77,381 research outputs found
Cell-Free Massive MIMO versus Small Cells
A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a
very large number of distributed access points (APs)which simultaneously serve
a much smaller number of users over the same time/frequency resources based on
directly measured channel characteristics. The APs and users have only one
antenna each. The APs acquire channel state information through time-division
duplex operation and the reception of uplink pilot signals transmitted by the
users. The APs perform multiplexing/de-multiplexing through conjugate
beamforming on the downlink and matched filtering on the uplink. Closed-form
expressions for individual user uplink and downlink throughputs lead to max-min
power control algorithms. Max-min power control ensures uniformly good service
throughout the area of coverage. A pilot assignment algorithm helps to mitigate
the effects of pilot contamination, but power control is far more important in
that regard.
Cell-Free Massive MIMO has considerably improved performance with respect to
a conventional small-cell scheme, whereby each user is served by a dedicated
AP, in terms of both 95%-likely per-user throughput and immunity to shadow
fading spatial correlation. Under uncorrelated shadow fading conditions, the
cell-free scheme provides nearly 5-fold improvement in 95%-likely per-user
throughput over the small-cell scheme, and 10-fold improvement when shadow
fading is correlated.Comment: EEE Transactions on Wireless Communications, accepted for publicatio
Governance-technology co-evolution and misalignment in the electricity industry
This paper explores some reasons why the alignment between governance and technology in infrastructures may be unstable or not easy to achieve. Focusing on the electricity industry, we claim that the decentralization of governance – an essential step towards a decentralized technical coordination - may be hampered by if deregulation magnifies behavioural uncertainties and asset specificities; and that in a technically decentralized system, political demand for centralized coordination may arise if the players are able to collude and lobby, and if such practices lead to higher electricity rates and lower efficiency. Our claims are supported by insights coming from approaches as diverse as transaction cost economics, the competence-based view of the firm, and political economy.Governance; Technology; Coherence; Competence; Transaction costs; Regulation.
Exact firing time statistics of neurons driven by discrete inhibitory noise
Neurons in the intact brain receive a continuous and irregular synaptic
bombardment from excitatory and inhibitory pre-synaptic neurons, which
determines the firing activity of the stimulated neuron. In order to
investigate the influence of inhibitory stimulation on the firing time
statistics, we consider Leaky Integrate-and-Fire neurons subject to inhibitory
instantaneous post-synaptic potentials. In particular, we report exact results
for the firing rate, the coefficient of variation and the spike train spectrum
for various synaptic weight distributions. Our results are not limited to
stimulations of infinitesimal amplitude, but they apply as well to finite
amplitude post-synaptic potentials, thus being able to capture the effect of
rare and large spikes. The developed methods are able to reproduce also the
average firing properties of heterogeneous neuronal populations.Comment: 20 pages, 8 Figures, submitted to Scientific Report
Modeling social information skills
In a modern economy, the most important resource consists in\ud
human talent: competent, knowledgeable people. Locating the right person for\ud
the task is often a prerequisite to complex problem-solving, and experienced\ud
professionals possess the social skills required to find appropriate human\ud
expertise. These skills can be reproduced more and more with specific\ud
computer software, an approach defining the new field of social information\ud
retrieval. We will analyze the social skills involved and show how to model\ud
them on computer. Current methods will be described, notably information\ud
retrieval techniques and social network theory. A generic architecture and its\ud
functions will be outlined and compared with recent work. We will try in this\ud
way to estimate the perspectives of this recent domain
A physiologically inspired model for solving the cocktail party problem.
At a cocktail party, we can broadly monitor the entire acoustic scene to detect important cues (e.g., our names being called, or the fire alarm going off), or selectively listen to a target sound source (e.g., a conversation partner). It has recently been observed that individual neurons in the avian field L (analog to the mammalian auditory cortex) can display broad spatial tuning to single targets and selective tuning to a target embedded in spatially distributed sound mixtures. Here, we describe a model inspired by these experimental observations and apply it to process mixtures of human speech sentences. This processing is realized in the neural spiking domain. It converts binaural acoustic inputs into cortical spike trains using a multi-stage model composed of a cochlear filter-bank, a midbrain spatial-localization network, and a cortical network. The output spike trains of the cortical network are then converted back into an acoustic waveform, using a stimulus reconstruction technique. The intelligibility of the reconstructed output is quantified using an objective measure of speech intelligibility. We apply the algorithm to single and multi-talker speech to demonstrate that the physiologically inspired algorithm is able to achieve intelligible reconstruction of an "attended" target sentence embedded in two other non-attended masker sentences. The algorithm is also robust to masker level and displays performance trends comparable to humans. The ideas from this work may help improve the performance of hearing assistive devices (e.g., hearing aids and cochlear implants), speech-recognition technology, and computational algorithms for processing natural scenes cluttered with spatially distributed acoustic objects.R01 DC000100 - NIDCD NIH HHSPublished versio
Using deep learning to understand and mitigate the qubit noise environment
Understanding the spectrum of noise acting on a qubit can yield valuable
information about its environment, and crucially underpins the optimization of
dynamical decoupling protocols that can mitigate such noise. However,
extracting accurate noise spectra from typical time-dynamics measurements on
qubits is intractable using standard methods. Here, we propose to address this
challenge using deep learning algorithms, leveraging the remarkable progress
made in the field of image recognition, natural language processing, and more
recently, structured data. We demonstrate a neural network based methodology
that allows for extraction of the noise spectrum associated with any qubit
surrounded by an arbitrary bath, with significantly greater accuracy than the
current methods of choice. The technique requires only a two-pulse echo decay
curve as input data and can further be extended either for constructing
customized optimal dynamical decoupling protocols or for obtaining critical
qubit attributes such as its proximity to the sample surface. Our results can
be applied to a wide range of qubit platforms, and provide a framework for
improving qubit performance with applications not only in quantum computing and
nanoscale sensing but also in material characterization techniques such as
magnetic resonance.Comment: Accepted for publication, 15 pages, 10 figure
- …