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AIMS Mathematics

2024, Volume 9, Issue 7: 18236-18246. doi: 10.3934/math.2024890
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Research article

A class of nearly optimal codebooks and their applications in strongly regular Cayley graphs

  • 1.
    School of Computer Science and Technology, Tiangong University, Tianjin 300387 China
  • 2.
    Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University, Tianjin 300387, China
  • 3.
    School of Information Technology and Engineering, Tianjin University of Technology and Education, Tianjin 300222 China
  • Received: 01 February 2024 Revised: 15 May 2024 Accepted: 27 May 2024 Published: 30 May 2024

  • MSC : 94B05, 11T23, 11T24, 12E20

  • Codebooks with small inner-product correlations are desirable in many fields, including compressed sensing, direct spread code division multiple access (CDMA) systems, and space-time codes. The objective of this paper is to present a class of codebooks and explore their applications in strongly regular Cayley graphs. The obtained codebooks are nearly optimal in the sense that their maximum cross-correlation amplitude nearly meets the Welch bound. As far as we know, this construction of codebooks provides new parameters.

    Citation: Qiuyan Wang, Weixin Liu, Jianming Wang, Yang Yan. A class of nearly optimal codebooks and their applications in strongly regular Cayley graphs[J]. AIMS Mathematics, 2024, 9(7): 18236-18246. doi: 10.3934/math.2024890

    Related Papers:

  • Codebooks with small inner-product correlations are desirable in many fields, including compressed sensing, direct spread code division multiple access (CDMA) systems, and space-time codes. The objective of this paper is to present a class of codebooks and explore their applications in strongly regular Cayley graphs. The obtained codebooks are nearly optimal in the sense that their maximum cross-correlation amplitude nearly meets the Welch bound. As far as we know, this construction of codebooks provides new parameters.


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  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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