no code implementations • 7 May 2024 • Chunyan Zheng, Keke Sun, Wenhao Zhao, Haibo Zhou, Lixin Jiang, Shaoyang Song, Chunlai Zhou
Large pretrained language models (LLMs) have shown surprising In-Context Learning (ICL) ability.
no code implementations • 27 Apr 2024 • Luofang Jiao, Jiwei Zhao, Yunting Xu, Tianqi Zhang, Haibo Zhou, Dongmei Zhao
With the ever-increasing number of connected vehicles in the fifth-generation mobile communication networks (5G) and beyond 5G (B5G), ensuring the reliability and high-speed demand of cellular vehicle-to-everything (C-V2X) communication in scenarios where vehicles are moving at high speeds poses a significant challenge. Recently, multi-connectivity technology has become a promising network access paradigm for improving network performance and reliability for C-V2X in the 5G and B5G era.
no code implementations • 22 Mar 2024 • Jiawen Kang, Xiaofeng Luo, Jiangtian Nie, Tianhao Wu, Haibo Zhou, Yonghua Wang, Dusit Niyato, Shiwen Mao, Shengli Xie
As highly computerized avatars of Vehicular Metaverse Users (VMUs), the Vehicle Twins (VTs) deployed in edge servers can provide valuable metaverse services to improve driving safety and on-board satisfaction for their VMUs throughout journeys.
no code implementations • 30 Nov 2023 • Jiwei Zhao, Jiacheng Chen, Zeyu Sun, Yuhang Shi, Haibo Zhou, Xuemin, Shen
As the demand for high-quality services proliferates, an innovative network architecture, the fully-decoupled RAN (FD-RAN), has emerged for more flexible spectrum resource utilization and lower network costs.
no code implementations • 8 Oct 2023 • Yong Zhou, Yuanming Shi, Haibo Zhou, Jingjing Wang, Liqun Fu, Yang Yang
The explosive growth of smart devices (e. g., mobile phones, vehicles, drones) with sensing, communication, and computation capabilities gives rise to an unprecedented amount of data.
no code implementations • 15 Aug 2023 • Longfei Ma, Nan Cheng, Xiucheng Wang, Zhisheng Yin, Haibo Zhou, Wei Quan
To fully leverage the high performance of traditional model-based methods and the low complexity of the NN-based method, a knowledge distillation (KD) based algorithm distillation (AD) method is proposed in this paper to improve the performance and convergence speed of the NN-based method, where traditional SINR optimization methods are employed as ``teachers" to assist the training of NNs, which are ``students", thus enhancing the performance of unsupervised and reinforcement learning techniques.
1 code implementation • 5 Dec 2022 • Luofang Jiao, Kai Yu, Yunting Xu, Tianqi Zhang, Haibo Zhou, Xuemin, Shen
The uplink (UL)/downlink (DL) decoupled access has been emerging as a novel access architecture to improve the performance gains in cellular networks.
Spectral Efficiency Analysis of Uplink-Downlink Decoupled Access in C-V2X Networks
no code implementations • 7 Apr 2022 • He Zhou, Haibo Zhou, Jianguo Li, Kai Yang, Jianping An, Xuemin, Shen
By combining the PCP and MRWP model, the distributions of distances from a typical terminal to the BSs in different tiers are derived.
no code implementations • 28 Mar 2022 • Yinan Zou, Zixin Wang, Xu Chen, Haibo Zhou, Yong Zhou
Based on the convergence analysis, we formulate an optimization problem to minimize the upper bound to enhance the learning performance, followed by proposing an alternating optimization algorithm to facilitate the optimal transceiver design for AirComp-assisted FL.
no code implementations • 18 May 2021 • Wen Wu, Conghao Zhou, Mushu Li, Huaqing Wu, Haibo Zhou, Ning Zhang, Xuemin, Shen, Weihua Zhuang
Then, network slicing solutions are studied to support emerging AI services by constructing AI instances and performing efficient resource management, i. e., slicing for AI.
1 code implementation • NeurIPS 2019 • Fan Zhou, Tengfei Li, Haibo Zhou, Hongtu Zhu, Ye Jieping
Graph-based semi-supervised learning is a very powerful tool in classification tasks, while in most existing literature the labelled nodes are assumed to be randomly sampled.
no code implementations • 25 Sep 2019 • Fan Zhou, Haibo Zhou, Hongtu Zhu
Origin-Destination (OD) flow data is an important instrument in transportation studies.