Design of Broadband Compressed Sampling Receiver Based on Concurrent Alternate Random Sequences

AbstractIn this paper, we investigate the asymptotic behavior of the multivariate record values by using the Reduced Ordering Principle (R-ordering). Necessary and sufficient conditions for weak convergence of the multivariate record values based on sup-norm are determined. Some illustrative examples are given.

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Pseudo-random sequences with non-maximal length realized according to the Galois scheme based on the primitive polynomial in degree

Journal of Physics Conference Series ◽

10.1088/1742-6596/1658/1/012036 ◽

2020 ◽

Vol 1658 ◽

pp. 012036

Author(s):

V A Pesoshin ◽

V M Kuznetsov ◽

A S Kuznetsova

Keyword(s):

Maximal Length ◽

Random Sequences ◽

Primitive Polynomial

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Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network

Mathematical Problems in Engineering ◽

10.1155/2016/9641608 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Ming Yin ◽

Kai Yu ◽

Zhi Wang

Keyword(s):

Compressive Sensing ◽

Sensor Array ◽

Superior Performance ◽

Wireless Sensor ◽

Prototype System ◽

Reconstruction Method ◽

Compressed Sampling ◽

Data Volume ◽

Simulation Results ◽

Sparsity Structure

For low-power wireless systems, transmission data volume is a key property, which influences the energy cost and time delay of transmission. In this paper, we introduce compressive sensing to propose a compressed sampling and collaborative reconstruction framework, which enables real-time direction of arrival estimation for wireless sensor array network. In sampling part, random compressed sampling and 1-bit sampling are utilized to reduce sample data volume while making little extra requirement for hardware. In reconstruction part, collaborative reconstruction method is proposed by exploiting similar sparsity structure of acoustic signal from nodes in the same array. Simulation results show that proposed framework can reach similar performances as conventional DoA methods while requiring less than 15% of transmission bandwidth. Also the proposed framework is compared with some data compression algorithms. While simulation results show framework’s superior performance, field experiment data from a prototype system is presented to validate the results.

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