5 research outputs found
True-data Testbed for 5G/B5G Intelligent Network
Future beyond fifth-generation (B5G) and sixth-generation (6G) mobile
communications will shift from facilitating interpersonal communications to
supporting Internet of Everything (IoE), where intelligent communications with
full integration of big data and artificial intelligence (AI) will play an
important role in improving network efficiency and providing high-quality
service. As a rapid evolving paradigm, the AI-empowered mobile communications
demand large amounts of data acquired from real network environment for
systematic test and verification. Hence, we build the world's first true-data
testbed for 5G/B5G intelligent network (TTIN), which comprises 5G/B5G on-site
experimental networks, data acquisition & data warehouse, and AI engine &
network optimization. In the TTIN, true network data acquisition, storage,
standardization, and analysis are available, which enable system-level online
verification of B5G/6G-orientated key technologies and support data-driven
network optimization through the closed-loop control mechanism. This paper
elaborates on the system architecture and module design of TTIN. Detailed
technical specifications and some of the established use cases are also
showcased.Comment: 12 pages, 10 figure
Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks
In this paper, the problem of minimizing energy and time consumption for task
computation and transmission is studied in a mobile edge computing
(MEC)-enabled balloon network. In the considered network, each user needs to
process a computational task in each time instant, where high-altitude balloons
(HABs), acting as flying wireless base stations, can use their powerful
computational abilities to process the tasks offloaded from their associated
users. Since the data size of each user's computational task varies over time,
the HABs must dynamically adjust the user association, service sequence, and
task partition scheme to meet the users' needs. This problem is posed as an
optimization problem whose goal is to minimize the energy and time consumption
for task computing and transmission by adjusting the user association, service
sequence, and task allocation scheme. To solve this problem, a support vector
machine (SVM)-based federated learning (FL) algorithm is proposed to determine
the user association proactively. The proposed SVM-based FL method enables each
HAB to cooperatively build an SVM model that can determine all user
associations without any transmissions of either user historical associations
or computational tasks to other HABs. Given the prediction of the optimal user
association, the service sequence and task allocation of each user can be
optimized so as to minimize the weighted sum of the energy and time
consumption. Simulations with real data of city cellular traffic from the
OMNILab at Shanghai Jiao Tong University show that the proposed algorithm can
reduce the weighted sum of the energy and time consumption of all users by up
to 16.1% compared to a conventional centralized method
Survey on Congestion Detection and Control in Connected Vehicles
The dynamic nature of vehicular ad hoc network (VANET) induced by frequent
topology changes and node mobility, imposes critical challenges for vehicular
communications. Aggravated by the high volume of information dissemination
among vehicles over limited bandwidth, the topological dynamics of VANET causes
congestion in the communication channel, which is the primary cause of problems
such as message drop, delay, and degraded quality of service. To mitigate these
problems, congestion detection, and control techniques are needed to be
incorporated in a vehicular network. Congestion control approaches can be
either open-loop or closed loop based on pre-congestion or post congestion
strategies. We present a general architecture of vehicular communication in
urban and highway environment as well as a state-of-the-art survey of recent
congestion detection and control techniques. We also identify the drawbacks of
existing approaches and classify them according to different hierarchical
schemes. Through an extensive literature review, we recommend solution
approaches and future directions for handling congestion in vehicular
communications