12 research outputs found
Combined Sparse Regularization for Nonlinear Adaptive Filters
Nonlinear adaptive filters often show some sparse behavior due to the fact
that not all the coefficients are equally useful for the modeling of any
nonlinearity. Recently, a class of proportionate algorithms has been proposed
for nonlinear filters to leverage sparsity of their coefficients. However, the
choice of the norm penalty of the cost function may be not always appropriate
depending on the problem. In this paper, we introduce an adaptive combined
scheme based on a block-based approach involving two nonlinear filters with
different regularization that allows to achieve always superior performance
than individual rules. The proposed method is assessed in nonlinear system
identification problems, showing its effectiveness in taking advantage of the
online combined regularization.Comment: This is a corrected version of the paper presented at EUSIPCO 2018
and published on IEEE https://ieeexplore.ieee.org/document/855295
Group Sparse Precoding for Cloud-RAN with Multiple User Antennas
Cloud radio access network (C-RAN) has become a promising network
architecture to support the massive data traffic in the next generation
cellular networks. In a C-RAN, a massive number of low-cost remote antenna
ports (RAPs) are connected to a single baseband unit (BBU) pool via high-speed
low-latency fronthaul links, which enables efficient resource allocation and
interference management. As the RAPs are geographically distributed, the group
sparse beamforming schemes attracts extensive studies, where a subset of RAPs
is assigned to be active and a high spectral efficiency can be achieved.
However, most studies assumes that each user is equipped with a single antenna.
How to design the group sparse precoder for the multiple antenna users remains
little understood, as it requires the joint optimization of the mutual coupling
transmit and receive beamformers. This paper formulates an optimal joint RAP
selection and precoding design problem in a C-RAN with multiple antennas at
each user. Specifically, we assume a fixed transmit power constraint for each
RAP, and investigate the optimal tradeoff between the sum rate and the number
of active RAPs. Motivated by the compressive sensing theory, this paper
formulates the group sparse precoding problem by inducing the -norm as
a penalty and then uses the reweighted heuristic to find a solution.
By adopting the idea of block diagonalization precoding, the problem can be
formulated as a convex optimization, and an efficient algorithm is proposed
based on its Lagrangian dual. Simulation results verify that our proposed
algorithm can achieve almost the same sum rate as that obtained from exhaustive
search
A Theoretical Performance Bound for Joint Beamformer Design of Wireless Fronthaul and Access Links in Downlink C-RAN
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 (C-RAN), which is a
candidate solution in 5G and future communication networks, 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 C-RAN with a wireless fronthaul and aim to minimize total power spent
by jointly designing beamformers for fronthaul and access links. We consider
the case where perfect channel state information is not available in the CP. We
first derive a novel theoretical performance bound for the problem defined.
Then we propose four algorithms with different complexities to show the
tightness of the bound. The first two algorithms apply successive convex
optimizations with semi-definite relaxation idea where other two are adapted
from well-known beamforming design methods. The detailed simulations under
realistic channel conditions show that as the complexity of the algorithm
increases, the corresponding performance becomes closer to the bound.Comment: 30 pages, single column, 11 figures, submitted to Transactions on
Wireless Communications in Oct. 20, 2020. Major Revision decision was made in
Jan. 16, 2021. After the revision, it will be resubmitted to the same journal
until the end of February, 202
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