26,946 research outputs found
Resource Allocation for Energy-Efficient 3-Way Relay Channels
Throughput and energy efficiency in 3-way relay channels are studied in this
paper. Unlike previous contributions, we consider a circular message exchange.
First, an outer bound and achievable sum rate expressions for different
relaying protocols are derived for 3-way relay channels. The sum capacity is
characterized for certain SNR regimes. Next, leveraging the derived achievable
sum rate expressions, cooperative and competitive maximization of the energy
efficiency are considered. For the cooperative case, both low-complexity and
globally optimal algorithms for joint power allocation at the users and at the
relay are designed so as to maximize the system global energy efficiency. For
the competitive case, a game theoretic approach is taken, and it is shown that
the best response dynamics is guaranteed to converge to a Nash equilibrium. A
power consumption model for mmWave board-to-board communications is developed,
and numerical results are provided to corroborate and provide insight on the
theoretical findings.Comment: Submitted to IEEE Transactions on Wireless Communication
Regularity dependence of the rate of convergence of the learning curve for Gaussian process regression
This paper deals with the speed of convergence of the learning curve in a
Gaussian process regression framework. The learning curve describes the average
generalization error of the Gaussian process used for the regression. More
specifically, it is defined in this paper as the integral of the mean squared
error over the input parameter space with respect to the probability measure of
the input parameters. The main result is the proof of a theorem giving the mean
squared error in function of the number of observations for a large class of
kernels and for any dimension when the number of observations is large. From
this result, we can deduce the asymptotic behavior of the generalization error.
The presented proof generalizes previous ones that were limited to more
specific kernels or to small dimensions (one or two). The result can be used to
build an optimal strategy for resources allocation. This strategy is applied
successfully to a nuclear safety problem
Spectral Efficiency of Multi-User Adaptive Cognitive Radio Networks
In this correspondence, the comprehensive problem of joint power, rate, and
subcarrier allocation have been investigated for enhancing the spectral
efficiency of multi-user orthogonal frequency-division multiple access (OFDMA)
cognitive radio (CR) networks subject to satisfying total average transmission
power and aggregate interference constraints. We propose novel optimal radio
resource allocation (RRA) algorithms under different scenarios with
deterministic and probabilistic interference violation limits based on a
perfect and imperfect availability of cross-link channel state information
(CSI). In particular, we propose a probabilistic approach to mitigate the total
imposed interference on the primary service under imperfect cross-link CSI. A
closed-form mathematical formulation of the cumulative density function (cdf)
for the received signal-to-interference-plus-noise ratio (SINR) is formulated
to evaluate the resultant average spectral efficiency (ASE). Dual decomposition
is utilized to obtain sub-optimal solutions for the non-convex optimization
problems. Through simulation results, we investigate the achievable performance
and the impact of parameters uncertainty on the overall system performance.
Furthermore, we present that the developed RRA algorithms can considerably
improve the cognitive performance whilst abide the imposed power constraints.
In particular, the performance under imperfect cross-link CSI knowledge for the
proposed `probabilistic case' is compared to the conventional scenarios to show
the potential gain in employing this scheme
Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission
By deploying machine-learning algorithms at the network edge, edge learning
can leverage the enormous real-time data generated by billions of mobile
devices to train AI models, which enable intelligent mobile applications. In
this emerging research area, one key direction is to efficiently utilize radio
resources for wireless data acquisition to minimize the latency of executing a
learning task at an edge server. Along this direction, we consider the specific
problem of retransmission decision in each communication round to ensure both
reliability and quantity of those training data for accelerating model
convergence. To solve the problem, a new retransmission protocol called
data-importance aware automatic-repeat-request (importance ARQ) is proposed.
Unlike the classic ARQ focusing merely on reliability, importance ARQ
selectively retransmits a data sample based on its uncertainty which helps
learning and can be measured using the model under training. Underpinning the
proposed protocol is a derived elegant communication-learning relation between
two corresponding metrics, i.e., signal-to-noise ratio (SNR) and data
uncertainty. This relation facilitates the design of a simple threshold based
policy for importance ARQ. The policy is first derived based on the classic
classifier model of support vector machine (SVM), where the uncertainty of a
data sample is measured by its distance to the decision boundary. The policy is
then extended to the more complex model of convolutional neural networks (CNN)
where data uncertainty is measured by entropy. Extensive experiments have been
conducted for both the SVM and CNN using real datasets with balanced and
imbalanced distributions. Experimental results demonstrate that importance ARQ
effectively copes with channel fading and noise in wireless data acquisition to
achieve faster model convergence than the conventional channel-aware ARQ.Comment: This is an updated version: 1) extension to general classifiers; 2)
consideration of imbalanced classification in the experiments. Submitted to
IEEE Journal for possible publicatio
Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation
We propose in this paper an exploratory analysis algorithm for functional
data. The method partitions a set of functions into clusters and represents
each cluster by a simple prototype (e.g., piecewise constant). The total number
of segments in the prototypes, , is chosen by the user and optimally
distributed among the clusters via two dynamic programming algorithms. The
practical relevance of the method is shown on two real world datasets
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