16,531 research outputs found
The journey from 5G towards 6G
This paper gives an overview of the journey from 5G towards 6G evolution. The 5G has been built across three main application verticals as defined by ITU, namely: Enhanced Mobile Broadband, Massive Machine Type Communications and Ultra-reliable Low Latency Communications (URRLC). To support these verticals, 5G has defined the following enablers: Massive MIMO, cloudification of network infrastructure, network automation, network slicing and edge cloud computing. It is expected that 5G will provide flexibility in terms of openness, mobility, programmability and agility and robustness in a standardized manner. The journey towards 6G will describe the limitations of 5G technologies and outlines the technology enablers for 6G. These enablers include smooth integration and interworking of Non-Terrestrial Networking technologies (NTN), use of Reconfigurable Intelligent Surfaces (RIS) and use of AI to orchestrate network and cloud resources. Additionally, the paper will give an overview of 6G research initiatives at both regional and international level
Architecture landscape
The network architecture evolution journey will carry on in the years ahead, driving a large scale adoption of 5th Generation (5G) and 5G-Advanced use cases with significantly decreased deployment and operational costs, and enabling new and innovative use-case-driven solutions towards 6th Generation (6G) with higher economic and societal values. The goal of this chapter, thus, is to present the envisioned societal impact, use cases and the End-to-End (E2E) 6G architecture. The E2E 6G architecture includes summarization of the various technical enablers as well as the system and functional views of the architecture
Millimeter-wave communication for a last-mile autonomous transport vehicle
Low-speed autonomous transport of passengers and goods is expected to have a strong, positive impact on the reliability and ease of travelling. Various advanced functions of the involved vehicles rely on the wireless exchange of information with other vehicles and the roadside infrastructure, thereby benefitting from the low latency and high throughput characteristics that 5G technology has to offer. This work presents an investigation of 5G millimeter-wave communication links for a low-speed autonomous vehicle, focusing on the effects of the antenna positions on both the received signal quality and the link performance. It is observed that the excess loss for communication with roadside infrastructure in front of the vehicle is nearly half-power beam width independent, and the increase of the root mean square delay spread plays a minor role in the resulting signal quality, as the absolute times are considerably shorter than the typical duration of 5G New Radio symbols. Near certain threshold levels, a reduction of the received power affects the link performance through an increased error vector magnitude of the received signal, and subsequent decrease of the achieved data throughput
An Overview on Evolution of Mobile Wireless Communication Networks: 1G-6G
There has been a huge advancement in mobile wireless communication since the last few decades. This advancement consist of several generations and is still going on. The journey of mobile wireless communication began with 1G followed by 2G,3G,4G,and under research future generations 5G,6G,7G. In this paper an attempt has been made to provide an overview of evolution of mobile generations by comparing the challenges and features that have evolved from each generation and explaining how improvements have been made from earlier generation to the next one.
DOI: 10.17762/ijritcc2321-8169.150513
Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks
The fifth generation of cellular networks (5G) will rely on edge cloud
deployments to satisfy the ultra-low latency demand of future applications. In
this paper, we argue that such deployments can also be used to enable advanced
data-driven and Machine Learning (ML) applications in mobile networks. We
propose an edge-controller-based architecture for cellular networks and
evaluate its performance with real data from hundreds of base stations of a
major U.S. operator. In this regard, we will provide insights on how to
dynamically cluster and associate base stations and controllers, according to
the global mobility patterns of the users. Then, we will describe how the
controllers can be used to run ML algorithms to predict the number of users in
each base station, and a use case in which these predictions are exploited by a
higher-layer application to route vehicular traffic according to network Key
Performance Indicators (KPIs). We show that the prediction accuracy improves
when based on machine learning algorithms that rely on the controllers' view
and, consequently, on the spatial correlation introduced by the user mobility,
with respect to when the prediction is based only on the local data of each
single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin
Please Lower Small Cell Antenna Heights in 5G
In this paper, we present a new and significant theoretical discovery. If the
absolute height difference between base station (BS) antenna and user equipment
(UE) antenna is larger than zero, then the network capacity performance in
terms of the area spectral efficiency (ASE) will continuously decrease as the
BS density increases for ultra-dense (UD) small cell networks (SCNs). This
performance behavior has a tremendous impact on the deployment of UD SCNs in
the 5th-generation (5G) era. Network operators may invest large amounts of
money in deploying more network infrastructure to only obtain an even worse
network performance. Our study results reveal that it is a must to lower the
SCN BS antenna height to the UE antenna height to fully achieve the capacity
gains of UD SCNs in 5G. However, this requires a revolutionized approach of BS
architecture and deployment, which is explored in this paper too.Comment: Final version in IEEE: http://ieeexplore.ieee.org/document/7842150/.
arXiv admin note: substantial text overlap with arXiv:1608.0669
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