60 research outputs found
Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges
As a promising paradigm for fifth generation (5G) wireless communication
systems, cloud radio access networks (C-RANs) have been shown to reduce both
capital and operating expenditures, as well as to provide high spectral
efficiency (SE) and energy efficiency (EE). The fronthaul in such networks,
defined as the transmission link between a baseband unit (BBU) and a remote
radio head (RRH), requires high capacity, but is often constrained. This
article comprehensively surveys recent advances in fronthaul-constrained
C-RANs, including system architectures and key techniques. In particular, key
techniques for alleviating the impact of constrained fronthaul on SE/EE and
quality of service for users, including compression and quantization,
large-scale coordinated processing and clustering, and resource allocation
optimization, are discussed. Open issues in terms of software-defined
networking, network function virtualization, and partial centralization are
also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin
note: text overlap with arXiv:1407.3855 by other author
Learning Optimal Fronthauling and Decentralized Edge Computation in Fog Radio Access Networks
Fog radio access networks (F-RANs), which consist of a cloud and multiple
edge nodes (ENs) connected via fronthaul links, have been regarded as promising
network architectures. The F-RAN entails a joint optimization of cloud and edge
computing as well as fronthaul interactions, which is challenging for
traditional optimization techniques. This paper proposes a Cloud-Enabled
Cooperation-Inspired Learning (CECIL) framework, a structural deep learning
mechanism for handling a generic F-RAN optimization problem. The proposed
solution mimics cloud-aided cooperative optimization policies by including
centralized computing at the cloud, distributed decision at the ENs, and their
uplink-downlink fronthaul interactions. A group of deep neural networks (DNNs)
are employed for characterizing computations of the cloud and ENs. The
forwardpass of the DNNs is carefully designed such that the impacts of the
practical fronthaul links, such as channel noise and signling overheads, can be
included in a training step. As a result, operations of the cloud and ENs can
be jointly trained in an end-to-end manner, whereas their real-time inferences
are carried out in a decentralized manner by means of the fronthaul
coordination. To facilitate fronthaul cooperation among multiple ENs, the
optimal fronthaul multiple access schemes are designed. Training algorithms
robust to practical fronthaul impairments are also presented. Numerical results
validate the effectiveness of the proposed approaches.Comment: to appear in IEEE Transactions on Wireless Communication
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
Non-Orthogonal Multiplexing of Ultra-Reliable and Broadband Services in Fog-Radio Architectures
The fifth generation (5G) of cellular systems is introducing Ultra-Reliable
Low-Latency Communications (URLLC) services alongside more conventional
enhanced Mobile BroadBand (eMBB) traffic. Furthermore, the 5G cellular
architecture is evolving from a base station-centric deployment to a fog-like
set-up that accommodates a flexible functional split between cloud and edge. In
this paper, a novel solution is proposed that enables the non-orthogonal
coexistence of URLLC and eMBB services by processing URLLC traffic at the Edge
Nodes (ENs), while eMBB communications are handled centrally at a cloud
processor as in a Cloud-Radio Access Network (C-RAN) system. This solution
guarantees the low-latency requirements of the URLLC service by means of edge
processing, e.g., for vehicle-to-cellular use cases, as well as the high
spectral efficiency for eMBB traffic via centralized baseband processing. Both
uplink and downlink are analyzed by accounting for the heterogeneous
performance requirements of eMBB and URLLC traffic and by considering practical
aspects such as fading, lack of channel state information for URLLC
transmitters, rate adaptation for eMBB transmitters, finite fronthaul capacity,
and different coexistence strategies, such as puncturing.Comment: Submitted as Journal Pape
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