Frame Structure Design and Analysis for Millimeter Wave Cellular Systems
December 17, 2015 Β· Declared Dead Β· π IEEE Transactions on Wireless Communications
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Sourjya Dutta, Marco Mezzavilla, Russell Ford, Menglei Zhang, Sundeep Rangan, Michele Zorzi
arXiv ID
1512.05691
Category
cs.NI: Networking & Internet
Citations
76
Venue
IEEE Transactions on Wireless Communications
Last Checked
5 months ago
Abstract
The millimeter-wave (mmWave) frequencies have attracted considerable attention for fifth generation (5G) cellular communication as they offer orders of magnitude greater bandwidth than current cellular systems. However, the medium access control (MAC) layer may need to be significantly redesigned to support the highly directional transmissions, ultra-low latencies and high peak rates expected in mmWave communication. To address these challenges, we present a novel mmWave MAC layer frame structure with a number of enhancements including flexible, highly granular transmission times, dynamic control signal locations, extended messaging and ability to efficiently multiplex directional control signals. Analytic formulae are derived for the utilization and control overhead as a function of control periodicity, number of users, traffic statistics, signal-to-noise ratio and antenna gains. Importantly, the analysis can incorporate various front-end MIMO capability assumptions -- a critical feature of mmWave. Under realistic system and traffic assumptions, the analysis reveals that the proposed flexible frame structure design offers significant benefits over designs with fixed frame structures similar to current 4G long-term evolution (LTE). It is also shown that fully digital beamforming architectures offer significantly lower overhead compared to analog and hybrid beamforming under equivalent power budgets.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Networking & Internet
R.I.P.
π»
Ghosted
π
π
The Cartographer
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
π
π
The Cartographer
A Survey of Indoor Localization Systems and Technologies
R.I.P.
π»
Ghosted
Survey of Important Issues in UAV Communication Networks
π
π
The Cartographer
Network Function Virtualization: State-of-the-art and Research Challenges
π
π
The Cartographer
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted