Publications

Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching

Yi Tian Xu

M. Jenkin

Xue Liu

Energy saving in wireless networks is growing in importance due to increasing demand for evolving new-gen cellular networks, environmental a… (voir plus)nd regulatory concerns, and potential energy crises arising from geopolitical tensions. In this work, we propose an approximate dynamic programming (ADP)-based method coupled with online optimization to switch on/off the cells of base stations to reduce network power consumption while maintaining adequate Quality of Service (QoS) metrics. We use a multilayer perceptron (MLP) given each state-action pair to predict the power consumption to approximate the value function in ADP for selecting the action with optimal expected power saved. To save the largest possible power consumption without deteriorating QoS, we include another MLP to predict QoS and a long short-term memory (LSTM) for predicting handovers, incorporated into an online optimization algorithm producing an adaptive QoS threshold for filtering cell switching actions based on the overall QoS history. The performance of the method is evaluated using a practical network simulator with various real-world scenarios with dynamic traffic patterns.

2024-06-08

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

doi.org

arxiv.org

Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization

Jimmy Li

Igor Kozlov

Di Wu

Xue Liu

Gregory Dudek

The use of learning-based methods for optimizing cellular radio access networks (RAN) has received increasing attention in recent years. Thi… (voir plus)s coincides with a rapid increase in the number of cell sites worldwide, driven largely by dramatic growth in cellular network traffic. Training and maintaining learned models that work well across a large number of cell sites has thus become a pertinent problem. This paper proposes a scalable framework for constructing a reinforcement learning policy bank that can perform RAN optimization across a large number of cell sites with varying traffic patterns. Central to our framework is a novel application of anomaly detection techniques to assess the compatibility between sites (tasks) and the policy bank. This allows our framework to intelligently identify when a policy can be reused for a task, and when a new policy needs to be trained and added to the policy bank. Our results show that our approach to compatibility assessment leads to an efficient use of computational resources, by allowing us to construct a performant policy bank without exhaustively training on all tasks, which makes it applicable under real-world constraints.

2024-06-08

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

doi.org

arxiv.org

Ctrl-V: Higher Fidelity Video Generation with Bounding-Box Controlled Object Motion

Ge Ya Luo

Zhi Hao Luo

Anthony Gosselin

Alexia Jolicoeur-Martineau

Christopher Pal

With recent advances in video prediction, controllable video generation has been attracting more attention. Generating high fidelity videos … (voir plus)according to simple and flexible conditioning is of particular interest. To this end, we propose a controllable video generation model using pixel level renderings of 2D or 3D bounding boxes as conditioning. In addition, we also create a bounding box predictor that, given the initial and ending frames' bounding boxes, can predict up to 15 bounding boxes per frame for all the frames in a 25-frame clip. We perform experiments across 3 well-known AV video datasets: KITTI, Virtual-KITTI 2 and BDD100k.

2024-06-08

ArXiv (prépublication)

doi.org

openreview.net

NPA: Improving Large-scale Graph Neural Networks with Non-parametric Attention.

Wentao Zhang

Guochen Yan

Yu Shen

Yang Ling

Yangyu Tao

Bin CUI

Jian Tang

Recent works show great interest in designing Graph Neural Networks (GNNs) that scale to large graphs. While previous work focuses on design… (voir plus)ing advanced sampling techniques for existing GNNs, the design of non-parametric GNNs, an orthogonal direction for scalable performance, has aroused lots of concerns recently. For example, nearly all top solutions in the Open Graph Benchmark leaderboard are non-parametric GNNs. Despite their high predictive performance and scalability, non-parametric GNNs still face two limitations. First, due to the propagation of over-smoothed features, they suffer from severe performance degradation along with the propagation depth. More importantly, they only consider the graph structure and ignore the feature influence during the non-parametric propagation, leading to sub-optimal propagated features. To address these limitations, we present non-parametric attention (NPA), a plug-and-play module that is compatible with non-parametric GNNs, to get scalable and deep GNNs simultaneously. We have deployed NPA in Tencent with the Angel platform, and we further evaluate NPA on both real-world datasets and large-scale industrial datasets. Experimental results on seven homophilic graphs (including the industrial Tencent Video graph) and five heterophilic graphs demonstrate NPA enjoys high performance -- achieves large performance gain over existing non-parametric GNNs, deeper architecture -- improves non-parametric GNNs with large model depth, and high scalability -- can support large-scale graphs with low time costs. Notably, it achieves state-of-the-art performance on the large ogbn-papers100M dataset.

2024-06-08

SIGMOD Conference Companion (publié)

doi.org

Optimizing Energy Saving for Wireless Networks Via Offline Decision Transformer

Yi Tian Xu

Di Wu

M. Jenkin

Seowoo Jang

Xue Liu

Gregory Dudek

With the global aim of reducing carbon emissions, energy saving for communication systems has gained tremendous attention. Efficient energy-… (voir plus)saving solutions are not only required to accommodate the fast growth in communication demand but solutions are also challenged by the complex nature of the load dynamics. Recent reinforcement learning (RL)-based methods have shown promising performance for network optimization problems, such as base station energy saving. However, a major limitation of these methods is the requirement of online exploration of potential solutions using a high-fidelity simulator or the need to perform exploration in a real-world environment. We circumvent this issue by proposing an offline reinforcement learning energy saving (ORES) framework that allows us to learn an efficient control policy using previously collected data. We first deploy a behavior energy-saving policy on base stations and generate a set of interaction experiences. Then, using a robust deep offline reinforcement learning algorithm, we learn an energy-saving control policy based on the collected experiences. Results from experiments conducted on a diverse collection of communication scenarios with different behavior policies showcase the effectiveness of the proposed energy-saving algorithms.

2024-06-08

ICC 2024 - IEEE International Conference on Communications (publié)

doi.org

PEOPLEx: PEdestrian Opportunistic Positioning LEveraging IMU, UWB, BLE and WiFi

Pierre-Yves Lajoie

Bobak H. Baghi

Sachini Herath

Francois Hogan

Xue Liu

Gregory Dudek

This paper advances the field of pedestrian localization by introducing a unifying framework for opportunistic positioning based on nonlinea… (voir plus)r factor graph optimization. While many existing approaches assume constant availability of one or multiple sensing signals, our methodology employs IMU-based pedestrian inertial navigation as the backbone for sensor fusion, opportunistically integrating Ultra-Wideband (UWB), Bluetooth Low Energy (BLE), and WiFi signals when they are available in the environment. The proposed PEOPLEx framework is designed to incorporate sensing data as it becomes available, operating without any prior knowledge about the environment (e.g. anchor locations, radio frequency maps, etc.). Our contributions are twofold: 1) we introduce an opportunistic multi-sensor and real-time pedestrian positioning framework fusing the available sensor measurements; 2) we develop novel factors for adaptive scaling and coarse loop closures, significantly improving the precision of indoor positioning. Experimental validation confirms that our approach achieves accurate localization estimates in real indoor scenarios using commercial smartphones.

2024-06-08

ICC 2024 - IEEE International Conference on Communications (publié)

doi.org

arxiv.org

Probabilistic Mobility Load Balancing for Multi-band 5G and Beyond Networks

Saria Al Lahham

Di Wu

Ekram Hossain

Xue Liu

Gregory Dudek

2024-06-08

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

doi.org

arxiv.org

Promoting Exploration in Memory-Augmented Adam using Critical Momenta

Pranshu Malviya

Goncalo Mordido

Aristide Baratin

Reza Babanezhad Harikandeh

Adaptive gradient-based optimizers, notably Adam, have left their mark in training large-scale deep learning models, offering fast convergen… (voir plus)ce and robustness to hyperparameter settings. However, they often struggle with generalization, attributed to their tendency to converge to sharp minima in the loss landscape. To address this, we propose a new memory-augmented version of Adam that encourages exploration towards flatter minima by incorporating a buffer of critical momentum terms during training. This buffer prompts the optimizer to overshoot beyond narrow minima, promoting exploration. Through comprehensive analysis in simple settings, we illustrate the efficacy of our approach in increasing exploration and bias towards flatter minima. We empirically demonstrate that it can improve model performance for image classification on ImageNet and CIFAR10/100, language modelling on Penn Treebank, and online learning tasks on TinyImageNet and 5-dataset. Our code is available at https://github.com/chandar-lab/CMOptimizer.

2024-06-08

Transactions on Machine Learning Research (accepté)

doi.org

openreview.net

Tensor-based Space Debris Detection for Satellite Mega-constellations

Olivier Daoust

Hasan Nayir

Irfan Azam

Antoine Lesage-Landry

Gunes Karabulut Kurt

2024-06-08

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

doi.org

arxiv.org

Why Don't Prompt-Based Fairness Metrics Correlate?

Abdelrahman Zayed

Goncalo Mordido

Ioana Baldini

A. Chandar

The widespread use of large language models has brought up essential questions about the potential biases these models might learn. This led… (voir plus) to the development of several metrics aimed at evaluating and mitigating these biases. In this paper, we first demonstrate that prompt-based fairness metrics exhibit poor agreement, as measured by correlation, raising important questions about the reliability of fairness assessment using prompts. Then, we outline six relevant reasons why such a low correlation is observed across existing metrics. Based on these insights, we propose a method called Correlated Fairness Output (CAIRO) to enhance the correlation between fairness metrics. CAIRO augments the original prompts of a given fairness metric by using several pre-trained language models and then selects the combination of the augmented prompts that achieves the highest correlation across metrics. We show a significant improvement in Pearson correlation from 0.3 and 0.18 to 0.90 and 0.98 across metrics for gender and religion biases, respectively. Our code is available at https://github.com/chandar-lab/CAIRO.

2024-06-08

ArXiv (prépublication)

doi.org

arxiv.org

A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques

Megh Thakkar

Quentin Fournier

Matthew D Riemer

Pin-Yu Chen

Amal Zouaq

Payel Das

A. Chandar