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arXiv 2025年

作者： Keyela, Patatchona Cherkaoui, Soumaya Computer Engineering and Software Engineering Dept. Polytechnique Montreal Montreal Canada

RAN slicing technology is a key aspect of the Open RAN paradigm, allowing simultaneous and independent provision of various services such as ultra-reliable low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC) through virtual networks that share a single radio access infrastructure. Efficient resource allocation is crucial for RAN slicing, as each service has specific quality of service (QoS) requirements, and a balance between different services must be maintained. Although heuristic and reinforcement learning (RL) techniques have been explored to achieve efficient resource allocation, these approaches face notable limitations: heuristic algorithms face complexity issues that limit their effectiveness in large networks, RL solutions are constrained by their dependency on training data and struggle to adapt to new scenarios and environments. This paper proposes a framework that leverages quantum optimization techniques to optimize radio resource blocks allocation in Open RAN slicing for URLLC and eMBB services. We provide a classical problem formulation and the quantum implementation using the constrained quadratic model on Dwave quantum annealing platform, showcasing the potential of quantum optimization techniques to deliver in real-time optimal solutions for optimization problems in 5G and beyond networks. © 2025, CC BY.

关键词： Constrained optimization

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Open RAN Slicing with Quantum Optimization

Open RAN Slicing with Quantum Optimization

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International Global Information Infrastructure Symposium (GIIS)

作者： Patatchona Keyela Soumaya Cherkaoui Computer Engineering and Software Engineering Dept. Polytechnique Montreal Montreal Canada

ISBN: (数字)9798331520113

ISBN: (纸本)9798331520120

关键词： Annealing Training data Open RAN Quality of service Reinforcement learning Ultra reliable low latency communication Quantum annealing Resource management Optimization Faces

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Exploration of the Possibility for a Wider Range of the Discount Factor for Reinforcement Learning

Exploration of the Possibility for a Wider Range of the Disc...

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International Conference on Communications, Information System and computer engineering (CISCE)

作者： Monica M.Q. Li Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

The discount factor for reinforcement learning has been constrained to the range [0, 1] to avoid overestimation of state values during training. Although theoretical studies have indicated that discount factors greater than 1 could be valid for certain tasks, experimental work has generally maintained the [0, 1] limit in flexible discount factor optimization. Our study explores the impact of allowing discount factors to exceed 1 across a variety of reinforcement learning algorithms and classic training environments. Our findings demonstrate that in some grid-based environments, reinforcement learning algorithms with discount factors above 1 can achieve comparable performance to those with discount factors within the [0, 1] range. However, performance tends to be more variable when the discount factor exceeds 1.

关键词： Training Reinforcement learning Task analysis Optimization Information systems

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Securing Next-Generation Networks against Eavesdroppers: FL-Enabled DRL Approach

Securing Next-Generation Networks against Eavesdroppers: FL-...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Deemah H. Tashman Soumaya Cherkaoui Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Anticipated advancements in 5G wireless networks and beyond would necessitate an increased emphasis on security measures to accommodate the projected rise in demand for connections and services. Therefore, this paper aims to investigate the physical layer security (PLS) to evaluate the privacy of authorized users in multi-cellular networks, which represent a fundamental architecture in next-generation networks. Each cell is assumed to include a base station (BS) that serves multiple users. This scenario also takes into account the presence of several eavesdroppers. Every BS functions as a reinforcement learning (RL) agent that must undergo training in order to optimize security. To enhance the safety and speed of training, a federated learning (FL) technique is utilized. In this approach, a central unit regularly receives the neural network (NN) weights from the agents, updates them, and then transfers the result back to the agents to update their model. We examine and compare two deep RL methodologies, specifically deep Q-network, and Reinforce deep policy gradient. The findings of our research demonstrate the influence of the number of eavesdroppers on security, as well as the impact of the number of cells and the aggregation frequency of neural network parameters.

关键词： Training Privacy Federated learning Microprocessors computer architecture Reinforcement learning Physical layer security

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Securing Cognitive IoT Networks: Reinforcement Learning for Adaptive Physical Layer Defense

Securing Cognitive IoT Networks: Reinforcement Learning for ...

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International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Deemah H. Tashman Soumaya Cherkaoui Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

Single-input multiple-output (SIMO) configurations are commonly employed in applications where managing power consumption is critical. This is the case for battery-powered sensors and low-power Internet of Things (IoT) devices, which may also employ energy harvesting (EH) strategies to augment their battery longevity. These systems can further access the underutilized spectrum through cognitive radio networks (CRNs). This study focuses on mitigating eavesdropping concerns in such configurations through the application of physical layer security (PLS). More specifically, the paper proposes a PLS technique to evaluate and enhance the confidentiality of secondary users' (SUs) transmissions for SIMO underlay CRN, considering the presence of an eavesdropper. The secondary user receiver incorporates the power splitting-EH method to extract energy, subsequently utilizing it to generate jamming signals to perplex the eavesdropper. We compare the results obtained when the eavesdropper extracts energy from the SUs' transmissions and when it chooses to only decode the wiretapped messages. We implement a deep reinforcement learning (DRL) approach [1], specifically the deep Q-network, to optimize the transmission power and thereby maximize the secrecy rate of the SUs and demonstrate that the performance of the approach surpasses that of the benchmarks.

关键词： Wireless networks Surveillance Signal processing Sensors Internet of Things Energy harvesting Cognitive radio Security SIMO Eavesdropping

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Exploring the Potential of Llama Models in Automated Code Refinement: A Replication Study

Exploring the Potential of Llama Models in Automated Code Re...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Genevieve Caumartin Qiaolin Qin Sharon Chatragadda Janmitsinh Panjrolia Heng Li Diego Elias Costa Dept of Computer Science and Software Engineering Concordia University Montreal Canada Dept of Computer Engineering and Software Engineering Polytechnique Montreal Montreal Canada

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

Code reviews are an integral part of software development and have been recognized as a crucial practice for minimizing bugs and favouring higher code quality. They serve as an important checkpoint before committing code and play an essential role in knowledge transfer between developers. However, code reviews can be time-consuming and can stale the development of large software projects. In a recent study, Guo et al. assessed how ChatGPT3.5 can help the code review process. They evaluated the effectiveness of ChatGPT in automating the code refinement tasks, where developers recommend small changes in the submitted code. While Guo et al.'s study showed promising results, proprietary models like ChatGPT pose risks to data privacy and incur extra costs for software projects. In this study, we explore alternatives to ChatGPT in code refinement tasks by including two open-source, smaller-scale large language models: CodeLlama and Llama 2 (7B parameters). Our results show that, if properly tuned, the Llama models, particularly CodeLlama, can achieve reasonable performance, often comparable to ChatGPT in auto-mated code refinement. However, not all code refinement tasks are equally successful: tasks that require changing existing code (e.g., refactoring) are more manageable for models to automate than tasks that demand new code. Our study highlights the potential of open-source models for code refinement, offering cost-effective, privacy-conscious solutions for real-world software development.

关键词： Temperature measurement Data privacy Codes Reviews Large language models Documentation Chatbots software Data models software development management

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LiDAR-based Real-Time Object Detection and Tracking in Dynamic Environments

arXiv

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arXiv 2024年

作者： Du, Wenqiang Beltrame, Giovanni MISTLab the Department of Computer Engineering and Software Engineering Polytechnique Montreal Montreal Canada

In dynamic environments, the ability to detect and track moving objects in real-time is crucial for autonomous robots to navigate safely and effectively. Traditional methods for dynamic object detection rely on high accuracy odometry and maps to detect and track moving objects. However, these methods are not suitable for long-term operation in dynamic environments where the surrounding environment is constantly changing. In order to solve this problem, we propose a novel system for detecting and tracking dynamic objects in real-time using only LiDAR data. By emphasizing the extraction of low-frequency components from LiDAR data as feature points for foreground objects, our method significantly reduces the time required for object clustering and movement analysis. Additionally, we have developed a tracking approach that employs intensity-based egomotion estimation along with a sliding window technique to assess object movements. This enables the precise identification of moving objects and enhances the system's resilience to odometry drift. Our experiments show that this system can detect and track dynamic objects in real-time with an average detection accuracy of 88.7% and a recall rate of 89.1%. Furthermore, our system demonstrates resilience against the prolonged drift typically associated with front-end only LiDAR odometry. All of the source code, labeled dataset, and the annotation tool are available at: https://***/MISTLab/lidar dynamic objects *** © 2024, CC BY.

关键词： Object detection

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Automated, Unsupervised, and Auto-parameterized Inference of Data Patterns and Anomaly Detection

arXiv

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arXiv 2024年

作者： Qin, Qiaolin Li, Heng Merlo, Ettore Lamothe, Maxime Dept. of Computer and Software Engineering Polytechnique Montreal Montreal Canada

With the advent of data-centric and machine learning (ML) systems, data quality is playing an increasingly critical role for ensuring the overall quality of software systems. Data preparation, an essential step towards high data quality, is known to be a highly effort-intensive process. Although prior studies have dealt with one of the most impacting issues, data pattern violations, these studies usually require data-specific configurations (i.e., parameterized) or use carefully curated data as learning examples (i.e., supervised), relying on domain knowledge and deep understanding of the data, or demanding significant manual effort. In this paper, we introduce RIOLU: Regex Inferencer autO-parameterized Learning with Uncleaned data. RIOLU is fully automated, automatically parameterized, and does not need labeled samples. RIOLU can generate precise patterns from datasets in various domains, with a high F1 score of 97.2%, exceeding the state-of-the-art baseline. In addition, according to our experiment on five datasets with anomalies, RIOLU can automatically estimate a data column’s error rate, draw normal patterns, and predict anomalies from unlabeled data with higher performance (up to 800.4% improvement in terms of F1) than the state-of-the-art baseline, even outperforming ChatGPT in terms of both accuracy (12.3% higher F1) and efficiency (10% less inference time). A variant of RIOLU, with user guidance, can further boost its precision, with up to 37.4% improvement in terms of F1. Our evaluation in an industrial setting further demonstrates the practical benefits of *** Codes 68P01 © 2024, CC BY.

关键词： Self-supervised learning

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Exploring the Potential of Llama Models in Automated Code Refinement: A Replication Study

arXiv

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arXiv 2024年

作者： Caumartin, Genevieve Qin, Qiaolin Chatragadda, Sharon Panjrolia, Janmitsinh Li, Heng Costa, Diego Elias Dept of Computer Science and Software Engineering Concordia University Montreal Canada Dept of Computer Engineering and Software Engineering Polytechnique Montreal Montreal Canada

Code reviews are an integral part of software development and have been recognized as a crucial practice for minimizing bugs and favouring higher code quality. They serve as an important checkpoint before committing code and play an essential role in knowledge transfer between developers. However, code reviews can be time-consuming and can stale the development of large software projects. In a recent study, Guo et al. assessed how ChatGPT3.5 can help the code review process. They evaluated the effectiveness of ChatGPT in automating the code refinement tasks, where developers recommend small changes in the submitted code. While Guo et al.’s study showed promising results, proprietary models like ChatGPT pose risks to data privacy and incur extra costs for software projects. In this study, we explore alternatives to ChatGPT in code refinement tasks by including two open-source, smaller-scale large language models: CodeLlama and Llama 2 (7B parameters). Our results show that, if properly tuned, the Llama models, particularly CodeLlama, can achieve reasonable performance, often comparable to ChatGPT in automated code refinement. However, not all code refinement tasks are equally successful: tasks that require changing existing code (e.g., refactoring) are more manageable for models to automate than tasks that demand new code. Our study highlights the potential of open-source models for code refinement, offering cost-effective, privacy-conscious solutions for real-world software development. Copyright © 2024, The Authors. All rights reserved.

关键词： Reviews

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Automated Analysis of Halo2 Circuits 21

Automated Analysis of Halo2 Circuits

引用

21st International Workshop on Satisfiability Modulo Theories, SMT 2023

作者： Soureshjani, Fatemeh Heidari Hall-Andersen, Mathias Jahanara, MohammadMahdi Kam, Jeffrey Gorzny, Jan Ahmadvand, Mohsen Computer Engineering and Software Engineering Department Polytechnique Montréal MontrealQC Canada Quantstamp Inc. United States Department of Computer Science Aarhus University Denmark

Zero-knowledge proof systems are becoming increasingly prevalent and being widely used to secure decentralized financial systems and protect the privacy of users. Given the sensitivity of these applications, zero-knowledge proof systems are a natural target for formal verification methods. We describe methods for checking one such proof system: Halo2. We use abstract interpretation and an SMT solver to check various properties of Halo2 circuits. Using abstract interpretation, we can detect unused gates, unconstrained cells, and unused columns. Using an SMT solver, we can detect under-constrained circuits (in the sense that for the same public input they have two efficiently computable satisfying assignments). This is the first work we are aware of that applies lightweight formal methods to PLONKish arithmetization and Halo2 circuits. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Timing circuits

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