Portrait of Antoine Lesage-Landry

Antoine Lesage-Landry

Associate Academic Member

Associate Professor, Polytechnique Montréal, Department of Electrical Engineering

Research Topics

Online Learning

Optimization

Biography

I am an Associate professor in the Department of Electrical Engineering at Polytechnique Montréal. I received my BEng degree in engineering physics from Polytechnique Montréal in 2015, and my PhD degree in electrical engineering from the University of Toronto in 2019. I was a postdoctoral scholar in the Energy & Resources Group at the University of California, Berkeley, from 2019 to 2020. My research interests include optimization, online learning and machine learning, and their application to power systems with renewable generation.

Current Students

Master's Research - Polytechnique Montréal

Master's Research - Polytechnique Montréal

Olivier Bélanger

Master's Research - Polytechnique Montréal

Mohamed Charara

Master's Research - Polytechnique Montréal

Co-supervisor :

Hanane Dagdougui

Marc-Antoine Coulombe

PhD - Université du Québec à Rimouski

Victor Darleguy

Master's Research - Polytechnique Montréal

Master's Research - Polytechnique Montréal

PhD - Polytechnique Montréal

Master's Research - Polytechnique Montréal

Jérôme Lafontaine

Master's Research - Polytechnique Montréal

PhD - Polytechnique Montréal

Master's Research - Polytechnique Montréal

PhD - Polytechnique Montréal

Master's Research - Polytechnique Montréal

Christina G. Soldati

Master's Research - Polytechnique Montréal

Etienne Tremblay

Master's Research - Polytechnique Montréal

PhD - Polytechnique Montréal

chao.yin@polymtl.ca

Publications

A Scalable Architecture for Future Regenerative Satellite Payloads

Olfa Ben Yahia

Zineb Garroussi

Brunilde Sansò

Jean-François Frigon

Stéphane Martel

Antoine Lesage-Landry

Gunes Karabulut Kurt

This paper addresses the limitations of current satellite payload architectures, which are predominantly hardware-driven and lack the flexib… (see more)ility to adapt to increasing data demands and uneven traffic. To overcome these challenges, we present a novel architecture for future regenerative and programmable satellite payloads and utilize interconnected modem banks to promote higher scalability and flexibility. We formulate an optimization problem to efficiently manage traffic among these modem banks and balance the load. Additionally, we provide comparative numerical simulation results, considering end-to-end delay and packet loss analysis. The results illustrate that our proposed architecture maintains lower delays and packet loss even with higher traffic demands and smaller buffer sizes.

2024-07-08

ArXiv (preprint)

A Scalable Architecture for Future Regenerative Satellite Payloads

Olfa Ben Yahia

Zineb Garroussi

Brunilde Sansò

Jean-François Frigon

Stéphane Martel

Antoine Lesage-Landry

Gunes Karabulut Kurt

This paper addresses the limitations of current satellite payload architectures, which are predominantly hardware-driven and lack the flexib… (see more)ility to adapt to increasing data demands and uneven traffic. To overcome these challenges, we present a novel architecture for future regenerative and programmable satellite payloads and utilize interconnected modem banks to promote higher scalability and flexibility. We formulate an optimization problem to efficiently manage traffic among these modem banks and balance the load. Additionally, we provide comparative numerical simulation results, considering end-to-end delay and packet loss analysis. The results illustrate that our proposed architecture maintains lower delays and packet loss even with higher traffic demands and smaller buffer sizes.

2024-07-08

ArXiv (preprint)

Tensor-based Space Debris Detection for Satellite Mega-constellations

Olivier Daoust

Hasan Nayir

Irfan Azam

Antoine Lesage-Landry

Gunes Karabulut Kurt

2024-06-09

2024 IEEE International Conference on Communications Workshops (ICC Workshops) (published)

Quality of Service-Constrained Online Routing in High Throughput Satellites

Olivier Bélanger

Olfa Ben Yahia

Stéphane Martel

Antoine Lesage-Landry

Gunes Karabulut Kurt

High throughput satellites (HTSs) outpace traditional satellites due to their multi-beam transmission. The rise of low Earth orbit mega cons… (see more)tellations amplifies HTS data rate demands to terabits/second with acceptable latency. This surge in data rate necessitates multiple modems, often exceeding single device capabilities. Consequently, satellites employ several processors, forming a complex packet-switch network. This can lead to potential internal congestion and challenges in adhering to strict quality of service (QoS) constraints. While significant research exists on constellation-level routing, a literature gap remains on the internal routing within a single HTS. The intricacy of this internal network architecture presents a significant challenge to achieve high data rates. This paper introduces an online optimal flow allocation and scheduling method for HTSs. The problem is presented as a multi-commodity flow instance with different priority data streams. An initial full time horizon model is proposed as a benchmark. We apply a model predictive control (MPC) approach to enable adaptive routing based on current information and the forecast within the prediction time horizon while allowing for deviation of the latter. Importantly, MPC is inherently suited to handle uncertainty in incoming flows. Our approach minimizes the packet loss by optimally and adaptively managing the priority queue schedulers and flow exchanges between satellite processing modules. Central to our method is a routing model focusing on optimal priority scheduling to enhance data rates and maintain QoS. The model's stages are critically evaluated, and results are compared to traditional methods via numerical simulations. Through simulations, our method demonstrates performance nearly on par with the hindsight optimum, showcasing its efficiency and adaptability in addressing satellite communication challenges.

2024-03-02

2024 IEEE Aerospace Conference (published)

Correction to: Multi-agent reinforcement learning for fast-timescale demand response of residential loads

Vincent Mai

Philippe Maisonneuve

Tianyu Zhang

Hadi Nekoei

Antoine Lesage-Landry

2024-02-23

Machine-mediated learning (published)

Efficient Data-Driven MPC for Demand Response of Commercial Buildings

Marie-Christine Par'e

Vasken Dermardiros

Antoine Lesage-Landry

Model predictive control (MPC) has been shown to significantly improve the energy efficiency of buildings while maintaining thermal comfort.… (see more) Data-driven approaches based on neural networks have been proposed to facilitate system modelling. However, such approaches are generally nonconvex and result in computationally intractable optimization problems. In this work, we design a readily implementable energy management method for small commercial buildings. We then leverage our approach to formulate a real-time demand bidding strategy. We propose a data-driven and mixed-integer convex MPC which is solved via derivative-free optimization given a limited computational time of 5 minutes to respect operational constraints. We consider rooftop unit heating, ventilation, and air conditioning systems with discrete controls to accurately model the operation of most commercial buildings. Our approach uses an input convex recurrent neural network to model the thermal dynamics. We apply our approach in several demand response (DR) settings, including a demand bidding, a time-of-use, and a critical peak rebate program. Controller performance is evaluated on a state-of-the-art building simulation. The proposed approach improves thermal comfort while reducing energy consumption and cost through DR participation, when compared to other data-driven approaches or a set-point controller.

2024-01-28

ArXiv (preprint)

Evolution of High-Throughput Satellite Systems: A Vision of Programmable Regenerative Payload

Olfa Ben Yahia

Zineb Garroussi

Olivier Bélanger

Brunilde Sansò

Jean-François Frigon

Stéphane Martel

Antoine Lesage-Landry

Gunes Karabulut Kurt

High-throughput satellite (HTS), with its digital payload technology, is expected to play a key role as an enabler of the upcoming sixth-gen… (see more)eration (6G) networks. HTS is mainly designed to provide higher data rates and capacities. Fueled by technological advancements, including beamforming, advanced modulation techniques, reconfigurable phased array technologies, and electronically steerable antennas, HTS has emerged as a fundamental component for future network generations. This paper offers a comprehensive state-of-the-art on HTS systems, focusing on standardization, patents, channel multiple access techniques, routing, load balancing, and the role of software-defined networking (SDN). In addition, we provide a vision for next-generation satellite systems that we have named Extremely-HTS (EHTS) toward autonomous satellites supported by the main requirements and key technologies expected for these systems. The EHTS system will be designed to maximize spectrum reuse and data rates and to flexibly steer the capacity to satisfy user demand. We introduce a novel architecture for future programmable regenerative payloads as well.

2024-01-01

IEEE Communications Surveys & Tutorials (published)

Evolution of High Throughput Satellite Systems: Vision, Requirements, and Key Technologies

Olfa Ben Yahia

Zineb Garroussi

Olivier Bélanger

Brunilde Sansò

Jean-François Frigon

Stéphane Martel

Antoine Lesage-Landry

Gunes Karabulut Kurt

High throughput satellites (HTS), with their digital payload technology, are expected to play a key role as enablers of the upcoming 6G netw… (see more)orks. HTS are mainly designed to provide higher data rates and capacities. Fueled by technological advancements including beamforming, advanced modulation techniques, reconfigurable phased array technologies, and electronically steerable antennas, HTS have emerged as a fundamental component for future network generation. This paper offers a comprehensive state-of-the-art of HTS systems, with a focus on standardization, patents, channel multiple access techniques, routing, load balancing, and the role of software-defined networking (SDN). In addition, we provide a vision for next-satellite systems that we named as extremely-HTS (EHTS) toward autonomous satellites supported by the main requirements and key technologies expected for these systems. The EHTS system will be designed such that it maximizes spectrum reuse and data rates, and flexibly steers the capacity to satisfy user demand. We introduce a novel architecture for future regenerative payloads while summarizing the challenges imposed by this architecture.

2023-10-06

ArXiv (preprint)

Online Interior-point Methods for Time-varying Equality-constrained Optimization

Jean-Luc Lupien

Iman Shames

Antoine Lesage-Landry

2023-07-30

ArXiv (preprint)

Mitigating Equipment Overloads due to Electric Vehicle Charging Using Customer Incentives

Feng Li

Ilhan Kocar

Antoine Lesage-Landry

This paper first presents a time-series impact analysis of charging electric vehicles (EVs) to loading levels of power network equipment con… (see more)sidering stochasticity in charging habits of EV owners. A novel incentive-based mitigation strategy is then designed to shift the EV charging from the peak hours when the equipment is overloaded to the off-peak hours and maintain equipment service lifetime. The incentive level and corresponding contributions from customers to alter their EV charging habits are determined by a search algorithm and a constrained optimization problem. The strategy is illustrated on a modified version of the IEEE 8500 feeder with a high EV penetration to mitigate overloads on the substation transformer.

2023-07-16

2023 IEEE Power & Energy Society General Meeting (PESGM) (published)

Overcoming the Technical Challenges of Coordinating Distributed Load Resources at Scale

Johanna Mathieu

Ian Hiskens

Ioannis Marios Granitsas

Oluwagbemileke Oyefeso

Gregory Ledva

Sebastian Nugroho

Salman Nazir

Scott Hinson

Suzanne Russo

Steve Mock

Rachel Jenkins

Jill Harlow

Grant Fisher

Drew Geller

Duncan Callaway

Phillippe Phanivong

Antoine Lesage-Landry

2023-07-08

(published)

Online Dynamic Submodular Optimization

Antoine Lesage-Landry

Julien Pallage

We propose new algorithms with provable performance for online binary optimization subject to general constraints and in dynamic settings. W… (see more)e consider the subset of problems in which the objective function is submodular. We propose the online submodular greedy algorithm (OSGA) which solves to optimality an approximation of the previous round loss function to avoid the NP-hardness of the original problem. We extend OSGA to a generic approximation function. We show that OSGA has a dynamic regret bound similar to the tightest bounds in online convex optimization with respect to the time horizon and the cumulative round optimum variation. For instances where no approximation exists or a computationally simpler implementation is desired, we design the online submodular projected gradient descent (OSPGD) by leveraging the Lova\'sz extension. We obtain a regret bound that is akin to the conventional online gradient descent (OGD). Finally, we numerically test our algorithms in two power system applications: fast-timescale demand response and real-time distribution network reconfiguration.

2023-06-19

ArXiv (preprint)