Future robotics systems aiming for true autonomy must be robust against dynamic and unstructured environments. The 5th generation (5G) mobile network is expected to provide ubiquitous, reliable and low-latency wireless communications to ground robots, especially in outdoor scenarios. Empowered by 5G, the digital transformation of robotics is emerging, enabled by the cloud-native paradigm and the adoption of edge-computing principles for heavy computational task offloading. However, wireless link quality fluctuates due to multiple aspects such as the topography of the deployment area, the presence of obstacles, robots’ movement and the configuration of the serving base stations. This directly impacts not only the connectivity to the robots but also the performance of robot operations, resulting in severe challenges when targeting full robot autonomy. To address such challenges, in this paper, we propose a framework to build a semantic map based on radio quality. By means of our proposed approach, mobile robots can gain knowledge on up-to-date radio context map information of the surrounding environment, hence enabling reliable and efficient robotics operations.
Full paper download: Enhancing_5G-enabled_Robots_Autonomy_by_Radio-Aware_Semantic_Maps_pre-print.pdf
Arnau Romero, Carmen Delgado, Lanfranco Zanzi, Xi Li, Xavier Costa-Pérez: “OROS: Online Operation and Orchestration of Collaborative Robots using 5G”, IEEE Transactions on Network and Service Management 2023 (accepted)
The 5G mobile networks extend the capability for supporting collaborative robot operations in outdoor scenarios. However, the restricted battery life of robots still poses a major obstacle to their effective implementation and utilization in real scenarios. One of the most challenging situations is the execution of mission-critical tasks that require the use of various on-board sensors to perform simultaneous localization and mapping (SLAM) of unexplored environments. Given the time-sensitive nature of these tasks, completing them in the shortest possible time is of the highest importance. In this paper, we analyze the benefits of 5G-enabled collaborative robots by enhancing the intelligence of the robot operation through joint orchestration of Robot Operating System (ROS) and 5G resources for energy-saving purposes, addressing the problem from both offline and online manners. We propose OROS, a novel orchestration approach that minimizes mission-critical task completion times as well as overall energy consumption of 5G-connected robots by jointly optimizing robotic navigation and sensing together with infrastructure resources. We validate our 5G-enabled collaborative framework by means of Matlab/Simulink, ROS software and Gazebo simulator. Our results show an improvement between 3.65% and 11.98% in exploration task by exploiting 5G orchestration features for battery life extension when using 3 robots.
J. Xavier Salvat, Jose A. Ayala-Romero, Lanfranco Zanzi, Andres Garcia-Saavedra, Xavier Costa-Perez: "Open Radio Access Networks (O-RAN) Experimentation Platform: Design and Datasets", IEEE Communications Magazine 2023
The Open Radio Access Network (O-RAN) Alliance is driving the latest evolution of RAN deployments, moving from traditionally closed and dedicated hardware implementations towards virtualized instances running over shared platforms characterized by open interfaces. Such progressive decoupling of radio software components from the hardware paves the road for future efficient and cost-effective RAN deployments. Nevertheless, there are many open aspects towards the successful implementation of O-RAN networks, such as the real-time configuration of the network parameters to maximize performance, how to reliably share processing units among multiple virtualized base station (vBS) instances, how to palliate their energy consumption, or how to deal with the couplings between vRANs and other services co-located at the edge. Intending to shed light on these aspects, in this article, we showcase the design principles of an O-RAN compliant testbed, and present different datasets collected over a wide set of experiments, which are made public to foster research in this field.
Published in: IEEE Communications Magazine 2023
Farhad Rezazadeh, Lanfranco Zanzi, Francesco Devoti, Sergio Barrachina-Muñoz, Engin Zeydan, Xavier Costa-Pérez, and Josep Mangues-Bafalluy: “A Multi-Agent Deep Reinforcement Learning Approach for RAN Resource Allocation in O-RAN”, IEEE Conference on Computer Communications (INFOCOM) 2023
Artificial intelligence (AI) and Machine Learning (ML) are considered as key enablers for realizing the full potential of fifth-generation (5G) and beyond mobile networks, particularly in the context of resource management and orchestration. In this demonstration, we consider a fully-fledged 5G mobile network and develop a multi-agent deep reinforcement learning (DRL) framework for RAN resource allocation. By leveraging local monitoring information generated by a shared gNodeB instance (gNB), each DRL agent aims to optimally allocate radio resources concerning service-specific traffic demands belonging to heterogeneous running services. We perform experiments on the deployed testbed in real-time, showing that DRL-based agents can allocate radio resources fairly while improving the overall efficiency of resource utilization and minimizing the risk of over provisioning.
In collaboration with: Centre Tecnológic de Telecomunicacions de Catalunya (CTTC), Universitat Politècnica de Catalunya · BarcelonaTech (UPC), i2CAT Foundation, ICREA - Institució Catalana de Recerca i Estudis Avançats.
Alessandro Rivitti, Roberto Bifulco, Angelo Tulumello, Marco Bonola, Salvatore Pontarelli: “eHDL: Turning eBPF/XDP Programs into Hardware Designs for the NIC”, ACM ASPLOS 2023
Scaling network packet processing performance to meet the increasing speed of network ports requires software programs to carefully leverage the network devices’ hardware features. This is a complex task for network programmers, who need to learn and deal with the heterogeneity of device architectures, and re-think their software to leverage them. In this paper we make first steps to reverse this design process, enabling the automatic generation of tailored hardware designs starting from a network packet processing program. We introduce eHDL, a high-level synthesis tool that automatically generates hardware pipelines from unmodified Linux’s eBPF/XDP programs. eHDL is designed to enable software developers to directly define and implement the hardware functions they need in the NIC. We prototype eHDL targeting a Xilinx Alveo U50 FPGA NIC, and evaluate it with a set of 5 eBPF/XDP programs. Our results show that the generated pipelines are efficient in terms of required hardware resources, using only 6.5%-13.3% of the FPGA, and always achieve the line rate forwarding throughput with about 1 microsecond of per-packet forwarding latency. Compared to other network-specific high-level synthesis tool, eHDL enables software programmers with no hardware expertise to describe stateful functions that operate on the entire packet data. Compared to alternative processor-based solutions that perform eBFP/XDP offloading to a NIC, eHDL provides 10-100x higher throughput.
Link to paper: https://dl.acm.org/doi/abs/10.1145/3582016.3582035
Marco Rossanese, Placido Mursia, Andres Garcia-Saavedra, Vincenzo Sciancalepore, Arash Asadi, Xavier Costa Pérez: “Experience: Designing, Building, and Characterizing a RF Switch-based Reconfigurable Intelligent Surface”, ACM MobiCom 2022
In this paper, we present our experience designing, prototyping, and empirically characterizing RF Switch-based Reconfigurable Intelligent Surfaces (RIS). Our RIS design comprises arrays of patch antennas, delay lines and programmable radio-frequency (RF) switches that enable almost-passive 3D beamforming, i.e., without active RF components. We implement this design using PCB technology and low-cost electronic components, and thoroughly validate our prototype in a controlled environment with high spatial resolution codebooks. Finally, we make available a large dataset with a complete characterization of our RIS and present the costs associated with reproducing our design.
ACM Reference Format:
Marco Rossanese, Placido Mursia, Andres Garcia-Saavedra, Vincenzo Sciancalepore, Arash Asadi, and Xavier Costa-Perez. 2022. Designing, Building, and Characterizing RF Switch-based Reconfigurable Intelligent Surfaces. In 16th ACM Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization (WiNTECH 2022), October 17, 2022, Sydney, NSW, Australia. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3556564.3558236
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®
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
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).
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
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