arXiv subjects

Fabrice Guillemin

Publications and source records attributed to Fabrice Guillemin.

At least 19 recordsLinked to original sources

arXiv2026

Entanglement distribution protocols under imperfect fidelity and quantum memory conditions

The rapid development of quantum computers and sensors urges for the development of a quantum Internet capable of transmitting quantum bits over long distances. Photons used for quantum data transfer are fragile over time and sensitive to their environment, so that they cannot be directly used over long distances. To remedy this problem, long distance paths are segmented into shorter links and entangled pairs of photons are distributed over these links and swapped to create end-to-end entangled pairs over long distances, eventually used for teleportation. In this paper, we develop an existing protocol taking account of fidelity and imperfect memories. We shorten the execution time and thus increase its link success probability creating the so-called Locally Heralded Distribution (LHD). It turns out that the proposed protocol outperforms some previous protocols. We benchmark through simulation the performances of protocols considered in this paper by using a blind entanglement protocol as a baseline.

quant-ph↗

arXiv2026

An efficient Progressive Swapping to the Middle distribution protocol adapted to imperfect quantum memories in quantum networks

The distribution of entangled pairs of photons on the links composing a quantum network, combined with Bell state measurements and teleportation, is the basic apparatus to transfer quantum bits (qubits) over long distances. Entanglement distribution establishes an end-to-end entangled pair while consuming intermediate pairs on links and holding them for a certain time period. The technical literature identifies two main kinds of protocols, parallel and sequential ones, the latter having an advantage in resource consumption over the former. In this paper, we introduce an efficient swapping protocol called Progressive Swapping to the Middle (PSM) as it combines the existing Progressive Swapping (PS) protocol from both extremities of a path that meet in the middle where the received pairs are swapped. We compare PSM with two parallel protocols and PS; in our evaluation, we take into account imperfect memories and fidelity degradation. We demonstrate that PSM yields a much better link probability than PS while keeping a reasonable link fidelity, and shows an advantage in resource consumption over other protocols.

quant-ph↗

arXiv2025

Towards End-to-End Network Intent Management with Large Language Models

Large Language Models (LLMs) are likely to play a key role in Intent-Based Networking (IBN) as they show remarkable performance in interpreting human language as well as code generation, enabling the translation of high-level intents expressed by humans into low-level network configurations. In this paper, we leverage closed-source language models (i.e., Google Gemini 1.5 pro, ChatGPT-4) and open-source models (i.e., LLama, Mistral) to investigate their capacity to generate E2E network configurations for radio access networks (RANs) and core networks in 5G/6G mobile networks. We introduce a novel performance metrics, known as FEACI, to quantitatively assess the format (F), explainability (E), accuracy (A), cost (C), and inference time (I) of the generated answer; existing general metrics are unable to capture these features. The results of our study demonstrate that open-source models can achieve comparable or even superior translation performance compared with the closed-source models requiring costly hardware setup and not accessible to all users.

cs.NI↗

arXiv2023

On the number of departures from the $M/M/\infty$ queue in a finite time interval

In this paper, we analyze the number of departures from an initially empty $M/M/\infty$ system in a finite time interval. We observe the system during an exponentially distributed period of time starting from the time origin. We then consider the absorbed Markov chain describing the number of arrivals and departures in the system until the observer leaves the system, triggering the absorption of the Markov chain. The generator of the absorbed Markov chain induces a selfadjoint operator in some Hilbert space. The use of spectral theory then allows us to compute the Laplace transform of several transient characteristics of the $M/M/\infty$ system (namely, the number of transitions of the Markov chain until absorption, the number of departures from the system, etc.). The analysis is extended to the finite capacity $MM/c/c$ system for some finite integer $c$}.

math.PR↗

arXiv2021

DRL-based Slice Placement under Realistic Network Load Conditions

We propose to demonstrate a network slice placement optimization solution based on Deep Reinforcement Learning (DRL), referred to as Heuristically-controlled DRL, which uses a heuristic to control the DRL algorithm convergence. The solution is adapted to realistic networks with large scale and under non-stationary traffic conditions (namely, the network load). We demonstrate the applicability of the proposed solution and its higher and stable performance over a non-controlled DRL-based solution. Demonstration scenarios include full online learning with multiple volatile network slice placement request arrivals.

cs.NI↗

arXiv2021

DRL-based Slice Placement Under Non-Stationary Conditions

We consider online learning for optimal network slice placement under the assumption that slice requests arrive according to a non-stationary Poisson process. We propose a framework based on Deep Reinforcement Learning (DRL) combined with a heuristic to design algorithms. We specifically design two pure-DRL algorithms and two families of hybrid DRL-heuristic algorithms. To validate their performance, we perform extensive simulations in the context of a large-scale operator infrastructure. The evaluation results show that the proposed hybrid DRL-heuristic algorithms require three orders of magnitude of learning episodes less than pure-DRL to achieve convergence. This result indicates that the proposed hybrid DRL-heuristic approach is more reliable than pure-DRL in a real non-stationary network scenario.

cs.NI↗

arXiv2021

On the Robustness of Controlled Deep Reinforcement Learning for Slice Placement

The evaluation of the impact of using Machine Learning in the management of softwarized networks is considered in multiple research works. Beyond that, we propose to evaluate the robustness of online learning for optimal network slice placement. A major assumption to this study is to consider that slice request arrivals are non-stationary. In this context, we simulate unpredictable network load variations and compare two Deep Reinforcement Learning (DRL) algorithms: a pure DRL-based algorithm and a heuristically controlled DRL as a hybrid DRL-heuristic algorithm, to assess the impact of these unpredictable changes of traffic load on the algorithms performance. We conduct extensive simulations of a large-scale operator infrastructure. The evaluation results show that the proposed hybrid DRL-heuristic approach is more robust and reliable in case of unpredictable network load changes than pure DRL as it reduces the performance degradation. These results are follow-ups for a series of recent research we have performed showing that the proposed hybrid DRL-heuristic approach is efficient and more adapted to real network scenarios than pure DRL.

cs.NI↗

arXiv2021

Controlled Deep Reinforcement Learning for Optimized Slice Placement

We present a hybrid ML-heuristic approach that we name "Heuristically Assisted Deep Reinforcement Learning (HA-DRL)" to solve the problem of Network Slice Placement Optimization. The proposed approach leverages recent works on Deep Reinforcement Learning (DRL) for slice placement and Virtual Network Embedding (VNE) and uses a heuristic function to optimize the exploration of the action space by giving priority to reliable actions indicated by an efficient heuristic algorithm. The evaluation results show that the proposed HA-DRL algorithm can accelerate the learning of an efficient slice placement policy improving slice acceptance ratio when compared with state-of-the-art approaches that are based only on reinforcement learning.

cs.LG↗

arXiv2021

A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement

Network Slice placement with the problem of allocation of resources from a virtualized substrate network is an optimization problem which can be formulated as a multiobjective Integer Linear Programming (ILP) problem. However, to cope with the complexity of such a continuous task and seeking for optimality and automation, the use of Machine Learning (ML) techniques appear as a promising approach. We introduce a hybrid placement solution based on Deep Reinforcement Learning (DRL) and a dedicated optimization heuristic based on the Power of Two Choices principle. The DRL algorithm uses the so-called Asynchronous Advantage Actor Critic (A3C) algorithm for fast learning, and Graph Convolutional Networks (GCN) to automate feature extraction from the physical substrate network. The proposed Heuristically-Assisted DRL (HA-DRL) allows to accelerate the learning process and gain in resource usage when compared against other state-of-the-art approaches as the evaluation results evidence.

cs.NI↗

arXiv2021

Asymptotic analysis of the sojourn time of a batch in an $M^{[X]}/M/1$ Processor Sharing Queue

In this paper, we exploit results obtained in an earlier study for the Laplace transform of the sojourn time $Ω$ of an entire batch in the $M^{[X]}/M/1$ Processor Sharing (PS) queue in order to derive the asymptotic behavior of the complementary probability distribution function of this random variable, namely the behavior of $P(Ω>x)$ when $x$ tends to infinity. We precisely show that up to a multiplying factor, the behavior of $P(Ω>x)$ for large $x$ is of the same order of magnitude as $P(ω>x)$, where $ω$ is the sojourn time of an arbitrary job is the system. From a practical point of view, this means that if a system has to be dimensioned to guarantee processing time for jobs then the system can also guarantee processing times for entire batches by introducing a marginal amount of processing capacity.

cs.PF↗

arXiv2021

Cloud-RAN functional split for an efficient fronthaul network

The evolution of telecommunication network towards cloud-native environments enables flexible centralization of the base band processing of radio signals. There is however a trade-off between the centralization benefits and the fronthaul cost for carrying the radio data between distributed antennas and data processing centers, which host the virtual RAN functions. In this paper, we present a specific split solution for an efficient fronthaul, which enables reducing the consumed bandwidth while being compliant with advanced cooperative radio technologies (interference reduction and data rate improvements). The proposed split has been implemented on the basis of Open Air Interface code and shows important gains in the required fronthaul bandwidth as well as significant latency reduction in the processing of radio frames. \\ \textbf{Publisher:} IEEE \\ \textbf{ISBN:}978-1-7281-3130-6

cs.NI↗

arXiv2021

Inversion of a Class of Singular Integral Operators on Entire Functions

Given constants $x, ν\in \mathbb{C}$ and the space $\mathscr{H}_0$ of entire functions in $\mathbb{C}$ vanishing at $0$, we consider the integro-differential operator $$ \mathfrak{L} = \left ( \frac{x \, ν(1-ν)}{1-x} \right ) \; δ\circ \mathfrak{M}\, , $$ with $δ= z \, \mathrm{d}/\mathrm{d}z$ and $\mathfrak{M}:\mathscr{H}_0 \rightarrow \mathscr{H}_0$ defined by $$ \mathfrak{M}f(z) = \int_0^1 e^{-z t^{-ν}(1-(1-x)t)} \, f \left (z \, t^{-ν}(1-t) \right ) \, \frac{\mathrm{d}t}{t}, \qquad z \in \mathbb{C}, $$ for any $f \in \mathscr{H}_0$. Operator $\mathfrak{L}$ originates from an inversion problem in Queuing Theory. Bringing the inversion of $\mathfrak{L}$ back to that of $\mathfrak{M}$ translates into a singular Volterra integral equation, but with no explicit kernel. In this paper, the inverse of operator $\mathfrak{L}$ is derived through a new inversion formula recently obtained for infinite matrices with entries involving Hypergeometric polynomials. For $x \notin \mathbb{R}^- \cup \{1\}$ and $\mathrm{Re}(ν) < 0$, we then show that the inverse $\mathfrak{L}^{-1}$ of $\mathfrak{L}$ on $\mathscr{H}_0$ has the integral representation $$ \mathfrak{L}^{-1}g(z) = \frac{1-x}{2iπx} \, e^{z} \int_1^{(0+)} \frac{e^{-xtz}}{t(t-1)} \, g \left (z \, (-t)^ν(1-t)^{1-ν} \right ) \, \mathrm{d}t, \qquad z \in \mathbb{C}, $$ for any $g \in \mathscr{H}_0$, where the bounded integration contour in the complex plane starts at point 1 and encircles the point 0 in the positive sense. Other related integral representations of $\mathfrak{L}^{-1}$ are also provided.

math.CA↗

arXiv2020

Cloud-RAN Factory: Instantiating virtualized mobile networks with ONAP

In this demo, we exhibit the negotiation based on the TM Forum framework (Customer Facing Service and Resource Facing Service) and the deployment of a fully virtualized end-to-end mobile network (including a RAN desegregated into Remote Unit, Distributed Unit and Centralized Unit) by using ONAP, an open-source network automation platform. The various components of mobile network are containerized and deployed by ONAP on top of Kubernetes. The demo is the first illustration of an end-to-end mobile network, which is fully virtualized up to the remote unit, whose architecture is compatible with the Open-RAN framework, and which implements a PHY layer on the basis of 3GPP 7.3 functional split in Open Air Interface code.

cs.NI↗

arXiv2020

Heuristic for Edge-enabled Network Slicing Optimization using the Power of Two Choices

We propose an online heuristic algorithm for the problem of network slice placement optimization. The solution is adapted to support placement on large scale networks and integrates Edge-specific and URLLC constraints. We rely on an approach called the Power of Two Choices to build the heuristic. The evaluation results show the good performance of the heuristic that solves the problem in few seconds under a large scale scenario. The heuristic also improves the acceptance ratio of network slice placement requests when compared against a deterministic online Integer Linear Programming (ILP) solution.

cs.NI↗

arXiv2020

A baseline Model for the Relationships between Network Operators and Tower Companies

The introduction of virtualization techniques in radio cellular networks allows the emergence of a business based on the outsourcing of towers hosting antennas and operated by the so-called Tower Companies (TowerCos). In this paper, we develop a baseline business model for studying the potential relationships between network operators and TowerCos. It turns out that the gain in operational costs achieved when network operators outsource the management of towers can be gracefully utilized to reduce prices so as to attract more customers. The price drop has however to be carefully realized so as not to break the market share between operators and to preserve competition. To prove this claim, we adopt in a first step a centralized optimization formulation. In a second step, we develop a game theoretic framework.

cs.NI↗

arXiv2020

On the sojourn time of a batch in the $M^{[X]}/M/1$ Processor Sharing Queue

In this paper, we analyze the sojourn of an entire batch in a processor sharing $M^{[X]}/M/1$ processor queue, where geometrically distributed batches arrive according to a Poisson process and jobs require exponential service times. By conditioning on the number of jobs in the systems and the number of jobs in a tagged batch, we establish recurrence relations between conditional sojourn times, which subsequently allow us to derive a partial differential equation for an associated bivariate generating function. This equation involves an unknown generating function, whose coefficients can be computed by solving an infinite lower triangular linear system. Once this unknown function is determined, we compute the Laplace transform and the mean value of the sojourn time of a batch in the system.

math.PR↗

arXiv2020

A New Linear Inversion Formula for a class of Hypergeometric polynomials

Given complex parameters $x$, $ν$, $α$, $β$ and $γ\notin -\mathbb{N}$, consider the infinite lower triangular matrix $\mathbf{A}(x,ν;α, β,γ)$ with elements $$ A_{n,k}(x,ν;α,β,γ) = \displaystyle (-1)^k\binom{n+α}{k+α} \cdot F(k-n,-(β+n)ν;-(γ+n);x) $$ for $1 \leqslant k \leqslant n$, depending on the Hypergeometric polynomials $F(-n,\cdot;\cdot;x)$, $n \in \mathbb{N}^*$. After stating a general criterion for the inversion of infinite matrices in terms of associated generating functions, we prove that the inverse matrix $\mathbf{B}(x,ν;α, β,γ) = \mathbf{A}(x,ν;α, β,γ)^{-1}$ is given by \begin{align} B_{n,k}(x,ν;α, β,γ) = & \; \displaystyle (-1)^k\binom{n+α}{k+α} \; \cdot \nonumber \\ & \; \biggl [ \; \frac{γ+k}{β+k} \, F(k-n,(β+k)ν;γ+k;x) \; + \nonumber \\ & \; \; \; \frac{β-γ}{β+k} \, F(k-n,(β+k)ν;1+γ+k;x) \; \biggr ] \nonumber \end{align} for $1 \leqslant k \leqslant n$, thus providing a new class of linear inversion formulas. Functional relations for the generating functions of related sequences $S$ and $T$, that is, $T = \mathbf{A}(x,ν;α, β,γ) \, S \Longleftrightarrow S = \mathbf{B}(x,ν;α, β,γ) \, T$, are also provided.

math.CA↗

arXiv2020

White Paper on Crowdsourced Network and QoE Measurements -- Definitions, Use Cases and Challenges

This white paper is the outcome of the Würzburg seminar on "Crowdsourced Network and QoE Measurements" which took place from 25-26 September 2019 in Würzburg, Germany. International experts were invited from industry and academia. They are well known in their communities, having different backgrounds in crowdsourcing, mobile networks, network measurements, network performance, Quality of Service (QoS), and Quality of Experience (QoE). The discussions in the seminar focused on how crowdsourcing will support vendors, operators, and regulators to determine the Quality of Experience in new 5G networks that enable various new applications and network architectures. As a result of the discussions, the need for a white paper manifested, with the goal of providing a scientific discussion of the terms "crowdsourced network measurements" and "crowdsourced QoE measurements", describing relevant use cases for such crowdsourced data, and its underlying challenges. During the seminar, those main topics were identified, intensively discussed in break-out groups, and brought back into the plenum several times. The outcome of the seminar is this white paper at hand which is - to our knowledge - the first one covering the topic of crowdsourced network and QoE measurements.

cs.NI↗