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Parallelization of big-data analytics services over a federation of heterogeneous clouds has been considered to improve performance. However, contrary to common intuition, there is an inherent tradeoff between the level of parallelism and the performance for big-data analytics principally because of a significant delay for big-data to get transferred over the network. The data transfer delay can be...
This correspondence describes an efficient Bayesian framework for localization of moving terminals (MTs) in wideband wireless networks. In a previous paper, the authors have presented a grid-based technique, based on a hidden Markov model, that used the power delay profiles of the received signals to track the MT position. This grid-based Bayesian method has proved its efficacy in reducing localization...
This work studies the performance of a data-driven reduced rank scheme for rapid timing acquisition in multiple access communications. Our results show that when only a limited amount of preamble bits are available, instead of the true second-order statistics (SOS), the reduced rank scheme provides reliable timing information. Exploiting the structure of the multiple access interference, the data...
Three applications in wireless networks where model-free stochastic learning is applicable, are discussed. The learning based optimization problems are formulated and simulation results are presented. Some open issues are also discussed.
A conflict of multiplicity k occurs when k stations transmit simultaneously to a multiple access channel. As a result, all stations receive feedback indicating whether k is 0, 1, or is ≥ 2. If k = 1 the transmission succeeds, whereas if k ≥ 2 all the transmissions fail. In general, no a priori information about k is available. We present and analyze an algorithm that enables the conflicting stations...
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