11,781 research outputs found
Impact of Spatial Filtering on Distortion from Low-Noise Amplifiers in Massive MIMO Base Stations
In massive MIMO base stations, power consumption and cost of the low-noise
amplifiers (LNAs) can be substantial because of the many antennas. We
investigate the feasibility of inexpensive, power efficient LNAs, which
inherently are less linear. A polynomial model is used to characterize the
nonlinear LNAs and to derive the second-order statistics and spatial
correlation of the distortion. We show that, with spatial matched filtering
(maximum-ratio combining) at the receiver, some distortion terms combine
coherently, and that the SINR of the symbol estimates therefore is limited by
the linearity of the LNAs. Furthermore, it is studied how the power from a
blocker in the adjacent frequency band leaks into the main band and creates
distortion. The distortion term that scales cubically with the power received
from the blocker has a spatial correlation that can be filtered out by spatial
processing and only the coherent term that scales quadratically with the power
remains. When the blocker is in free-space line-of-sight and the LNAs are
identical, this quadratic term has the same spatial direction as the desired
signal, and hence cannot be removed by linear receiver processing
Specification and estimation of spatial econometric models : A discussion of alternative strategies for spatial economic modelling
The semantical insufficiency of (spatial) economic theories necessitates the making of additional assumptions â thereby introducing substantial specification uncertainty â in order to arrive at a fully specified econometric model. The traditional or current approach to econometric modelling treats specification uncertainty inadequately. This proposition is illustrated by two well-known examples from the spatial economic literature. Two alternative specification strategies for spatial economic modelling â designed to improve the current spatial econometric modelling approach â are proposed. One of these strategies is used for a specification analysis of agricultural output in Eire
How single neuron properties shape chaotic dynamics and signal transmission in random neural networks
While most models of randomly connected networks assume nodes with simple
dynamics, nodes in realistic highly connected networks, such as neurons in the
brain, exhibit intrinsic dynamics over multiple timescales. We analyze how the
dynamical properties of nodes (such as single neurons) and recurrent
connections interact to shape the effective dynamics in large randomly
connected networks. A novel dynamical mean-field theory for strongly connected
networks of multi-dimensional rate units shows that the power spectrum of the
network activity in the chaotic phase emerges from a nonlinear sharpening of
the frequency response function of single units. For the case of
two-dimensional rate units with strong adaptation, we find that the network
exhibits a state of "resonant chaos", characterized by robust, narrow-band
stochastic oscillations. The coherence of stochastic oscillations is maximal at
the onset of chaos and their correlation time scales with the adaptation
timescale of single units. Surprisingly, the resonance frequency can be
predicted from the properties of isolated units, even in the presence of
heterogeneity in the adaptation parameters. In the presence of these
internally-generated chaotic fluctuations, the transmission of weak,
low-frequency signals is strongly enhanced by adaptation, whereas signal
transmission is not influenced by adaptation in the non-chaotic regime. Our
theoretical framework can be applied to other mechanisms at the level of single
nodes, such as synaptic filtering, refractoriness or spike synchronization.
These results advance our understanding of the interaction between the dynamics
of single units and recurrent connectivity, which is a fundamental step toward
the description of biologically realistic network models in the brain, or, more
generally, networks of other physical or man-made complex dynamical units
Hydrodynamic Impacts of a Marine Renewable Energy Installation on the Benthic Boundary Layer in a Tidal Channel
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