Publications associées à Multi-radio Channel Allocation in Competitive Wireless Networks

Channel-Aware 5G RAN Slicing with Customizable Schedulers

This paper focuses on 5G RAN slicing, where the 5G radio resources must be divided across slices (or enterprises) so as to achieve high spectrum efficiency, fairness and isolation across slices, and the ability for each slice to customize how the radio res ...

USENIX ASSOC2023

Cellular Fronthauling for Data Capacity Increase in Underserved Spaces

Adrian Schumacher

The increase in wireless data traffic continues and is a product of several factors. First, new technologies and capabilities enable new use cases for which new products emerge. Then, with the growing user adoption over time, the data traffic is further in ...

EPFL2022

Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Update

Efstratios Panteleimon Skoulakis

In this paper we provide a novel and simple algorithm, Clairvoyant Multiplicative Weights Updates (CMWU), for convergence to \textit{Coarse Correlated Equilibria} (CCE) in general games. CMWU effectively corresponds to the standard MWU algorithm but where ...

2022

Beam Selection and Tracking for Amplify-and-Forward Repeaters

Ruben Merz, Andreas Peter Burg, Adrian Schumacher

Mobile network operators constantly have to upgrade their cellular network to satisfy the public's hunger for increasing data capacity. However, regulatory limits regarding allowed electromagnetic field strength on existing cell sites often limit or preven ...

IEEE2022

Increasing Cellular Network Energy Efficiency for Railway Corridors

Ruben Merz, Andreas Peter Burg, Adrian Schumacher

Modern trains act as Faraday cages making it challenging to provide high cellular data capacities to passengers. A solution is the deployment of linear cells along railway tracks, forming a cellular corridor. To provide a sufficiently high data capacity, m ...

IEEE2022

Improved Cooperation by Exploiting a Common Signal

Boi Faltings, Panayiotis Danassis, Zeki Doruk Erden

Can artificial agents benefit from human conventions? Human societies manage to successfully self-organize and resolve the tragedy of the commons in common-pool resources, in spite of the bleak prediction of non-cooperative game theory. On top of that, rea ...

International Foundation for Autonomous Agents and Multiagent Systems2022

Exploiting environmental signals to enable policy correlation in large-scale decentralized systems

Boi Faltings, Panayiotis Danassis, Zeki Doruk Erden

Can artificial agents benefit from human conventions? Human societies manage to successfully self-organize and resolve the tragedy of the commons in common-pool resources, in spite of the bleak prediction of non-cooperative game theory. On top of that, rea ...

SPRINGER2022

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

Vincent Gramoli

There is an increasing interest in a fast-growing machine learning technique called Federated Learning (FL), in which the model training is distributed over mobile user equipment (UEs), exploiting UEs' local computation and training data. Despite its advan ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Physical Layer Aspects of LoRa and Full-Duplex Wireless Transceivers

Orion Afisiadis

Wireless communications are currently faced with two main challenges. The first challenge stems from the enormous number of Internet of Things (IoT) devices that transmit very small amounts of data. The second challenge is the need for ever-increasing data ...

EPFL2020

Balancing management of strategic aggregators using non-cooperative game theory

Sidi-Rachid Cherkaoui, Mokhtar Bozorg, Mohammad Rayati

The aggregators are intermediary players at the distribution system level. They manage the financial transactions of resources such as micro-turbines (MTs), photo-voltaic (PV) production systems, flexible demands (FDs), and must-run demands (MDs) in the en ...

Elsevier2020

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Michel Bierlaire, Gael Lederrey, Virginie Janine Camille Lurkin, Timothy Michael Hillel

The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the esti ...

2021

Design and Implementation of a Neural Network Aided Self Interference Cancellation Scheme for Full-Duplex Radios

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Yann Kurzo

In-band full-duplex systems are able to transmit and receive information simultaneously on the same frequency band. Due to the strong self-interference caused by the transmitter to its own receiver, the use of non-linear digital self interference cancellat ...

IEEE2018

When Stuck, Flip a Coin : New Algorithms for Large-Scale Tasks

Slobodan Mitrovic

Many modern services need to routinely perform tasks on a large scale. This prompts us to consider the following question: How can we design efficient algorithms for large-scale computation? In this thesis, we focus on devising a general strategy to addr ...

EPFL2018

Distributed State Estimation and Cooperative Path-Following Under Communication Constraints

Francisco Fernandes Castro Rego

The main topics of this thesis are distributed estimation and cooperative path-following in the presence of communication constraints, with applications to autonomous marine vehicles. To this end, we study algorithms that take explicitly into account the c ...

EPFL2018

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

Dominique Bonvin, Predrag Milosavljevic, René Uwe Schneider

The desire to operate chemical processes in a safe and economically optimal way has motivated the development of so-called real-time optimization (RTO) methods [1]. For continuous processes, these methods aim to compute safe and optimal steady-state set ...

2016

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Michaël Unser, Martin Kurt Storath

We consider

L ^{ 1 }

-TV regularization of univariate signals with values on the real line or on the unit circle. While the real data space leads to a convex optimization problem, the problem is nonconvex for circle-valued data. In this paper, we deriv ...

SIAM2016

Bringing physical layer cooperation closer to practical wireless systems

Emre Atsan

Nowadays the demand for communication over the wireless medium is significantly increasing, while the available wireless spectrum is already limited. In this thesis, we introduce new practical physical layer designs that employ node cooperation for more ef ...

EPFL2015

Solving the Shortest Vector Problem in 2^n Time via Discrete Gaussian Sampling

Divesh Aggarwal

We give a randomized 2^{n+o(n)}-time and space algorithm for solving the Shortest Vector Problem (SVP) on n-dimensional Euclidean lattices. This improves on the previous fastest algorithm: the deterministic O(4^n)-time and O(2^n)-space algorithm of Miccian ...

ACM2015

Centrality of trees for capacitated -center

Ola Nils Anders Svensson, Hyung Chan An, Aditya Bhaskara

We consider the capacitated -center problem. In this problem we are given a finite set of locations in a metric space and each location has an associated non-negative integer capacity. The goal is to choose (open) locations (called centers) and assign each ...

Springer Heidelberg2015

Cross Layer Energy-Efficiency Optimization For Cognitive Radio Transceivers

Andreas Peter Burg, Carl Christian Sten Dominic Senning

Designing energy-efficient cognitive radio transceivers requires joint optimization of medium access control and the physical layer implementation. In this paper we show an energy efficiency optimization strategy for IEEE 802.11n compliant transceivers in ...

IEEE2014