65,814 research outputs found
On Communication through a Gaussian Channel with an MMSE Disturbance Constraint
This paper considers a Gaussian channel with one transmitter and two
receivers. The goal is to maximize the communication rate at the
intended/primary receiver subject to a disturbance constraint at the
unintended/secondary receiver. The disturbance is measured in terms of minimum
mean square error (MMSE) of the interference that the transmission to the
primary receiver inflicts on the secondary receiver.
The paper presents a new upper bound for the problem of maximizing the mutual
information subject to an MMSE constraint. The new bound holds for vector
inputs of any length and recovers a previously known limiting (when the length
of vector input tends to infinity) expression from the work of Bustin
The key technical novelty is a new upper bound on the MMSE.
This bound allows one to bound the MMSE for all signal-to-noise ratio (SNR)
values a certain SNR at which the MMSE is known (which
corresponds to the disturbance constraint). This bound complements the
`single-crossing point property' of the MMSE that upper bounds the MMSE for all
SNR values a certain value at which the MMSE value is known.
The MMSE upper bound provides a refined characterization of the
phase-transition phenomenon which manifests, in the limit as the length of the
vector input goes to infinity, as a discontinuity of the MMSE for the problem
at hand.
For vector inputs of size , a matching lower bound, to within an
additive gap of order (where
is the disturbance constraint), is shown by means of the mixed
inputs technique recently introduced by Dytso Comment: Submitted to IEEE Transactions on Information Theor
Adaptive and Iterative Multi-Branch MMSE Decision Feedback Detection Algorithms for MIMO Systems
In this work, decision feedback (DF) detection algorithms based on multiple
processing branches for multi-input multi-output (MIMO) spatial multiplexing
systems are proposed. The proposed detector employs multiple cancellation
branches with receive filters that are obtained from a common matrix inverse
and achieves a performance close to the maximum likelihood detector (MLD).
Constrained minimum mean-squared error (MMSE) receive filters designed with
constraints on the shape and magnitude of the feedback filters for the
multi-branch MMSE DF (MB-MMSE-DF) receivers are presented. An adaptive
implementation of the proposed MB-MMSE-DF detector is developed along with a
recursive least squares-type algorithm for estimating the parameters of the
receive filters when the channel is time-varying. A soft-output version of the
MB-MMSE-DF detector is also proposed as a component of an iterative detection
and decoding receiver structure. A computational complexity analysis shows that
the MB-MMSE-DF detector does not require a significant additional complexity
over the conventional MMSE-DF detector, whereas a diversity analysis discusses
the diversity order achieved by the MB-MMSE-DF detector. Simulation results
show that the MB-MMSE-DF detector achieves a performance superior to existing
suboptimal detectors and close to the MLD, while requiring significantly lower
complexity.Comment: 10 figures, 3 tables; IEEE Transactions on Wireless Communications,
201
Using the Oxford cognitive screen to detect cognitive impairment in stroke patients. A comparison with the Mini-Mental State Examination
Background: The Oxford Cognitive Screen (OCS) was recently developed with the aim of describing the cognitive de cits after stroke. The scale consists of 10 tasks encom- passing ve cognitive domains: attention and executive function, language, memory, number processing, and praxis. OCS was devised to be inclusive and un-confounded by aphasia and neglect. As such, it may have a greater potential to be informative on stroke cognitive de cits of widely used instruments, such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment, which were originally devised for demented patients.
Objective: The present study compared the OCS with the MMSE with regards to their ability to detect cognitive impairments post-stroke. We further aimed to examine perfor- mance on the OCS as a function of subtypes of cerebral infarction and clinical severity.
Methods: 325 rst stroke patients were consecutively enrolled in the study over a 9-month period. The OCS and MMSE, as well as the Bamford classi cation and NIHSS, were given according to standard procedures.
results: About a third of patients (35.3%) had a performance lower than the cutoff (<22) on the MMSE, whereas 91.6% were impaired in at least one OCS domain, indicating higher incidences of impairment for the OCS. More than 80% of patients showed an impairment in two or more cognitive domains of the OCS. Using the MMSE as a standard of clinical practice, the comparative sensitivity of OCS was 100%. Out of the 208 patients with normal MMSE performance 180 showed impaired performance in at least one domain of the OCS. The discrepancy between OCS and MMSE was particularly strong for patients with milder strokes. As for subtypes of cerebral infarction, fewer patients demonstrated widespread impairments in the OCS in the Posterior Circulation Infarcts category than in the other categories.
conclusion: Overall, the results showed a much higher incidence of cognitive impairment with the OCS than with the MMSE and demonstrated no false negatives for OCS vs MMSE. It is concluded that OCS is a sensitive screen tool for cognitive de cits after stroke. In particular, the OCS detects high incidences of stroke-specific cognitive impairments, not detected by the MMSE, demonstrating the importance of cognitive pro ling.Background: The Oxford Cognitive Screen (OCS) was recently developed with the aim of describing the cognitive deficits after stroke. The scale consists of 10 tasks encompassing five cognitive domains: attention and executive function, language, memory, number processing, and praxis. OCS was devised to be inclusive and un-confounded by aphasia and neglect. As such, it may have a greater potential to be informative on stroke cognitive deficits of widely used instruments, such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment, which were originally devised for demented patients. Objective: The present study compared the OCS with the MMSE with regards to their ability to detect cognitive impairments post-stroke. We further aimed to examine performance on the OCS as a function of subtypes of cerebral infarction and clinical severity. Methods: 325 first stroke patients were consecutively enrolled in the study over a 9-month period. The OCS and MMSE, as well as the Bamford classification and NIHSS, were given according to standard procedures. Results: About a third of patients (35.3%) had a performance lower than the cutoff(< 22) on the MMSE, whereas 91.6% were impaired in at least one OCS domain, indicating higher incidences of impairment for the OCS. More than 80% of patients showed an impairment in two or more cognitive domains of the OCS. Using the MMSE as a standard of clinical practice, the comparative sensitivity of OCS was 100%. Out of the 208 patients with normal MMSE performance 180 showed impaired performance in at least one domain of the OCS. The discrepancy between OCS and MMSE was particularly strong for patients with milder strokes. As for subtypes of cerebral infarction, fewer patients demonstrated widespread impairments in the OCS in the Posterior Circulation Infarcts category than in the other categories. Conclusion: Overall, the results showed a much higher incidence of cognitive impairment with the OCS than with the MMSE and demonstrated no false negatives for OCS vs MMSE. It is concluded that OCS is a sensitive screen tool for cognitive deficits after stroke. In particular, the OCS detects high incidences of stroke-specific cognitive impairments, not detected by the MMSE, demonstrating the importance of cognitive profiling. © 2018 Mancuso, Demeyere, Abbruzzese, Damora, Varalta, Pirrotta, Antonucci, Matano, Caputo, Caruso, Pontiggia, Coccia, Ciancarelli, Zoccolotti and The Italian OCS Grou
A genetic algorithm-assisted semi-adaptive MMSE multi-user detection for MC-CDMA mobile communication systems
In this work, a novel Minimum-Mean Squared-Error (MMSE) multi-user detector is proposed for MC-CDMA transmission systems working over mobile radio channels characterized by time-varying multipath fading. The proposed MUD algorithm is based on a Genetic Algorithm (GA)-assisted per-carrier MMSE criterion. The GA block works in two successive steps: a training-aided step aimed at computing the optimal receiver weights using a very short training sequence, and a decision-directed step aimed at dynamically updating the weights vector during a channel coherence period. Numerical results evidenced BER performances almost coincident with ones yielded by ideal MMSE-MUD based on the perfect knowledge of channel impulse response. The proposed GA-assisted MMSE-MUD clearly outperforms state-of-the-art adaptive MMSE receivers based on deterministic gradient algorithms, especially for high number of transmitting users
Privacy-Aware MMSE Estimation
We investigate the problem of the predictability of random variable under
a privacy constraint dictated by random variable , correlated with ,
where both predictability and privacy are assessed in terms of the minimum
mean-squared error (MMSE). Given that and are connected via a
binary-input symmetric-output (BISO) channel, we derive the \emph{optimal}
random mapping such that the MMSE of given is minimized while
the MMSE of given is greater than for a
given . We also consider the case where are continuous
and is restricted to be an additive noise channel.Comment: 9 pages, 3 figure
MMSE Optimal Algebraic Space-Time Codes
Design of Space-Time Block Codes (STBCs) for Maximum Likelihood (ML)
reception has been predominantly the main focus of researchers. However, the ML
decoding complexity of STBCs becomes prohibitive large as the number of
transmit and receive antennas increase. Hence it is natural to resort to a
suboptimal reception technique like linear Minimum Mean Squared Error (MMSE)
receiver. Barbarossa et al and Liu et al have independently derived necessary
and sufficient conditions for a full rate linear STBC to be MMSE optimal, i.e
achieve least Symbol Error Rate (SER). Motivated by this problem, certain
existing high rate STBC constructions from crossed product algebras are
identified to be MMSE optimal. Also, it is shown that a certain class of codes
from cyclic division algebras which are special cases of crossed product
algebras are MMSE optimal. Hence, these STBCs achieve least SER when MMSE
reception is employed and are fully diverse when ML reception is employed.Comment: 5 pages, 1 figure, journal version to appear in IEEE Transactions on
Wireless Communications. Conference version appeared in NCC 2007, IIT Kanpur,
Indi
Mutual Information and Minimum Mean-square Error in Gaussian Channels
This paper deals with arbitrarily distributed finite-power input signals
observed through an additive Gaussian noise channel. It shows a new formula
that connects the input-output mutual information and the minimum mean-square
error (MMSE) achievable by optimal estimation of the input given the output.
That is, the derivative of the mutual information (nats) with respect to the
signal-to-noise ratio (SNR) is equal to half the MMSE, regardless of the input
statistics. This relationship holds for both scalar and vector signals, as well
as for discrete-time and continuous-time noncausal MMSE estimation. This
fundamental information-theoretic result has an unexpected consequence in
continuous-time nonlinear estimation: For any input signal with finite power,
the causal filtering MMSE achieved at SNR is equal to the average value of the
noncausal smoothing MMSE achieved with a channel whose signal-to-noise ratio is
chosen uniformly distributed between 0 and SNR
- …