Optimal Prediction Likelihood Tree Based Source- Channel ML Decoder for Wireless Sensor Networks

Manoj, C.; Jagannatham, Aditya K.

doi:10.1109/lsp.2013.2294794

Cited by 3 publications

(5 citation statements)

References 8 publications

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“…An interesting approach to address this problem is to avoid sending redundant data to the server. Several works on sensor networks have noted that the data collected by sensors is highly redundant [142]. Reducing data redundancy could be achieved using some dimensionality reduction algorithms like principal component analysis (PCA) or by using a smart gateway for the sensor network, which aggregates the data collected by the network before sending to the remote server [143].…”

Section: Intelligent Processing and Storagementioning

confidence: 99%

5G roadmap: 10 key enabling technologies

et al. 2016

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Section: Intelligent Processing and Storagementioning

confidence: 99%

5G roadmap: 10 key enabling technologies

et al. 2016

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“…Recursive algorithms in and this paper are very different. References and describe application of prediction concept (Markov chain models and wireless sensor networks) and have no connection with the recursive algorithm in this paper.Remark Identification algorithms for the state space model are presented in . But field is not so close to our polynomial domain which is considered in this paper.…”

Section: Robust Recursive Stochastic Gradient Algorithmmentioning

confidence: 99%

“…Recursive algorithms in [1] and this paper are very different. References [2] and [3] describe application of prediction concept (Markov chain models and wireless sensor networks) and have no connection with the recursive algorithm in this paper.…”

Section: Remarkmentioning

confidence: 99%

See 1 more Smart Citation

A global convergent outlier robust adaptive predictor for MIMO Hammerstein models

Filipović

2016

Intl J Robust & Nonlinear

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Summary The paper considers the outlier‐robust recursive stochastic approximation algorithm for adaptive prediction of multiple‐input multiple‐output (MIMO) Hammerstein model with a static nonlinear block in polynomial form and a linear block is output error (OE) model. It is assumed that there is a priori information about a distribution class to which a real disturbance belongs. Within the framework of these assumptions, the main contributions of this paper are: (i) for MIMO Hammerstein OE model, the stochastic approximation algorithm, based on robust statistics (in the sense of Huber), is derived; (ii) scalar gain of algorithm is exactly determined using the Laplace function; and (iii) a global convergence of robust adaptive predictor is proved. The proof is based on martingale theory and generalized strictly positive real conditions. Practical behavior of algorithm was illustrated by simulations. Copyright © 2016 John Wiley & Sons, Ltd.

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“…A priced-based distributed power and rate control algorithm was proposed in [ 12 ]; it can simulate the cooperation of power control and rate adaptation among the nodes. A joint source-channel maximum likelihood (SCML) decoding framework is proposed in wireless sensor networks (WSNs) [ 13 ], and prediction likelihood tree (PLT) approach is applied to exploit the spatiotemporal narrowband properties of the sensor data for sequence detection.…”

Section: Introductionmentioning

confidence: 99%

Distributed Optimal Power and Rate Control in Wireless Sensor Networks

Tang

Bai

et al. 2014

The Scientific World Journal

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With the rapid development of wireless sensor networks, reducing energy consumption is becoming one of the important factors to extend node lifetime, and it is necessary to adjust the launching power of each node because of the limited energy available to the sensor nodes in the networks. This paper proposes a power and rate control model based on the network utility maximization (NUM) framework, where a weighting factor is used to reflect the influence degree of the sending power and transmission rate to the utility function. In real networks, nodes interfere with each other in the procedure of transmitting signal, which may lead to signal transmission failure and may negatively have impacts on networks throughput. Using dual decomposition techniques, the NUM problem is decomposed into two distributed subproblems, and then the conjugate gradient method is applied to solve the optimization problem with the calculation of the Hessian matrix and its inverse in order to guarantee fast convergence of the algorithm. The convergence proof is also provided in this paper. Numerical examples show that the proposed solution achieves significant throughput compared with exiting approaches.

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Optimal Prediction Likelihood Tree Based Source- Channel ML Decoder for Wireless Sensor Networks

Cited by 3 publications

References 8 publications

5G roadmap: 10 key enabling technologies

5G roadmap: 10 key enabling technologies

A global convergent outlier robust adaptive predictor for MIMO Hammerstein models

Distributed Optimal Power and Rate Control in Wireless Sensor Networks

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