76 research outputs found
Centralized and Distributed Sparsification for Low-Complexity Message Passing Algorithm in C-RAN Architectures
Cloud radio access network (C-RAN) is a promising technology for
fifth-generation (5G) cellular systems. However the burden imposed by the huge
amount of data to be collected (in the uplink) from the radio remote heads
(RRHs) and processed at the base band unit (BBU) poses serious challenges. In
order to reduce the computation effort of minimum mean square error (MMSE)
receiver at the BBU the Gaussian message passing (MP) together with a suitable
sparsification of the channel matrix can be used. In this paper we propose two
sets of solutions, either centralized or distributed ones. In the centralized
solutions, we propose different approaches to sparsify the channel matrix, in
order to reduce the complexity of MP. However these approaches still require
that all signals reaching the RRH are conveyed to the BBU, therefore the
communication requirements among the backbone network devices are unaltered. In
the decentralized solutions instead we aim at reducing both the complexity of
MP at the BBU and the requirements on the RRHs-BBU communication links by
pre-processing the signals at the RRH and convey a reduced set of signals to
the BBU.Comment: Accepted for pubblication in IEEE VTC 201
Asymptotics of Nonlinear LSE Precoders with Applications to Transmit Antenna Selection
This paper studies the large-system performance of Least Square Error (LSE)
precoders which~minimize~the~input-output distortion over an arbitrary support
subject to a general penalty function. The asymptotics are determined via the
replica method in a general form which encloses the Replica Symmetric (RS) and
Replica Symmetry Breaking (RSB) ans\"atze. As a result, the "marginal
decoupling property" of LSE precoders for -steps of RSB is derived. The
generality of the studied setup enables us to address special cases in which
the number of active transmit antennas are constrained. Our numerical
investigations depict that the computationally efficient forms of LSE precoders
based on "-norm" minimization perform close to the cases with
"zero-norm" penalty function which have a considerable improvements compared to
the random antenna selection. For the case with BPSK signals and restricted
number of active antennas, the results show that RS fails to predict the
performance while the RSB ansatz is consistent with theoretical bounds.Comment: 5 pages; 2 figures; to be presented at ISIT 201
Asymptotics of Transmit Antenna Selection: Impact of Multiple Receive Antennas
Consider a fading Gaussian MIMO channel with transmit and
receive antennas. The transmitter selects
antennas corresponding to the strongest channels. For this setup, we study the
distribution of the input-output mutual information when grows
large. We show that, for any and , the
distribution of the input-output mutual information is accurately approximated
by a Gaussian distribution whose mean grows large and whose variance converges
to zero. Our analysis depicts that, in the large limit, the gap between the
expectation of the mutual information and its corresponding upper bound,
derived by applying Jensen's inequality, converges to a constant which only
depends on and . The result extends the scope of
channel hardening to the general case of antenna selection with multiple
receive and selected transmit antennas. Although the analyses are given for the
large-system limit, our numerical investigations indicate the robustness of the
approximated distribution even when the number of antennas is not large.Comment: 6 pages, 4 figures, ICC 201
Employing Antenna Selection to Improve Energy-Efficiency in Massive MIMO Systems
Massive MIMO systems promise high data rates by employing large number of
antennas, which also increases the power usage of the system as a consequence.
This creates an optimization problem which specifies how many antennas the
system should employ in order to operate with maximal energy efficiency. Our
main goal is to consider a base station with a fixed number of antennas, such
that the system can operate with a smaller subset of antennas according to the
number of active user terminals, which may vary over time. Thus, in this paper
we propose an antenna selection algorithm which selects the best antennas
according to the better channel conditions with respect to the users, aiming at
improving the overall energy efficiency. Then, due to the complexity of the
mathematical formulation, a tight approximation for the consumed power is
presented, using the Wishart theorem, and it is used to find a deterministic
formulation for the energy efficiency. Simulation results show that the
approximation is quite tight and that there is significant improvement in terms
of energy efficiency when antenna selection is employed.Comment: To appear in Transactions on Emerging Telecommunications
Technologies, 12 pages, 8 figures, 2 table
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