224 research outputs found
Semi-blind sparse channel estimation with constant modulus symbols
We propose two methods for the estimation of sparse communication channels. In the first method, we consider the problem of channel estimation based on training symbols, and formulate it as an optimization problem. In this formulation, we combine the objective of fidelity to the received data with a non-quadratic constraint reflecting the prior information about the sparsity of the channel. This approach leads to accurate channel estimates with much shorter training sequences than conventional methods. The second method we propose is aimed at taking advantage of any available training-based data, as well as any "blind" data based on unknown, constant modulus symbols. We propose a semi-blind optimization framework making use of these two types of data, and enforcing the sparsity of the channel, as well as the constant modulus property of the symbols. This approach improves upon the channel estimates based only on training sequences, and also produces accurate estimates for the unknown symbols
Testing the Structure of a Gaussian Graphical Model with Reduced Transmissions in a Distributed Setting
Testing a covariance matrix following a Gaussian graphical model (GGM) is
considered in this paper based on observations made at a set of distributed
sensors grouped into clusters. Ordered transmissions are proposed to achieve
the same Bayes risk as the optimum centralized energy unconstrained approach
but with fewer transmissions and a completely distributed approach. In this
approach, we represent the Bayes optimum test statistic as a sum of local test
statistics which can be calculated by only utilizing the observations available
at one cluster. We select one sensor to be the cluster head (CH) to collect and
summarize the observed data in each cluster and intercluster communications are
assumed to be inexpensive. The CHs with more informative observations transmit
their data to the fusion center (FC) first. By halting before all transmissions
have taken place, transmissions can be saved without performance loss. It is
shown that this ordering approach can guarantee a lower bound on the average
number of transmissions saved for any given GGM and the lower bound can
approach approximately half the number of clusters when the minimum eigenvalue
of the covariance matrix under the alternative hypothesis in each cluster
becomes sufficiently large
Trading Safety Versus Performance: Rapid Deployment of Robotic Swarms with Robust Performance Constraints
In this paper we consider a stochastic deployment problem, where a robotic
swarm is tasked with the objective of positioning at least one robot at each of
a set of pre-assigned targets while meeting a temporal deadline. Travel times
and failure rates are stochastic but related, inasmuch as failure rates
increase with speed. To maximize chances of success while meeting the deadline,
a control strategy has therefore to balance safety and performance. Our
approach is to cast the problem within the theory of constrained Markov
Decision Processes, whereby we seek to compute policies that maximize the
probability of successful deployment while ensuring that the expected duration
of the task is bounded by a given deadline. To account for uncertainties in the
problem parameters, we consider a robust formulation and we propose efficient
solution algorithms, which are of independent interest. Numerical experiments
confirming our theoretical results are presented and discussed
Unequal Error Protection Querying Policies for the Noisy 20 Questions Problem
In this paper, we propose an open-loop unequal-error-protection querying
policy based on superposition coding for the noisy 20 questions problem. In
this problem, a player wishes to successively refine an estimate of the value
of a continuous random variable by posing binary queries and receiving noisy
responses. When the queries are designed non-adaptively as a single block and
the noisy responses are modeled as the output of a binary symmetric channel the
20 questions problem can be mapped to an equivalent problem of channel coding
with unequal error protection (UEP). A new non-adaptive querying strategy based
on UEP superposition coding is introduced whose estimation error decreases with
an exponential rate of convergence that is significantly better than that of
the UEP repetition coding introduced by Variani et al. (2015). With the
proposed querying strategy, the rate of exponential decrease in the number of
queries matches the rate of a closed-loop adaptive scheme where queries are
sequentially designed with the benefit of feedback. Furthermore, the achievable
error exponent is significantly better than that of random block codes
employing equal error protection.Comment: To appear in IEEE Transactions on Information Theor
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