Xue (Steve) Liu

Can (Sam) Chen

Doctorat - McGill

Co-superviseur⋅e :

Chris Pal

chencan421@gmail.com

Ralph Cui

Postdoctorat

chengming.hu@mail.mcgill.ca

Google Scholar

Lyu Fuyuan

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Yili Jin

Doctorat - McGill

Google Scholar

Xuan Li

Doctorat - McGill

Zonglun Li

Doctorat - McGill

Junliang Luo

Doctorat - McGill

rishabh.thaney@mail.mcgill.ca

Shaoxiang Qin

Doctorat - McGill

Alejandro Salinas-Medina

Doctorat - McGill

Maîtrise recherche - McGill

Dheeraj Vattikonda

Maîtrise recherche - McGill

Haolun Wu

Doctorat - McGill

Co-superviseur⋅e :

Fernando Diaz

Ye Yuan

Doctorat - McGill

ye.yuan3@mail.mcgill.ca

Google Scholar

Dun Yuan

Maîtrise recherche - McGill

Gaspard Zhao

Postdoctorat - McGill

Co-superviseur⋅e :

Co-enseignement sensible à l’importance pour optimisation hors-ligne fondée sur un modèle

Shuhao Zheng

Doctorat - McGill

Billets de blogue

1 juillet 2024

par

Ye Yuan

Can Chen

Zixuan Liu

Willie Neiswanger

Xue Liu

Lire l'article

Bidirectional Learning for Offline Model-based Optimization

20 septembre 2023

Apprentissage bidirectionnel pour l’optimisation hors ligne basée sur un modèle

par

Can Chen

Yingxue Zhang

Xue Liu

Mark Coates

Lire l'article

Publications

CATS: A Computation-Aware Transaction Processing System with Proactive Unlocking

Bolun Zhu

Yu Hua

Ziyin Long

With the increasing complexity of network applications and high demands for QoS, transaction processing systems have received more attention… (voir plus)s due to salient features of simplicity and atomicity. Computation operations play an important role in transaction processing systems. However, conventional QoS-based mechanisms become inefficient due to the limited concurrent support upon computation operations, thus causing high time consumption in the critical path of concurrency control. In order to efficiently offer concurrent computations, we propose CATS, a Computation Aware Transaction processing System, to mitigate performance impacts caused by computation operations. CATS further leverages program semantics to defer the execution of transaction operations in the commit phase to alleviate unnecessary conflicts caused by computations. Extensive evaluation results demonstrate that CATS significantly outperforms state-of-the-art designs, including 2PL and OCC based transaction processing systems on high-contended and computation-intensive workloads. We have released the open-source codes in GitHub for public use.

2023-06-19

2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS) (publié)

CATS: A Computation-Aware Transaction Processing System with Proactive Unlocking

Bolun Zhu

Yu Hua

Ziyin Long

2023-06-19

International Workshop on Quality of Service (published)

AI Clinics on Mobile (AICOM): Universal AI Doctors for the Underserved and Hard-to-Reach

Tianyi Yang

Tianze Yang

Na An

Ao Kong

Shaoshan Liu

2023-06-17

ArXiv (prépublication)

ANSEL Photobot: A Robot Event Photographer with Semantic Intelligence

Dmitriy Rivkin

Nikhil Kakodkar

David Meger

Oliver Limoyo

Francois Hogan

Our work examines the way in which large language models can be used for robotic planning and sampling in the context of automated photograp… (voir plus)hic documentation. Specifically, we illustrate how to produce a photo-taking robot with an exceptional level of semantic awareness by leveraging recent advances in general purpose language (LM) and vision-language (VLM) models. Given a high-level description of an event we use an LM to generate a natural-language list of photo descriptions that one would expect a photographer to capture at the event. We then use a VLM to identify the best matches to these descriptions in the robot's video stream. The photo portfolios generated by our method are consistently rated as more appropriate to the event by human evaluators than those generated by existing methods.

2023-06-02

2023 IEEE International Conference on Robotics and Automation (ICRA) (publié)

Mixed-Variable PSO with Fairness on Multi-Objective Field Data Replication in Wireless Networks

Dun Yuan

Yujin Nam

Amal Feriani

Abhisek Konar

Di Wu

Seowoo Jang

Digital twins have shown a great potential in supporting the development of wireless networks. They are virtual representations of 5G/6G sys… (voir plus)tems enabling the design of machine learning and optimization-based techniques. Field data replication is one of the critical aspects of building a simulation-based twin, where the objective is to calibrate the simulation to match field performance measurements. Since wireless networks involve a variety of key performance indicators (KPIs), the replication process becomes a multi-objective optimization problem in which the purpose is to minimize the error between the simulated and field data KPIs. Unlike previous works, we focus on designing a data-driven search method to calibrate the simulator and achieve accurate and reliable reproduction of field performance. This work proposes a search-based algorithm based on mixed-variable particle swarm optimization (PSO) to find the optimal simulation parameters. Furthermore, we extend this solution to account for potential conflicts between the KPIs using a-fairness concept to adjust the importance attributed to each KPI during the search. Experiments on field data showcase the effectiveness of our approach to (i) improve the accuracy of the replication, (ii) enhance the fairness between the different KPIs, and (iii) guarantee faster convergence compared to other methods.

2023-06-01

ICC 2023 - IEEE International Conference on Communications (publié)

Multi-Agent Attention Actor-Critic Algorithm for Load Balancing in Cellular Networks

Jikun Kang

Di Wu

Ju Wang

Ekram Hossain

In cellular networks, User Equipment (UE) handoff from one Base Station (BS) to another, giving rise to the load balancing problem among the… (voir plus) BSs. To address this problem, BSs can work collaboratively to deliver a smooth migration (or handoff) and satisfy the UEs' service requirements. This paper formulates the load balancing problem as a Markov game and proposes a Robust Multi-agent Attention Actor-Critic (Robust-MA3C) algorithm that can facilitate collaboration among the BSs (i.e., agents). In particular, to solve the Markov game and find a Nash equilibrium policy, we embrace the idea of adopting a nature agent to model the system uncertainty. Moreover, we utilize the self-attention mechanism, which encourages high-performance BSs to assist low-performance BSs. In addition, we consider two types of schemes, which can facilitate load balancing for both active UEs and idle UEs. We carry out extensive evaluations by simulations, and simulation results illustrate that, compared to the state-of-the-art MARL methods, Robust-MA3C scheme can improve the overall performance by up to 45%.

2023-06-01

ICC 2023 - IEEE International Conference on Communications (publié)

Policy Reuse for Communication Load Balancing in Unseen Traffic Scenarios

Yi Tian Xu

Jimmy Li

Di Wu

M. Jenkin

Seowoo Jang

With the continuous growth in communication network complexity and traffic volume, communication load balancing solutions are receiving incr… (voir plus)easing attention. Specifically, reinforcement learning (RL)-based methods have shown impressive performance compared with traditional rule-based methods. However, standard RL methods generally require an enormous amount of data to train, and generalize poorly to scenarios that are not encountered during training. We propose a policy reuse framework in which a policy selector chooses the most suitable pre-trained RL policy to execute based on the current traffic condition. Our method hinges on a policy bank composed of policies trained on a diverse set of traffic scenarios. When deploying to an unknown traffic scenario, we select a policy from the policy bank based on the similarity between the previous-day traffic of the current scenario and the traffic observed during training. Experiments demonstrate that this framework can outperform classical and adaptive rule-based methods by a large margin.

2023-06-01

ICC 2023 - IEEE International Conference on Communications (publié)

Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks

Igor Kozlov

Dmitriy Rivkin

Wei-Di Chang

Di Wu

Radio Access Networks (RANs) for telecommunications represent large agglomerations of interconnected hardware consisting of hundreds of thou… (voir plus)sands of transmitting devices (cells). Such networks undergo frequent and often heterogeneous changes caused by network operators, who are seeking to tune their system parameters for optimal performance. The effects of such changes are challenging to predict and will become even more so with the adoption of fifth-generation/sixth-generation (5G/6G) networks. Therefore, RAN monitoring is vital for network operators. We propose a self-supervised learning framework that leverages self-attention and self-distillation for this task. It works by detecting changes in Performance Measurement data, a collection of time-varying metrics which reflect a set of diverse measurements of the network performance at the cell level. Experimental results show that our approach outperforms the state of the art by 4% on a real-world based dataset consisting of about hundred thousands time series. It also has the merits of being scalable and generalizable. This allows it to provide deep insight into the specifics of mode of operation changes while relying minimally on expert knowledge.

2023-06-01

ICC 2023 - IEEE International Conference on Communications (publié)

Reinforcement learning for communication load balancing: approaches and challenges

Di Wu

Jimmy Li

Amal Ferini

Yi Tian Xu

M. Jenkin

Seowoo Jang

The amount of cellular communication network traffic has increased dramatically in recent years, and this increase has led to a demand for e… (voir plus)nhanced network performance. Communication load balancing aims to balance the load across available network resources and thus improve the quality of service for network users. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. Furthermore, rule-based methods are difficult to adapt to quickly changing traffic patterns in real-world environments. Reinforcement learning (RL) algorithms, especially deep reinforcement learning algorithms, have achieved impressive successes in many application domains and offer the potential of good adaptabiity to dynamic changes in network load patterns. This survey presents a systematic overview of RL-based communication load-balancing methods and discusses related challenges and opportunities. We first provide an introduction to the load balancing problem and to RL from fundamental concepts to advanced models. Then, we review RL approaches that address emerging communication load balancing issues important to next generation networks, including 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions for applying RL for communication load balancing.

2023-05-31

Frontiers of Computer Science (publié)

Think Before You Act: Decision Transformers with Internal Working Memory

Jikun Kang

Romain Laroche

Xingdi Yuan

Adam Trischler

Jie Fu

Large language model (LLM)-based decision-making agents have shown the ability to generalize across multiple tasks. However, their performan… (voir plus)ce relies on massive data and compute. We argue that this inefficiency stems from the forgetting phenomenon, in which a model memorizes its behaviors in parameters throughout training. As a result, training on a new task may deteriorate the model's performance on previous tasks. In contrast to LLMs' implicit memory mechanism, the human brain utilizes distributed memory storage, which helps manage and organize multiple skills efficiently, mitigating the forgetting phenomenon. Thus inspired, we propose an internal working memory module to store, blend, and retrieve information for different downstream tasks. Evaluation results show that the proposed method improves training efficiency and generalization in both Atari games and meta-world object manipulation tasks. Moreover, we demonstrate that memory fine-tuning further enhances the adaptability of the proposed architecture.

2023-05-24

ArXiv (prépublication)

Eliminating Space Scanning: Fast mmWave Beam Alignment with UWB Radios

Ju Wang

Xi Chen

Due to their large bandwidth and impressive data speed, millimeter-wave (mmWave) radios are expected to play a key role in the 5G and beyond… (voir plus) (e.g., 6G) communication networks. Yet, to release mmWave’s true power, the highly directional mmWave beams need to be aligned perfectly. Most existing beam alignment methods adopt an exhaustive or semi-exhaustive space scanning, which introduces up to seconds of delays. To eliminate the need for complex space scanning, this article presents an Ultra-wideband (UWB)-assisted mmWave communication framework, which leverages the co-located UWB antennas to estimate the best angles for mmWave beam alignment. One major challenge of applying this idea in the real world is the barrier of limited antenna numbers. Commercial-Off-The-Shelf (COTS) devices are usually equipped with only a small number of UWB antennas, which are not enough for the existing algorithms to provide an accurate angle estimation. To solve this challenge, we design a novel Multi-Frequency MUltiple SIgnal Classification (MF-MUSIC) algorithm, which extends the classic MUltiple SIgnal Classification (MUSIC) algorithm to the frequency domain and overcomes the antenna limitation barrier in the spatial domain. Extensive real-world experiments and numerical simulations illustrate the advantage of the proposed MF-MUSIC algorithm. MF-MUSIC uses only three antennas to achieve an accurate angle estimation, which is a mere 0.15° (or a relative difference of 3.6%) different from the state-of-the-art 16-antenna-based angle estimation method.

2023-05-16

ACM Transactions on Sensor Networks (publié)

Embracing Channel Estimation in Multi-Packet Reception of ZigBee

Zhe Wang

Linghe Kong

Guihai Chen

As a low-power and low-cost wireless protocol, the promising ZigBee has been widely used in sensor networks and cyber-physical systems. Sinc… (voir plus)e ZigBee based networks usually adopt tree or cluster topology, the convergecast scenarios are common in which multiple transmitters send packets to one receiver, leading to the severe collision problem. The conventional ZigBee adopts carrier sense multiple access with collisions avoidance to avoid collisions, which introduces additional time/energy overhead. The state-of-the-art methods resolve collisions instead of avoidance, in which mZig decomposes a collision by the collision itself and reZig decodes a collision by comparing with reference waveforms. However, mZig falls into high decoding errors only exploiting the signal amplitudes while reZig incurs high computational complexity for waveform comparison. In this paper, we propose CmZig to embrace channel estimation in multiple-packet reception (MPR) of ZigBee, which effectively improves MPR via lightweight computing used for channel estimation and collision decomposition. First, CmZig enables accurate collision decomposition with low computational complexity, which uses the estimated channel parameters modeling both signal amplitudes and phases. Second, CmZig adopts reference waveform comparison only for collisions without chip-level time offsets, instead of the complex machine learning based method. We implement CmZig on USRP-N210 and establish a six-node testbed. Results show that CmZig achieves a bit error rate in the order of

2023-05-01

IEEE Transactions on Mobile Computing (publié)