11,598 research outputs found
Are Slepian-Wolf Rates Necessary for Distributed Parameter Estimation?
We consider a distributed parameter estimation problem, in which multiple
terminals send messages related to their local observations using limited rates
to a fusion center who will obtain an estimate of a parameter related to
observations of all terminals. It is well known that if the transmission rates
are in the Slepian-Wolf region, the fusion center can fully recover all
observations and hence can construct an estimator having the same performance
as that of the centralized case. One natural question is whether Slepian-Wolf
rates are necessary to achieve the same estimation performance as that of the
centralized case. In this paper, we show that the answer to this question is
negative. We establish our result by explicitly constructing an asymptotically
minimum variance unbiased estimator (MVUE) that has the same performance as
that of the optimal estimator in the centralized case while requiring
information rates less than the conditions required in the Slepian-Wolf rate
region.Comment: Accepted in Allerton 201
Energy-Efficient Cooperative Cognitive Relaying Schemes for Cognitive Radio Networks
We investigate a cognitive radio network in which a primary user (PU) may
cooperate with a cognitive radio user (i.e., a secondary user (SU)) for
transmissions of its data packets. The PU is assumed to be a buffered node
operating in a time-slotted fashion where the time is partitioned into
equal-length slots. We develop two schemes which involve cooperation between
primary and secondary users. To satisfy certain quality of service (QoS)
requirements, users share time slot duration and channel frequency bandwidth.
Moreover, the SU may leverage the primary feedback message to further increase
both its data rate and satisfy the PU QoS requirements. The proposed
cooperative schemes are designed such that the SU data rate is maximized under
the constraint that the PU average queueing delay is maintained less than the
average queueing delay in case of non-cooperative PU. In addition, the proposed
schemes guarantee the stability of the PU queue and maintain the average energy
emitted by the SU below a certain value. The proposed schemes also provide more
robust and potentially continuous service for SUs compared to the conventional
practice in cognitive networks where SUs transmit in the spectrum holes and
silence sessions of the PUs. We include primary source burstiness, sensing
errors, and feedback decoding errors to the analysis of our proposed
cooperative schemes. The optimization problems are solved offline and require a
simple 2-dimensional grid-based search over the optimization variables.
Numerical results show the beneficial gains of the cooperative schemes in terms
of SU data rate and PU throughput, average PU queueing delay, and average PU
energy savings
A methodology for assessing the effect of correlations among muscle synergy activations on task-discriminating information
Muscle synergies have been hypothesized to be the building blocks used by the central nervous system to generate movement. According to this hypothesis, the accomplishment of various motor tasks relies on the ability of the motor system to recruit a small set of synergies on a single-trial basis and combine them in a task-dependent manner. It is conceivable that this requires a fine tuning of the trial-to-trial relationships between the synergy activations. Here we develop an analytical methodology to address the nature and functional role of trial-to-trial correlations between synergy activations, which is designed to help to better understand how these correlations may contribute to generating appropriate motor behavior. The algorithm we propose first divides correlations between muscle synergies into types (noise correlations, quantifying the trial-to-trial covariations of synergy activations at fixed task, and signal correlations, quantifying the similarity of task tuning of the trial-averaged activation coefficients of different synergies), and then uses single-trial methods (task-decoding and information theory) to quantify their overall effect on the task-discriminating information carried by muscle synergy activations. We apply the method to both synchronous and time-varying synergies and exemplify it on electromyographic data recorded during performance of reaching movements in different directions. Our method reveals the robust presence of information-enhancing patterns of signal and noise correlations among pairs of synchronous synergies, and shows that they enhance by 9–15% (depending on the set of tasks) the task-discriminating information provided by the synergy decompositions. We suggest that the proposed methodology could be useful for assessing whether single-trial activations of one synergy depend on activations of other synergies and quantifying the effect of such dependences on the task-to-task differences in muscle activation patterns
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