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

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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.

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Notes

  1. Note that “gcd” specifies greatest common divisor.

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Authors and Affiliations

  1. Department of Applied Data Science, San Jose State University, San Jose, CA, 95192, USA

    Shih Yu Chang

  2. School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA, 70803, USA

    Hsiao-Chun Wu

  3. Communications Research Centre Canada, Carling Ave., Box 11490, Station H, Ottawa, ON, K2H 8S2, Canada

    Yiyan Wu

  4. Department of Engineering Technology, Northwestern State University, Natchitoches, LA, 71497, USA

    Xinjia Chen

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  1. Shih Yu Chang
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  2. Hsiao-Chun Wu
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Correspondence to Shih Yu Chang.

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Chang, S.Y., Wu, HC., Wu, Y. et al. New probabilistic SINR analysis for capacity and reception-quality studies of DTV transmitter identification systems. Wireless Netw 28, 1521–1530 (2022). https://doi.org/10.1007/s11276-021-02884-9

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