96 research outputs found
Green OFDMA Resource Allocation in Cache-Enabled CRAN
Cloud radio access network (CRAN), in which remote radio heads (RRHs) are
deployed to serve users in a target area, and connected to a central processor
(CP) via limited-capacity links termed the fronthaul, is a promising candidate
for the next-generation wireless communication systems. Due to the
content-centric nature of future wireless communications, it is desirable to
cache popular contents beforehand at the RRHs, to reduce the burden on the
fronthaul and achieve energy saving through cooperative transmission. This
motivates our study in this paper on the energy efficient transmission in an
orthogonal frequency division multiple access (OFDMA)-based CRAN with multiple
RRHs and users, where the RRHs can prefetch popular contents. We consider a
joint optimization of the user-SC assignment, RRH selection and transmit power
allocation over all the SCs to minimize the total transmit power of the RRHs,
subject to the RRHs' individual fronthaul capacity constraints and the users'
minimum rate constraints, while taking into account the caching status at the
RRHs. Although the problem is non-convex, we propose a Lagrange duality based
solution, which can be efficiently computed with good accuracy. We compare the
minimum transmit power required by the proposed algorithm with different
caching strategies against the case without caching by simulations, which show
the significant energy saving with caching.Comment: Presented in IEEE Online Conference on Green Communications (Online
GreenComm), Nov. 2016 (Invited Paper
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
Beamformer Design with Smooth Constraint-Free Approximation in Downlink Cloud Radio Access Networks
It is known that data rates in standard cellular networks are limited due to
inter-cell interference. An effective solution of this problem is to use the
multi-cell cooperation idea. In Cloud Radio Access Network, which is a
candidate solution in 5G and beyond, cooperation is applied by means of central
processors (CPs) connected to simple remote radio heads with finite capacity
fronthaul links. In this study, we consider a downlink scenario and aim to
minimize total power spent by designing beamformers. We consider the case where
perfect channel state information is not available in the CP. The original
problem includes discontinuous terms with many constraints. We propose a novel
method which transforms the problem into a smooth constraint-free form and a
solution is found by the gradient descent approach. As a comparison, we
consider the optimal method solving an extensive number of convex sub-problems,
a known heuristic search algorithm and some sparse solution techniques.
Heuristic search methods find a solution by solving a subset of all possible
convex sub-problems. Sparse techniques apply some norm approximation
() or convex approximation to make the objective
function more tractable. We also derive a theoretical performance bound in
order to observe how far the proposed method performs off the optimal method
when running the optimal method is prohibitive due to computational complexity.
Detailed simulations show that the performance of the proposed method is close
to the optimal one, and it outperforms other methods analyzed.Comment: 18 pages, 12 figures, submitted to IEEE Access in Feb. 03, 2021. It
is a revised version of the paper submitted to IEEE Access in Nov. 23, 2020.
Revisions were made according to the reviewer comment
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