Simultaneous representation and separation for multiple interference allied with approximation message passing in vehicular communications

Reliable communication is a competitive task for broadband vehicular communications due to the fact that multiform interference has been introduced to the existing broadband transmission, which promotes the development of cognitive vehicular communication systems. To facilitate the improvement of the anti-jamming performance for the coexistence of diverse interference and signals in wireless heterogeneous networks, separating and eliminating various interference to cognitive communication systems is of importance. This paper formulated a novel sparse learning method-based cognitive transformation framework of interference separation for precise interference recovery, which can be efficiently resolved by iteratively learning the prior probability distribution of the sparse interference support. To further enhance the separation accuracy and iterative convergence, the principal component analysis and Bayesian perspective in orthogonal base learning were exploited to singly recover the multiple interference and communication signals. Moreover, through different sparsity states of spectrum analysis, the proposed novel interference separation algorithm was applied to simultaneous separation based on state evolving of approximation message passing, which iteratively learned the belief propagation posteriors and shrank by iterative shrinkage threshold. Simulation results demonstrate that the proposed methods were effective in separating and recovering sparse diversities of interference to communication systems, thereby significantly outperformed the state-of-the-art methods.

• Record URL:
  https://doi.org/10.1016/j.vehcom.2021.100421
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2214209621000905
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/22142096
• Supplemental Notes:
  • © 2021 Guisheng Wang et al. Published by Elsevier Inc. Abstract reprinted with permission of Elsevier.
• Authors:
  • Wang, Guisheng
  • 0000-0002-8509-6667
  • Dong, Shufu
  • Huang, Guoce
  • Hanxiao, Hongyi
  • Yang, Bo
  • 0000-0002-7774-5233
• Publication Date: 2022-4

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Serial:
  • Vehicular Communications
  • Volume: 34
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2214-2096
  • Serial URL: http://www.sciencedirect.com/science/journal/22142096/1/2

Subject/Index Terms

• TRT Terms: Machine learning; Mobile communication systems; Networks; Transmission errors and interference
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01785605
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 25 2021 9:16AM