7,169 research outputs found
Bayesian Symbol Detection in Wireless Relay Networks via Likelihood-Free Inference
This paper presents a general stochastic model developed for a class of
cooperative wireless relay networks, in which imperfect knowledge of the
channel state information at the destination node is assumed. The framework
incorporates multiple relay nodes operating under general known non-linear
processing functions. When a non-linear relay function is considered, the
likelihood function is generally intractable resulting in the maximum
likelihood and the maximum a posteriori detectors not admitting closed form
solutions. We illustrate our methodology to overcome this intractability under
the example of a popular optimal non-linear relay function choice and
demonstrate how our algorithms are capable of solving the previously
intractable detection problem. Overcoming this intractability involves
development of specialised Bayesian models. We develop three novel algorithms
to perform detection for this Bayesian model, these include a Markov chain
Monte Carlo Approximate Bayesian Computation (MCMC-ABC) approach; an Auxiliary
Variable MCMC (MCMC-AV) approach; and a Suboptimal Exhaustive Search Zero
Forcing (SES-ZF) approach. Finally, numerical examples comparing the symbol
error rate (SER) performance versus signal to noise ratio (SNR) of the three
detection algorithms are studied in simulated examples
One-shot rates for entanglement manipulation under non-entangling maps
We obtain expressions for the optimal rates of one- shot entanglement
manipulation under operations which generate a negligible amount of
entanglement. As the optimal rates for entanglement distillation and dilution
in this paradigm, we obtain the max- and min-relative entropies of
entanglement, the two logarithmic robustnesses of entanglement, and smoothed
versions thereof. This gives a new operational meaning to these entanglement
measures. Moreover, by considering the limit of many identical copies of the
shared entangled state, we partially recover the recently found reversibility
of entanglement manipu- lation under the class of operations which
asymptotically do not generate entanglement.Comment: 7 pages; no figure
10211 Abstracts Collection -- Flexible Network Design
From Monday 24.05.2010---Friday 28.05.2010, the Dagstuhl Seminar 10211 ``Flexible Network Design \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
NASA Thesaurus supplement: A four part cumulative supplement to the 1988 edition of the NASA Thesaurus (supplement 3)
The four-part cumulative supplement to the 1988 edition of the NASA Thesaurus includes the Hierarchical Listing (Part 1), Access Vocabulary (Part 2), Definitions (Part 3), and Changes (Part 4). The semiannual supplement gives complete hierarchies and accepted upper/lowercase forms for new terms
Double Deep Q-Learning and Faster R-CNN-Based Autonomous Vehicle Navigation and Obstacle Avoidance in Dynamic Environment
Autonomous vehicle navigation in an unknown dynamic environment is crucial for both supervised- and Reinforcement Learning-based autonomous maneuvering. The cooperative fusion of these two learning approaches has the potential to be an effective mechanism to tackle indefinite environmental dynamics. Most of the state-of-the-art autonomous vehicle navigation systems are trained on a specific mapped model with familiar environmental dynamics. However, this research focuses on the cooperative fusion of supervised and Reinforcement Learning technologies for autonomous navigation of land vehicles in a dynamic and unknown environment. The Faster RCNN, a supervised learning approach, identifies the ambient environmental obstacles for untroubled maneuver of the autonomous vehicle. Whereas, the training policies of Double Deep Q-Learning, a Reinforcement Learning approach, enable the autonomous agent to learn effective navigation decisions form the dynamic environment. The proposed model is primarily tested in a gaming environment similar to the real-world. It exhibits the overall efficiency and effectiveness in the maneuver of autonomous land vehicles.publishedVersio
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