22,425 research outputs found
A Novel Antenna Selection Scheme for Spatially Correlated Massive MIMO Uplinks with Imperfect Channel Estimation
We propose a new antenna selection scheme for a massive MIMO system with a
single user terminal and a base station with a large number of antennas. We
consider a practical scenario where there is a realistic correlation among the
antennas and imperfect channel estimation at the receiver side. The proposed
scheme exploits the sparsity of the channel matrix for the effective selection
of a limited number of antennas. To this end, we compute a sparse channel
matrix by minimising the mean squared error. This optimisation problem is then
solved by the well-known orthogonal matching pursuit algorithm. Widely used
models for spatial correlation among the antennas and channel estimation errors
are considered in this work. Simulation results demonstrate that when the
impacts of spatial correlation and imperfect channel estimation introduced, the
proposed scheme in the paper can significantly reduce complexity of the
receiver, without degrading the system performance compared to the maximum
ratio combining.Comment: in Proc. IEEE 81st Vehicular Technology Conference (VTC), May 2015, 6
pages, 5 figure
MmWave Massive MIMO Based Wireless Backhaul for 5G Ultra-Dense Network
Ultra-dense network (UDN) has been considered as a promising candidate for
future 5G network to meet the explosive data demand. To realize UDN, a
reliable, Gigahertz bandwidth, and cost-effective backhaul connecting
ultra-dense small-cell base stations (BSs) and macro-cell BS is prerequisite.
Millimeter-wave (mmWave) can provide the potential Gbps traffic for wireless
backhaul. Moreover, mmWave can be easily integrated with massive MIMO for the
improved link reliability. In this article, we discuss the feasibility of
mmWave massive MIMO based wireless backhaul for 5G UDN, and the benefits and
challenges are also addressed. Especially, we propose a digitally-controlled
phase-shifter network (DPSN) based hybrid precoding/combining scheme for mmWave
massive MIMO, whereby the low-rank property of mmWave massive MIMO channel
matrix is leveraged to reduce the required cost and complexity of transceiver
with a negligible performance loss. One key feature of the proposed scheme is
that the macro-cell BS can simultaneously support multiple small-cell BSs with
multiple streams for each smallcell BS, which is essentially different from
conventional hybrid precoding/combining schemes typically limited to
single-user MIMO with multiple streams or multi-user MIMO with single stream
for each user. Based on the proposed scheme, we further explore the fundamental
issues of developing mmWave massive MIMO for wireless backhaul, and the
associated challenges, insight, and prospect to enable the mmWave massive MIMO
based wireless backhaul for 5G UDN are discussed.Comment: This paper has been accepted by IEEE Wireless Communications
Magazine. This paper is related to 5G, ultra-dense network (UDN), millimeter
waves (mmWave) fronthaul/backhaul, massive MIMO, sparsity/low-rank property
of mmWave massive MIMO channels, sparse channel estimation, compressive
sensing (CS), hybrid digital/analog precoding/combining, and hybrid
beamforming. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=730653
Functional kernel estimators of conditional extreme quantiles
We address the estimation of "extreme" conditional quantiles i.e. when their
order converges to one as the sample size increases. Conditions on the rate of
convergence of their order to one are provided to obtain asymptotically
Gaussian distributed kernel estimators. A Weissman-type estimator and kernel
estimators of the conditional tail-index are derived, permitting to estimate
extreme conditional quantiles of arbitrary order.Comment: arXiv admin note: text overlap with arXiv:1107.226
A class of quasi-sparse companion pencils
In this paper, we introduce a general class of quasi-sparse potential companion pencils for arbitrary square matrix polynomials over an arbitrary field, which extends the class introduced in [B. Eastman, I.-J. Kim, B. L. Shader, K.N. Vander Meulen, Companion matrix patterns. Linear Algebra Appl. 436 (2014) 255-272] for monic scalar polynomials. We provide a canonical form, up to permutation, for companion pencils in this class. We also relate these companion pencils with other relevant families of companion linearizations known so far. Finally, we determine the number of different sparse companion pencils in the class, up to permutation.This work has been partially supported by theMinisterio de EconomĂa y Competitividad of Spain through grants MTM2015-68805-REDT and MTM2015-65798-P
A set of exactly solvable Ising models with half-odd-integer spin
We present a set of exactly solvable Ising models, with half-odd-integer
spin-S on a square-type lattice including a quartic interaction term in the
Hamiltonian. The particular properties of the mixed lattice, associated with
mixed half-odd-integer spin-(S,1/2) and only nearest-neighbour interaction,
allow us to map this system either onto a purely spin-1/2 lattice or onto a
purely spin-S lattice. By imposing the condition that the mixed
half-odd-integer spin-(S,1/2) lattice must have an exact solution, we found a
set of exact solutions that satisfy the {\it free fermion} condition of the
eight vertex model. The number of solutions for a general half-odd-integer
spin-S is given by . Therefore we conclude that this transformation is
equivalent to a simple spin transformation which is independent of the
coordination number
NIRS potential use for the determination of natural resources quality from dehesa (acorn and grass) in Montanera system for Iberian pigs.
NIRS technology has been used as an alternative to conventional methods to determinate the content of nutrients of acorns and grass from dehesa ecosystem. Dry matter (DM), crude fat (CF), crude protein (CP), starch, total phenolic compounds (TP), α-tocopherol, γ-tocopherol, fatty acids, neutral detergent fiber (NDF), total antioxidant activity (TAA) and total energy (TE) were determined by conventional methods for later development of NIRS predictive equations. The NIR spectrum of each sample was collected and for all studied parameters, a predictive model was obtained and external validated. Good prediction equations were obtained for moisture, crude fat, crude protein, total energy and γ-tocopherol in acorns samples, with high coefficients of correlation (1-VR) and low standard error of prediction (SEP) (1-VR=0.81, SEP=2.62; 1-VR=0.92, SEP=0.54; 1-VR=0.86, SEP=0.47; 1-VR=0.84, SEP=0.2; 1-VR=0.88, SEP=5.4, respectively) and crude protein, NDF, α-tocopherol and linolenic acid content in grass samples (1-VR=0.9, SEP=1.99; 1-VR=0.87, SEP=4.13; 1-VR=0.76, SEP=10.9; 1-VR=0.82, SEP=0.6, respectively). Therefore, these prediction models could be used to determinate the nutritional composition of Montanera natural resources
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