Jinke Ren - Academia.edu (original) (raw)

Papers by Jinke Ren

American journal of translational research, 2019

Distal tibiofibular syndesmosis injury (DTS) occurs frequently with ankle sprains. Current treatm... more Distal tibiofibular syndesmosis injury (DTS) occurs frequently with ankle sprains. Current treatments pose several limitations including causing soft tissue irritation, bringing damage to fixation secondary to weight-bearing, and requiring follow-up surgeries. Here, we investigated the clinical effects of a new technique, titanium cable isotonic annular fixation, for the treatment of DTS injury. From January 2015 to June 2017, 36 patients with ankle fractures and DTS injuries had their fractures repaired with the titanium cable isotonic annular fixation system. Recovery was scored by the AOFAS ankle function score system. We also assessed the differences in ankle motion between healthy and operative joints, and recorded the complications. All patients recovered from surgery without any serious complications. We followed all the cases for 18-25 months with an average follow-up of 21.26±3.23 months. 12 months after the operation, X-ray images showed that the titanium cables were fixed...

2020 54th Asilomar Conference on Signals, Systems, and Computers

This paper proposes a novel scheduling policy for federated edge learning, which exploits both di... more This paper proposes a novel scheduling policy for federated edge learning, which exploits both diversity in multiuser channels and diversity in the "importance" of the edge devices’ learning updates. A probabilistic scheduling framework is first developed to yield unbiased update aggregation in federated edge learning. The importance of a local learning update is measured by its gradient divergence. Considering the tradeoff between channel quality and update importance, the optimal scheduling policy is developed in closed form. The convergence analysis is also provided. Numerical results demonstrate the effectiveness of the proposed scheduling policy as compared with some benchmark policies.

ArXiv, 2021

Generative adversarial networks (GANs) are emerging machine learning models for generating synthe... more Generative adversarial networks (GANs) are emerging machine learning models for generating synthesized data similar to real data by jointly training a generator and a discriminator. In many applications, data and computational resources are distributed over many devices, so centralized computation with all data in one location is infeasible due to privacy and/or communication constraints. This paper proposes a new framework for training GANs in a distributed fashion: Each device computes a local discriminator using local data; a single server aggregates their results and computes a global GAN. Specifically, in each iteration, the server sends the global GAN to the devices, which then update their local discriminators; the devices send their results to the server, which then computes their average as the global discriminator and updates the global generator accordingly. Two different update schedules are designed with different levels of parallelism between the devices and the server...

2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)

IEEE Journal on Selected Areas in Communications

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

2019 IEEE Wireless Communications and Networking Conference (WCNC)

IEEE Transactions on Wireless Communications

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

IEEE Transactions on Wireless Communications

IEEE Transactions on Vehicular Technology

IEEE Transactions on Wireless Communications

IEEE Communications Letters

IEEE Transactions on Wireless Communications

By offloading intensive computation tasks to the edge cloud located at the cellular base stations... more By offloading intensive computation tasks to the edge cloud located at the cellular base stations, mobile-edge computation offloading (MECO) has been regarded as a promising means to accomplish the ambitious millisecond-scale end-to-end latency requirement of the fifth-generation networks. In this paper, we investigate the latency-minimization problem in a multiuser time-division multiple access MECO system with joint communication and computation resource allocation. Three different computation models are studied, i.e., local compression, edge cloud compression, and partial compression offloading. First, closed-form expressions of optimal resource allocation and minimum system delay for both local and edge cloud compression models are derived. Then, for the partial compression offloading model, we formulate a piecewise optimization problem and prove that the optimal data segmentation strategy has a piecewise structure. Based on this result, an optimal joint communication and computation resource allocation algorithm is developed. To gain more insights, we also analyze a specific scenario where communication resource is adequate while computation resource is limited. In this special case, the closed-form solution of the piecewise optimization problem can be derived. Our proposed algorithms are finally verified by numerical results, which show that the novel partial compression offloading model can significantly reduce the end-to-end latency. Index Terms Mobile edge computation offloading (MECO), local compression, edge cloud compression, partial compression offloading, resource allocation, piecewise optimization, data segmentation strategy. I. INTRODUCTION Over the past few years, the explosive popularity of mobile devices, such as smart-phones, tablets, and wearable devices, has been accelerating the development of the Internet of Things

IEEE Network

In order to mitigate the long processing delay and high energy consumption of mobile augmented re... more In order to mitigate the long processing delay and high energy consumption of mobile augmented reality (AR) applications, mobile edge computing (MEC) has been recently proposed and is envisioned as a promising means to deliver better quality of experience (QoE) for AR consumers. In this article, we first present a comprehensive AR overview, including the indispensable components of general AR applications, fashionable AR devices, and several existing techniques for overcoming the thorny latency and energy consumption problems. Then, we propose a novel hierarchical computation architecture by inserting an edge layer between the conventional user layer and cloud layer. Based on the proposed architecture, we further develop an innovated operation mechanism to improve the performance of mobile AR applications. Three key technologies are also discussed to further assist the proposed AR architecture. Simulation results are finally provided to verify that our proposals can significantly improve the latency and energy performance as compared against existing baseline schemes.

American journal of translational research, 2019

Distal tibiofibular syndesmosis injury (DTS) occurs frequently with ankle sprains. Current treatm... more Distal tibiofibular syndesmosis injury (DTS) occurs frequently with ankle sprains. Current treatments pose several limitations including causing soft tissue irritation, bringing damage to fixation secondary to weight-bearing, and requiring follow-up surgeries. Here, we investigated the clinical effects of a new technique, titanium cable isotonic annular fixation, for the treatment of DTS injury. From January 2015 to June 2017, 36 patients with ankle fractures and DTS injuries had their fractures repaired with the titanium cable isotonic annular fixation system. Recovery was scored by the AOFAS ankle function score system. We also assessed the differences in ankle motion between healthy and operative joints, and recorded the complications. All patients recovered from surgery without any serious complications. We followed all the cases for 18-25 months with an average follow-up of 21.26±3.23 months. 12 months after the operation, X-ray images showed that the titanium cables were fixed...

2020 54th Asilomar Conference on Signals, Systems, and Computers

This paper proposes a novel scheduling policy for federated edge learning, which exploits both di... more This paper proposes a novel scheduling policy for federated edge learning, which exploits both diversity in multiuser channels and diversity in the "importance" of the edge devices’ learning updates. A probabilistic scheduling framework is first developed to yield unbiased update aggregation in federated edge learning. The importance of a local learning update is measured by its gradient divergence. Considering the tradeoff between channel quality and update importance, the optimal scheduling policy is developed in closed form. The convergence analysis is also provided. Numerical results demonstrate the effectiveness of the proposed scheduling policy as compared with some benchmark policies.

ArXiv, 2021

Generative adversarial networks (GANs) are emerging machine learning models for generating synthe... more Generative adversarial networks (GANs) are emerging machine learning models for generating synthesized data similar to real data by jointly training a generator and a discriminator. In many applications, data and computational resources are distributed over many devices, so centralized computation with all data in one location is infeasible due to privacy and/or communication constraints. This paper proposes a new framework for training GANs in a distributed fashion: Each device computes a local discriminator using local data; a single server aggregates their results and computes a global GAN. Specifically, in each iteration, the server sends the global GAN to the devices, which then update their local discriminators; the devices send their results to the server, which then computes their average as the global discriminator and updates the global generator accordingly. Two different update schedules are designed with different levels of parallelism between the devices and the server...

2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)

IEEE Journal on Selected Areas in Communications

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

2019 IEEE Wireless Communications and Networking Conference (WCNC)

IEEE Transactions on Wireless Communications

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

IEEE Transactions on Wireless Communications

IEEE Transactions on Vehicular Technology

IEEE Transactions on Wireless Communications

IEEE Communications Letters

IEEE Transactions on Wireless Communications

By offloading intensive computation tasks to the edge cloud located at the cellular base stations... more By offloading intensive computation tasks to the edge cloud located at the cellular base stations, mobile-edge computation offloading (MECO) has been regarded as a promising means to accomplish the ambitious millisecond-scale end-to-end latency requirement of the fifth-generation networks. In this paper, we investigate the latency-minimization problem in a multiuser time-division multiple access MECO system with joint communication and computation resource allocation. Three different computation models are studied, i.e., local compression, edge cloud compression, and partial compression offloading. First, closed-form expressions of optimal resource allocation and minimum system delay for both local and edge cloud compression models are derived. Then, for the partial compression offloading model, we formulate a piecewise optimization problem and prove that the optimal data segmentation strategy has a piecewise structure. Based on this result, an optimal joint communication and computation resource allocation algorithm is developed. To gain more insights, we also analyze a specific scenario where communication resource is adequate while computation resource is limited. In this special case, the closed-form solution of the piecewise optimization problem can be derived. Our proposed algorithms are finally verified by numerical results, which show that the novel partial compression offloading model can significantly reduce the end-to-end latency. Index Terms Mobile edge computation offloading (MECO), local compression, edge cloud compression, partial compression offloading, resource allocation, piecewise optimization, data segmentation strategy. I. INTRODUCTION Over the past few years, the explosive popularity of mobile devices, such as smart-phones, tablets, and wearable devices, has been accelerating the development of the Internet of Things

IEEE Network

In order to mitigate the long processing delay and high energy consumption of mobile augmented re... more In order to mitigate the long processing delay and high energy consumption of mobile augmented reality (AR) applications, mobile edge computing (MEC) has been recently proposed and is envisioned as a promising means to deliver better quality of experience (QoE) for AR consumers. In this article, we first present a comprehensive AR overview, including the indispensable components of general AR applications, fashionable AR devices, and several existing techniques for overcoming the thorny latency and energy consumption problems. Then, we propose a novel hierarchical computation architecture by inserting an edge layer between the conventional user layer and cloud layer. Based on the proposed architecture, we further develop an innovated operation mechanism to improve the performance of mobile AR applications. Three key technologies are also discussed to further assist the proposed AR architecture. Simulation results are finally provided to verify that our proposals can significantly improve the latency and energy performance as compared against existing baseline schemes.