16 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
Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computation
Federated learning (FL) as a promising edge-learning framework can
effectively address the latency and privacy issues by featuring distributed
learning at the devices and model aggregation in the central server. In order
to enable efficient wireless data aggregation, over-the-air computation
(AirComp) has recently been proposed and attracted immediate attention.
However, fading of wireless channels can produce aggregate distortions in an
AirComp-based FL scheme. To combat this effect, the concept of dynamic learning
rate (DLR) is proposed in this work. We begin our discussion by considering
multiple-input-single-output (MISO) scenario, since the underlying optimization
problem is convex and has closed-form solution. We then extend our studies to
more general multiple-input-multiple-output (MIMO) case and an iterative method
is derived. Extensive simulation results demonstrate the effectiveness of the
proposed scheme in reducing the aggregate distortion and guaranteeing the
testing accuracy using the MNIST and CIFAR10 datasets. In addition, we present
the asymptotic analysis and give a near-optimal receive beamforming design
solution in closed form, which is verified by numerical simulations
Link-level simulator for 5G localization
Channel-state-information-based localization in 5G networks has been a
promising way to obtain highly accurate positions compared to previous
communication networks. However, there is no unified and effective platform to
support the research on 5G localization algorithms. This paper releases a
link-level simulator for 5G localization, which can depict realistic physical
behaviors of the 5G positioning signal transmission. Specifically, we first
develop a simulation architecture considering more elaborate parameter
configuration and physical-layer processing. The architecture supports the link
modeling at sub-6GHz and millimeter-wave (mmWave) frequency bands.
Subsequently, the critical physical-layer components that determine the
localization performance are designed and integrated. In particular, a
lightweight new-radio channel model and hardware impairment functions that
significantly limit the parameter estimation accuracy are developed. Finally,
we present three application cases to evaluate the simulator, i.e.
two-dimensional mobile terminal localization, mmWave beam sweeping, and
beamforming-based angle estimation. The numerical results in the application
cases present the performance diversity of localization algorithms in various
impairment conditions
Toward 6G TK Extreme Connectivity: Architecture, Key Technologies and Experiments
Sixth-generation (6G) networks are evolving towards new features and
order-of-magnitude enhancement of systematic performance metrics compared to
the current 5G. In particular, the 6G networks are expected to achieve extreme
connectivity performance with Tbps-scale data rate, Kbps/Hz-scale spectral
efficiency, and s-scale latency. To this end, an original three-layer 6G
network architecture is designed to realise uniform full-spectrum cell-free
radio access and provide task-centric agile proximate support for diverse
applications. The designed architecture is featured by super edge node (SEN)
which integrates connectivity, computing, AI, data, etc. On this basis, a
technological framework of pervasive multi-level (PML) AI is established in the
centralised unit to enable task-centric near-real-time resource allocation and
network automation. We then introduce a radio access network (RAN) architecture
of full spectrum uniform cell-free networks, which is among the most attractive
RAN candidates for 6G TK extreme connectivity. A few most promising key
technologies, i.e., cell-free massive MIMO, photonics-assisted Terahertz
wireless access and spatiotemporal two-dimensional channel coding are further
discussed. A testbed is implemented and extensive trials are conducted to
evaluate innovative technologies and methodologies. The proposed 6G network
architecture and technological framework demonstrate exciting potentials for
full-service and full-scenario applications.Comment: 15 pages, 12 figure