Portrait of Junliang Luo is unavailable

Junliang Luo

PhD - McGill University
Supervisor
Xue (Steve) Liu
Research Topics
Data Mining
Learning on Graphs
GitHub

Publications

Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching
Junliang Luo
Yi Tian Xu
Di Wu
M. Jenkin
Xue Liu
Gregory Dudek
Energy saving in wireless networks is growing in importance due to increasing demand for evolving new-gen cellular networks, environmental a… (see more)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) (published)
doi.org
arxiv.org
Hallucination Detection and Hallucination Mitigation: An Investigation
Junliang Luo
Tianyu Li
Di Wu
M. Jenkin
Steve Liu
Gregory Dudek
2024-01-15
ArXiv (preprint)
doi.org
arxiv.org
IDEA-DAC: Integrity-Driven Editing for Accountable Decentralized Anonymous Credentials via ZK-JSON
Shuhao Zheng
Zonglun Li
Junliang Luo
Ziyue Xin
Xue Liu
Decentralized Anonymous Credential (DAC) systems are increasingly relevant, especially when enhancing revocation mechanisms in the face of c… (see more)omplex traceability challenges. This paper introduces IDEA-DAC a paradigm shift from the conventional revoke-and-reissue methods, promoting direct and Integrity-Driven Editing (IDE) for Accountable DACs, which results in better integrity accountability, traceability, and system simplicity. We further incorporate an Edit-bound Conformity Check that ensures tailored integrity standards during credential amendments using R1CS-based ZK-SNARKs. Delving deeper, we propose ZK-JSON, a unique R1CS circuit design tailored for IDE over generic JSON documents. This design imposes strictly O(N) rank-1 constraints for variable-length JSON documents of up to N bytes in length, encompassing serialization, encryption, and edit-bound conformity checks. Additionally, our circuits only necessitate a one-time compilation, setup, and smart contract deployment for homogeneous JSON documents up to a specified size. While preserving core DAC features such as selective disclosure, anonymity, and predicate provability, IDEA-DAC achieves precise data modification checks without revealing private content, ensuring only authorized edits are permitted. In summary, IDEA-DAC offers an enhanced methodology for large-scale JSON-formatted credential systems, setting a new standard in decentralized identity management efficiency and precision.
2023-12-31
ACM Web Conference (published)
doi.org