Nov 9, 2021
Sep 9, 2021
Mar 1, 2021
Jan 1, 2021

Recent Publications

More Publications

. RadiOrchestra: Proactive Management of Millimeter-wave Self-backhauled Small Cells via Joint Optimization of Beamforming, User Association, Rate Selection, and Admission Control. In IEEE Transactions on Wireless Communications, 2022.

. VERA: Resource Orchestration for Virtualized Services at the Edge. In IEEE ICC, 2022.

. EdgeBOL: Automating Energy-savings for Mobile Edge AI. In ACM CoNEXT, 2021.

Preprint PDF Code Dataset

. Nuberu: Reliable RAN Virtualization in Shared Platforms. In ACM MobiCom, 2021.

Preprint PDF Dataset

. Orchestrating Energy-Efficient vRANs: Bayesian Learning and Experimental Results. In IEEE Transactions on Mobile Computing, 2021.

Preprint PDF Code

Selected Publications

Supporting Edge AI services is one of the most exciting features of future mobile networks. These services involve the collection and processing of voluminous data streams, right at the network edge, so as to offer real-time and accurate inferences to users. However, their widespread deployment is hampered by the energy cost they induce to the network. To overcome this obstacle, 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 desirable accuracy and latency thresholds. Using a fully-fledged prototype with a software-defined base station (BS) and a GPU-enabled edge server, we profile a state-of-the-art video analytics AI service and identify new performance trade-offs. Accordingly, we tailor the optimization framework to account for the network context, the user needs, and the service metrics. The efficacy of our proposal is verified in a series of experiments and comparisons with neural network-based benchmarks.
In ACM CoNEXT, 2021

RAN virtualization will become a key technology for the last mile of next-generation mobile networks driven by initiatives such as the O-RAN alliance. However, due to the computing fluctuations inherent to wireless dynamics and resource contention in shared computing infrastructure, the price to migrate from dedicated to shared platforms may be too high. Indeed, we show in this paper that the baseline architecture of a base station’s distributed unit (DU) collapses upon moments of deficit in computing capacity. Recent solutions to accelerate some signal processing tasks certainly help but do not tackle the core problem: a DU pipeline that requires predictable computing to provide carrier-grade reliability. We present Nuberu, a novel pipeline architecture for 4G/5G DUs specifically engineered for non-deterministic computing platforms. Our design has one key objective to attain reliability: to guarantee a minimum set of signals that preserve synchronization between the DU and its users during computing capacity shortages and, provided this, maximize network throughput. To this end, we use 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 approaches lose connectivity, and at least 80% resource savings.
In ACM MobiCom, 2021

This paper proposes a dynamic pricing and revenue-driven service federation strategy based on a Deep Q-Network (DQN) to instantly and automatically decide federation across different service provider domains, each introduces dynamic service prices offering to its customers and towards other domains. A dynamic pricing model is considered in this work based on the analysis of real pricing data collected from public cloud provider, and upon this a dynamic arrival process as a result of the price changes is proposed for formulating the service federation problem as a Markov Decision Problem (MDP). In this work, several reinforcement learning algorithms are developed to solve the problem, and the presented results show that the DQN method reached 90% of the optimal revenue and outperformed existing state-of-the-art strategies, and it can learn the federation pricing dynamics to make optimum federation decisions according to price changes.
In IEEE Transactions on Network and Service Management, 2021

We present vrAIn, a resource orchestrator for vRANs based on deep reinforcement learning. 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
In IEEE Transactions on Mobile Computing, 2021

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.
In IEEE Transactions on Wireless Communications, 2020

Based on data provided by a major European carrier during mass events in a football stadium comprising up to 30.000 people, 16 base station sectors and 1Km2 area, we performed a data-driven analysis of the radio access network infrastructure dynamics during such events. Given the insights obtained from the analysis, we developed ARENA, a model-free deep learning Radio Access Network (RAN) capacity forecasting solution that, taking as input past network monitoring data and events context information, provides guidance to mobile operators on the expected RAN capacity needed during a future event.
In IEEE Transactions on Network and Service Management, 2020

In this paper, we pioneer a novel radio slicing orchestration solution that simultaneously provides-latency and throughput guarantees in a multi-tenancy environment. Leveraging on a solid mathematical framework, we exploit the exploration-vs-exploitation paradigm by means of a multi-armed-bandit-based (MAB) orchestrator, LACO, that makes adaptive resource slicing decisions with no prior knowledge on the traffic demand or channel quality statistics.
In IEEE Transactions on Wireless Communications, 2020

In this paper we study a beyond 5G scenario consisting of a multi-antenna base station (BS) serving a large set of single-antenna user equipments (UEs) with the aid of RISs to cope with non-line-of-sight paths. Specifically, we build a mathematical framework to jointly optimize the precoding strategy of the BS and the RIS parameters in order to minimize the system sum mean squared error (SMSE).
In IEEE Journal on Selected Areas in Communications, 2020

We present vrAIn, a dynamic resource controller for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data (traffic and signal 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 (encoded) contexts into resource control decisions
In ACM MobiCom, 2019

The main contribution of this paper is threefold. First, we design a hierarchical control plane to manage the orchestration of slices end-to-end, including radio access, transport network, and distributed computing infrastructure. Second, we cast the orchestration problem as a stochastic yield management problem and propose two algorithms to solve it: an optimal Benders decomposition method and a suboptimal heuristic that expedites solutions. Third, we implement an experimental proof-of-concept and assess our approach both experimentally and via simulations with topologies from three real operators and a wide set of realistic scenarios.
In ACM CoNEXT, 2018

In this paper, we propose a novel modeling approach and a rigorous analytical framework, MvRAN, that minimizes vRAN costs and maximizes MEC performance. Our framework selects jointly the base station function splits, the fronthaul routing paths, and the placement of MEC functions.
In IEEE Journal on Selected Areas in Communications, 2018

In this paper we propose LaSR, a practical multi-connectivity scheduler for OFDMA-based multi-RAT systems. LaSR makes optimal discrete control actions by solving a sequence of simple optimization problems that do not require prior information of traffic patterns. In marked contrast to previous work, the flexibility of our approach allows us to construct scheduling policies that achieve a good balance between system cost and utility satisfaction, while jointly operate across heterogeneous RATs, accommodate real-system requirements, and guarantee system stability.
In IEEE Transactions on Mobile Computing, 2018

The 3GPP has recently defined a LAA scheme to enable global U-LTE deployment, aiming at (i) ensuring fair coexistence with incumbent WiFi networks, i.e., impacting on their performance no more than another WiFi device, and (ii) achieving superior airtime efficiency as compared to WiFi. In this paper we show the standardized LAA fails to simultaneously fulfill these objectives, and design an alternative orthogonal (collision-free) listen-before-talk coexistence paradigm that provides a substantial improvement in performance, yet imposes no penalty on existing WiFi networks. We derive two LAA optimal transmission policies, ORLA and OLAA, that maximize LAA throughput in both asynchronous and synchronous (i.e., with alignment to licensed anchor frame boundaries) modes of operation, respectively.
In IEEE/ACM Transactions on Networking, 2018

In this paper, we propose Stochastic Earliest Delivery Path First (S-EDPF), a generalization of EDPF which takes into account uncertainty and time-variation in path delays yet has low-complexity suited to practical implementation. Moreover, we integrate a novel low-delay Forward Error Correction (FEC) scheme into S-EDPF in a principled manner by deriving the optimal schedule for coded packets across multiple paths.
In IEEE Transactions on Mobile Computing, 2017

In this work we present srsLTE, an open-source platform for LTE experimentation designed for maximum modularity and code reuse and fully compliant with LTE Release 8.
In ACM WiNTECH, 2016

Projects

5G-TRANSFORMER

5G-TRANSFORMER is an H2020 PPP project co-funded by the European Commission under the ICT theme (H2020-ICT-2016-2). 5G-TRANSFORMER aims to transform today’s mobile transport network into an SDN/NFV-based Mobile Transport and Computing Platform (MTP), which brings the “Network Slicing” paradigm into mobile transport networks by provisioning and managing MTP slices tailored to the specific needs of vertical industries.

5G-CROSSHAUL

5G-Crosshaul is an H2020 PPP project co-funded by the European Commission under the ICT theme (Call 14). The 5G-Crosshaul project aims at developing a 5G integrated backhaul and fronthaul transport network enabling a flexible and software-defined reconfiguration of all networking elements in a multi-tenant and service-oriented unified management environment.

FLAVIA

FLAVIA is a project funded by the European Commission under FP7 Call 5, Objective 1.1: The Network of the Future. FLAVIA fosters a paradigm shift towards the Future Wireless Internet: from pre-designed link services to programmable link processors. The key concept is to expose flexible programmable interfaces enabling service customization and performance optimization through software-based exploitation of low-level operations and control primitives, e.g., transmission timing, frame customization and processing, spectrum and channel management, power control, etc.

CARMEN

CARMEN (CARrier grade MEsh Networks) is funded by the European Commission under the ICT theme ICT-2007.1.1 - The network of the future. CARMEN will study and specify a wireless mesh network supporting carrier grade triple-play services for mobile/fixed network operators. Future operator networks will be comprised of a common core network and several access networks, and the CARMEN access network will complement other access technologies by providing a low cost and fast deployment mesh network access technology. The project proposes the integration of heterogeneous wireless technologies in a multi-hop fashion to provide scalable and efficient ubiquitous quad-play carrier services.

Service to Research Community

Editorial and Organization duties

Technical Program Committee (TPC) and Reviewing duties

  • TPC member in IEEE Globecom (2018), MadCom (2018), FiCloud (2018), IEEE COMNETSAT (2018), IEEE MASS (2018), EFC-IoT (2018), IEEE VTC (2015-Fall, 2017-spring), Elsevier Computer Communications (2014, 2015, 2016), MACOM (2014, 2015), AWICT (2015), IEEE GlobalSIP (2015), MOBIWAC (2014, 2015, 2016, 2017, 2018), ACM WiNTECH (2013, 2016, 2017), SustainIT (2013, 2014, 2015, 2016), ICALP (2012)
  • Regular reviewer of IEEE Communication Letters (2014 Exemplary Reviewer), IEEE Journal on Selected Areas in Communication (JSAC), IEEE Transactions on Vehicular Technology, IEEE INFOCOM, IEEE Wireless Communications, IEEE/ACM Transactions on Networking, IEEE Transactions on Mobile Computing, IEEE Communications Magazine, IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, IEEE Communication Surveys & Tutorials, IEEE Globecom, IEEE VTC, Elsevier Adhoc Networks, Springer Wireless Networks, International Conference on Communications (ICT), EURASIP Journal on Wireless Communications and Networking, IEEE Globecom, International Workshop on Multiple Access Communications (MACOM), Elsevier Computer Communications (COMCOM), Elsevier Computer Networks (COMNET), International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), International Workshop on Wireless Network Testbeds (WiNTECH), International Colloquium on Automata, Languages and Programming (ICALP), International Symposium on a World of Wireless, Mobile and Multimedia Networks (IEEE WoWMoM).

Teaching

I have supervised the following students:

  • Josep Xavier Salvat, M.Sc. Telematics Eng., Universitat Politècnica de Catalunya (2016).
  • Jose Maria Montes Yuste, B.Sc. Telematics Eng., University Carlos III of Madrid (2013).
  • Georgios Z. Papadopoulos, M.Sc Telematics Eng., University Carlos III of Madrid (2012).
  • Vasileios Papadopoulos, B.Sc. Software Eng., Alexander Technological Institute of Thessaloniki (2011).

My teaching duties, while being a Teaching Assistant (TA) at UC3M, were:

  • Theory of networks (10 /11, 11 /12, 12 /13): Introduction to probability, Markov models and queueing theory. B.Sc. Telematics Eng. University Carlos III of Madrid
  • Audiovisual mobile systems (12 /13): Introduction to wireless communication systems. B.Sc. Telematics Eng. University Carlos III of Madrid
  • Networks and Services (09 /10, 10 /11, 11 /12): General introduction to communication networks. B.Sc. Telematics Eng. University Carlos III of Madrid

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