Tra Huong Thi Le 교수

Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning and still guarantee high learning performance. However, it is impractical that all users will sacrifice their resources to join the FL algorithm. This motivates us to study the incentive mechanism design for FL. In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users. The mobile users use their own data to train the local machine le

Federated learning is an prominent machine learning technique that model is trained distributively by using local data of mobile users, which can preserve the privacy of users and still guarantee high learning performance. In this paper, we deal with the problem of incentive mechanism design for motivating users to participate in training. In this paper, we employ the randomized auction framework for incentive mechanism design in which the base station is a seller and mobile users are buyers. Co

Inspired by the shared infrastructure of colocation data centers and the growth of Mobile Edge Computing (MEC), colocation MEC businesses have thrived to offer an economical and low latency solution for MEC based tenants. In colocation MEC, a colocation service provider leases spaces at the base stations (BSs) to tenants for housing their servers. Although the tenants fully manage their own servers, they still need to purchase bandwidth from the network provider to via bandwidth purchasing marke

In the commercial caching system, both Infrastructure Provider (InP), who owns the infrastructure and wireless network resource and Service Providers (SPs), who provide service to its users based on the virtual resource provided by the InP, are beneficial in leasing and renting the cache space. By partitioning the cache space at the BS into slices and leasing each partition to the SPs, the InP can receive a payment. Meanwhile, the SPs can serve their users with faster download service with local

Federated learning (FL) has emerged as a promising framework to exploit massive data generated by edge devices in developing a common learning model while preserving the privacy of local data. In implementing FL over wireless networks, the participation of more devices is encouraged to alleviate the training inefficiency due to irregular local data but it tends to increase communication latency. To solve this problem, we address non-orthogonal multiple access (NOMA) assisted by intelligent refle

This paper presents an accessing and allocation scheme for a Global Pilot and Download Channel (GPDCH) on which all broadcast, signalling messages and downloading of re-configuration data are conducted between the base station and mobile software terminals. The performance of this proposed scheme is investigated by means of computer simulation to estimate the delay in the registration and downloading processes when a software terminal reconfigures itself over-the-air to a new air interface stand

In this paper, we consider a cloud radio access network-based system consisting of one network operator (NO) and several content providers (CPs). The NO owns a cloud cache and provides caching as a service for CPs, who provide contents to users. While the NO wishes to motivate CPs to rent its cache and maximize its profit, CPs want to optimize the service performance for users and their renting utilities. Due to the time separation between cache allocation and user association problems, we model

Vehicular Mobile Edge Computing is a promising technology to leverage the bottleneck at a base station (BS) at peak hours. However, to deploy Vehicular Mobile Edge Computing requires to deal with the challenges in how to incentive vehicles to resource sharing and how to assign tasks and computation resource to minimize the total network delay. In this paper, we develop a two-stage incentive mechanism and task assignment and resource allocation scheme by combining auction game, matching theory, a

Federated Learning (FL) is a distributed learning framework that can deal\nwith the distributed issue in machine learning and still guarantee high\nlearning performance. However, it is impractical that all users will sacrifice\ntheir resources to join the FL algorithm. This motivates us to study the\nincentive mechanism design for FL. In this paper, we consider a FL system that\ninvolves one base station (BS) and multiple mobile users. The mobile users use\ntheir own data to train the local mach

Recently, wireless virtualization has attracted more and more attentions from research communities. With virtualization, resource utilization is higher, system performance is improved and the investment capital is lower. However, there are remaining challenges to be addressed before wireless virtualization is widespread deployed. One challenge is user association which has great influence on the performance of the wireless virtualization network. Traditionally, user associates to the base statio

Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning and still guarantee high learning performance. However, it is impractical that all users will sacrifice their resources to join the FL algorithm. This motivates us to study the incentive mechanism design for FL. In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users. The mobile users use their own data to train the local machine le

One of the primary objectives for future wireless communication networks is to facilitate the provision of ultra-reliable and low-latency communication services while simultaneously ensuring the capability for vast connection. In order to achieve this objective, we examine a hybrid multi-access scheme inside the finite blocklength (FBL) regime. This system combines the benefits of non-orthogonal multiple access (NOMA) and time-division multiple access (TDMA) schemes with the aim of fulfilling th

The simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has recently emerged as a cutting-edge technology for future wireless networks. In this paper, we explore an uplink STAR-RIS-enabled ultra-reliable and low-latency communications (URLLC) system utilizing hybrid non-orthogonal multiple access (NOMA). To enhance system performance, we pair one transmitting user with one reflecting user as a NOMA pair. Using a time division multiple access (TDMA) protocol amo