485 research outputs found
Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks
The vision of the upcoming 6G technologies, characterized by ultra-dense
network, low latency, and fast data rate is to support Pervasive AI (PAI) using
zero-touch solutions enabling self-X (e.g., self-configuration,
self-monitoring, and self-healing) services. However, the research on 6G is
still in its infancy, and only the first steps have been taken to conceptualize
its design, investigate its implementation, and plan for use cases. Toward this
end, academia and industry communities have gradually shifted from theoretical
studies of AI distribution to real-world deployment and standardization. Still,
designing an end-to-end framework that systematizes the AI distribution by
allowing easier access to the service using a third-party application assisted
by a zero-touch service provisioning has not been well explored. In this
context, we introduce a novel platform architecture to deploy a zero-touch
PAI-as-a-Service (PAIaaS) in 6G networks supported by a blockchain-based smart
system. This platform aims to standardize the pervasive AI at all levels of the
architecture and unify the interfaces in order to facilitate the service
deployment across application and infrastructure domains, relieve the users
worries about cost, security, and resource allocation, and at the same time,
respect the 6G stringent performance requirements. As a proof of concept, we
present a Federated Learning-as-a-service use case where we evaluate the
ability of our proposed system to self-optimize and self-adapt to the dynamics
of 6G networks in addition to minimizing the users' perceived costs.Comment: IEEE Communications Magazin
A Comprehensive Survey on Resource Allocation for CRAN in 5G and Beyond Networks
The diverse service requirements coming with the
advent of sophisticated applications as well as a large number
of connected devices demand for revolutionary changes in the
traditional distributed radio access network (RAN). To this end,
Cloud-RAN (CRAN) is considered as an important paradigm
to enhance the performance of the upcoming fifth generation
(5G) and beyond wireless networks in terms of capacity, latency,
and connectivity to a large number of devices. Out of several
potential enablers, efficient resource allocation can mitigate various
challenges related to user assignment, power allocation, and
spectrum management in a CRAN, and is the focus of this paper.
Herein, we provide a comprehensive review of resource allocation
schemes in a CRAN along with a detailed optimization taxonomy
on various aspects of resource allocation. More importantly,
we identity and discuss the key elements for efficient resource
allocation and management in CRAN, namely: user assignment,
remote radio heads (RRH) selection, throughput maximization,
spectrum management, network utility, and power allocation.
Furthermore, we present emerging use-cases including heterogeneous
CRAN, millimeter-wave CRAN, virtualized CRAN, Non-
Orthogonal Multiple Access (NoMA)-based CRAN and fullduplex
enabled CRAN to illustrate how their performance can
be enhanced by adopting CRAN technology. We then classify
and discuss objectives and constraints involved in CRAN-based
5G and beyond networks. Moreover, a detailed taxonomy of
optimization methods and solution approaches with different
objectives is presented and discussed. Finally, we conclude the
paper with several open research issues and future directions
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