20 research outputs found
Fog Radio Access Networks: Mobility management, interference mitigation and resource optimization
In order to make Internet connections ubiquitous and autonomous in our daily lives, maximizing the
utilization of radio resources and social information is one of the major research topics in future mobile
communication technologies. Fog radio access network (FRAN) is regarded as a promising paradigm
for the fifth generation (5G) of mobile networks. FRAN integrates fog computing with RAN and makes
full use of the edge of networks. FRAN would be different in networking, computing, storage and
control as compared with conventional radio access networks (RAN) and the emerging cloud RAN.
In this article, we provide a description of the FRAN architecture, and discuss how the distinctive
characteristics of FRAN make it possible to efficiently alleviate the burden on the fronthaul, backhaul
and backbone networks, as well as reduce content delivery latencies. We will focus on the mobility management, interference mitigation, and resource optimization in FRAN. Our simulation results show
that the proposed FRAN architecture and the associated mobility and resource management mechanisms
can reduce the signaling cost and increase the net utility for the RAN
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