New probabilistic SINR analysis for capacity and reception-quality studies of DTV transmitter identification systems

Publication Date

5-1-2022

Document Type

Article

Publication Title

Wireless Networks

Volume

28

Issue

4

DOI

10.1007/s11276-021-02884-9

First Page

1521

Last Page

1530

Abstract

Digital Terrestrial Television (DTV) has been widely deployed globally for more than a decade. The transmitter identification (Tx-ID) technique specified in modern DTV standards becomes important today as the number of DTV transmitters grows with the expanded coverage area. In the ATSC standards, Kasami sequences, a crucial class of pseudo random sequences, are considered as feasible Tx-ID sequences because they possess several favorable properties leading to nearly Dirac-delta autocorrelation/cross-correlation functions and large sequence capacities. It is well known that the interference-pluse-noise level (INL) or signal-to-interferenec-plus-noise ratio (SINR) plays a very important role in the Tx-ID system performance. Nontheless, such a crucial factor has been evaluated only in the statistical average due to the difficulty of characterizing the exact pertinent probabilitistic analysis. In this work, we combat the aforementioned difficulty by applying a probability-density approximation method to statistically characterize random variables like INL and SINR. With the exact probability density functions of INL and SINR, we can analyze the detailed statistical characteristics of numerous Tx-ID related parameters, including Tx-ID capacity and reception quality in terms of SINR. Extensive numerical experiments are also undertaken to justify the effectiveness of our proposed new analysis and explore detailed quantitative insight for critical Tx-ID system parameters and performance metrics.

Keywords

Correlation distribution, Digital terrestrial television (DTV), Fading channels, Kasami sequences, Probability density function (PDF) approximation, Signal-to-interference-plus-noise ratio (SINR), Transmitter identification (Tx-ID)

Department

Applied Data Science

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