1.7k citations · 2.9k across the 35 of their papers we have counts for
36 papers
Orchestrating Joint Offloading and Scheduling for Low-Latency Edge SLAM
Yao Zhang, Yuyi Mao, Hui Wang +5
Visual Simultaneous Localization and Mapping (vSLAM) is a prevailing technology for many emerging robotic applications. Achieving real-time SLAM on mobile robotic systems with limi…
Decentralizing Coherent Joint Transmission Precoding via Fast ADMM with Deterministic Equivalents
Xinyu Bian, Yuhao Liu, Yizhou Xu +4
Inter-cell interference (ICI) suppression is critical for multi-cell multi-user networks. In this paper, we investigate advanced precoding techniques for coordinated multi-point (C…
Decentralizing Coherent Joint Transmission Precoding via Deterministic Equivalents
Yuhao Liu, Xinyu Bian, Yizhou Xu +4
In order to control the inter-cell interference for a multi-cell multi-user multiple-input multiple-output network, we consider the precoder design for coordinated multi-point with…
Joint Activity-Delay Detection and Channel Estimation for Asynchronous Massive Random Access: A Free Probability Theory Approach
Xinyu Bian, Yuyi Mao, Jun Zhang
Grant-free random access (RA) has been recognized as a promising solution to support massive connectivity due to the removal of the uplink grant request procedures. While most ende…
Green Edge AI: A Contemporary Survey
Yuyi Mao, Xianghao Yu, Kaibin Huang +2
Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude of industries largely due to their significant resurgence over the past decade. The tr…
FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features
Zijian Li, Zehong Lin, Jiawei Shao +2
Federated learning (FL) is a distributed learning paradigm that maximizes the potential of data-driven models for edge devices without sharing their raw data. However, devices ofte…
Feature Matching Data Synthesis for Non-IID Federated Learning
Zijian Li, Yuchang Sun, Jiawei Shao +3
Federated learning (FL) has emerged as a privacy-preserving paradigm that trains neural networks on edge devices without collecting data at a central server. However, FL encounters…
MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates
Yuchang Sun, Yuyi Mao, Jun Zhang
Federated learning (FL) is a promising framework for privacy-preserving collaborative learning, where model training tasks are distributed to clients and only the model updates nee…
Joint Activity-Delay Detection and Channel Estimation for Asynchronous Massive Random Access
Xinyu Bian, Yuyi Mao, Jun Zhang
Most existing studies on joint activity detection and channel estimation for grant-free massive random access (RA) systems assume perfect synchronization among all active users, wh…
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning
Yuchang Sun, Zehong lin, Yuyi Mao +2
Federated learning (FL) is a popular privacy-preserving distributed training scheme, where multiple devices collaborate to train machine learning models by uploading local model up…
Grant-free Massive Random Access with Retransmission: Receiver Optimization and Performance Analysis
Xinyu Bian, Yuyi Mao, Jun Zhang
There is an increasing demand of massive machine-type communication (mMTC) to provide scalable access for a large number of devices, which has prompted extensive investigation on g…
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design
Yuchang Sun, Jiawei Shao, Yuyi Mao +2
Federated learning (FL) has achieved great success as a privacy-preserving distributed training paradigm, where many edge devices collaboratively train a machine learning model by…
Resource-Constrained Edge AI with Early Exit Prediction
Rongkang Dong, Yuyi Mao, Jun Zhang
By leveraging the data sample diversity, the early-exit network recently emerges as a prominent neural network architecture to accelerate the deep learning inference process. Howev…
Federated Learning with GAN-based Data Synthesis for Non-IID Clients
Zijian Li, Jiawei Shao, Yuyi Mao +2
Federated learning (FL) has recently emerged as a popular privacy-preserving collaborative learning paradigm. However, it suffers from the non-independent and identically distribut…
Error Rate Analysis for Grant-free Massive Random Access with Short-Packet Transmission
Xinyu Bian, Yuyi Mao, Jun Zhang
Grant-free massive random access (RA) is a promising protocol to support the massive machine-type communications (mMTC) scenario in 5G and beyond networks. In this paper, we focus…
Stochastic Coded Federated Learning with Convergence and Privacy Guarantees
Yuchang Sun, Jiawei Shao, Songze Li +2
Federated learning (FL) has attracted much attention as a privacy-preserving distributed machine learning framework, where many clients collaboratively train a machine learning mod…
Asynchronous Semi-Decentralized Federated Edge Learning for Heterogeneous Clients
Yuchang Sun, Jiawei Shao, Yuyi Mao +1
Federated edge learning (FEEL) has drawn much attention as a privacy-preserving distributed learning framework for mobile edge networks. In this work, we investigate a novel semi-d…
Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity
Yuchang Sun, Jiawei Shao, Yuyi Mao +2
Federated edge learning (FEEL) has attracted much attention as a privacy-preserving paradigm to effectively incorporate the distributed data at the network edge for training deep l…
Task-Oriented Communication for Multi-Device Cooperative Edge Inference
Jiawei Shao, Yuyi Mao, Jun Zhang
This paper investigates task-oriented communication for multi-device cooperative edge inference, where a group of distributed low-end edge devices transmit the extracted features o…
Communication-Computation Efficient Device-Edge Co-Inference via AutoML
Xinjie Zhang, Jiawei Shao, Yuyi Mao +1
Device-edge co-inference, which partitions a deep neural network between a resource-constrained mobile device and an edge server, recently emerges as a promising paradigm to suppor…
Joint Activity Detection, Channel Estimation, and Data Decoding for Grant-free Massive Random Access
Xinyu Bian, Yuyi Mao, Jun Zhang
In the massive machine-type communication (mMTC) scenario, a large number of devices with sporadic traffic need to access the network on limited radio resources. While grant-free r…
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data
Yuchang Sun, Jiawei Shao, Yuyi Mao +2
Federated edge learning (FEEL) has emerged as an effective approach to reduce the large communication latency in Cloud-based machine learning solutions, while preserving data priva…
Joint Activity Detection and Data Decoding in Massive Random Access via a Turbo Receiver
Xinyu Bian, Yuyi Mao, Jun Zhang
In this paper, we propose a turbo receiver for joint activity detection and data decoding in grant-free massive random access, which iterates between a detector and a belief propag…
Supporting More Active Users for Massive Access via Data-assisted Activity Detection
Xinyu Bian, Yuyi Mao, Jun Zhang
Massive machine-type communication (mMTC) has been regarded as one of the most important use scenarios in the fifth generation (5G) and beyond wireless networks, which demands scal…