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.
M. Rossanese, P. Mursia, A. Garcia-Saavedra, V. Sciancalepore, A. Asadi, X. Costa-Perez
In ACM WiNTECH,
2022
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.
J. A. Ayala-Romero, A. Garcia-Saavedra, X. Costa-Perez, G. Iosifidis
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.
G. Garcia-Aviles, A. Garcia-Saavedra, M. Gramaglia, X. Costa-Perez, P. Serrano, A. Banchs
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.
J. Martin-Perez, K. Antevski, A. Garcia-Saavedra, X. Li, C. J. Bernardos
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
J. A. Ayala-Romero, A. Garcia-Saavedra, M. Gramaglia, X. Costa-Perez, A. Banchs, J. J. Alcaraz
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.
F. W. Murti, J. A. Ayala-Romero, A. Garcia-Saavedra, X. Costa-Perez, G. Iosifidis
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.
L. Zanzi, V. Sciancalepore, A. Garcia-Saavedra, X. Costa-Perez, G. Agapiou, H. D. Schotten
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.
L. Zanzi, V. Sciancalepore, A. Garcia-Saavedra, H. D. Schotten, X. Costa-Perez
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).
P. Mursia, V. Sciancalepore, A. Garcia-Saavedra, L. Cottatellucci, X. Costa-Perez, D. Gesbert
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
J. A. Ayala-Romero, A. Garcia-Saavedra, M. Gramaglia, X. Costa-Perez, A. Banchs, J. J. Alcaraz
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.
J. X. Salvat, L. Zanzi, A. Garcia-Saavedra, V. Sciancalepore, X. Costa-Perez
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.
A. Garcia-Saavedra, G. Iosifidis, X. Costa-Perez, D. J. Leith
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.
L. Diez, A. Garcia-Saavedra, V. Valls, X. Li, X. Costa-Perez, R. Aguero
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.
A. Garcia-Saavedra, P. Patras, V. Valls, X. Costa-Perez, D. J. Leith
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.
A. Garcia-Saavedra, M. Karzand, D. J. Leith
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.
I. Gomez-Miguelez, A. Garcia-Saavedra, P. D. Sutton, P. Serrano, C. Cano, D. J. Leith
In ACM WiNTECH,
2016
