Beyond the Ultra-Dense Barrier - Paradigm shifts on the road beyond 1000x wireless capacity
June 11, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Jens Zander
arXiv ID
1606.03600
Category
cs.NI: Networking & Internet
Citations
56
Venue
arXiv.org
Last Checked
5 months ago
Abstract
It has become increasingly clear that the current design paradigm for mobile broadband systems is not a scalable and economically feasible way to solve the expected future 'capacity crunch', in particular in indoor locations with large user densities. 'Moore's law', e.g. state-of-the art signal processing and advanced antenna techniques now being researched, as well as more millimeter wave spectrum indeed provide more capacity, but are not the answer to the 3-4 orders of magnitude more capacity at today's cost, that is needed.We argue that solving the engineering problem of providing high data rates alone is not sufficient. Instead we need to solve the techno-economic problem to find both business models and scalable technical solutions that provide extreme area capacity for a given cost and energy consumption. In this paper we will show that achieving very high capacities is indeed feasible in indoor environments.
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