Energy-Efficient Interference Cancellation in Integrated Sensing and Communication Scenarios

Article Properties
  • DOI (url)
    10.1109/tgcn.2023.3234404
  • Publication Date
    2023/03/01
  • Journal
    IEEE Transactions on Green Communications and Networking
  • Indian UGC (journal)
  • Refrences
    23
  • Citations
    1
  • Junsheng Mu School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China ORCID (unauthenticated)
  • Wenjiang Ouyang School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
  • Zexuan Jing School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China ORCID (unauthenticated)
  • Bohan Li Electronics and Computer Science, University of Southampton, Southampton, U.K. ORCID (unauthenticated)
  • Fangpei Zhang Information Science, China Electronics Technology Group Corporation, Beijing, China
Cite
Mu, Junsheng, et al. “Energy-Efficient Interference Cancellation in Integrated Sensing and Communication Scenarios”. IEEE Transactions on Green Communications and Networking, vol. 7, no. 1, 2023, pp. 370-8, https://doi.org/10.1109/tgcn.2023.3234404.
Mu, J., Ouyang, W., Jing, Z., Li, B., & Zhang, F. (2023). Energy-Efficient Interference Cancellation in Integrated Sensing and Communication Scenarios. IEEE Transactions on Green Communications and Networking, 7(1), 370-378. https://doi.org/10.1109/tgcn.2023.3234404
Mu J, Ouyang W, Jing Z, Li B, Zhang F. Energy-Efficient Interference Cancellation in Integrated Sensing and Communication Scenarios. IEEE Transactions on Green Communications and Networking. 2023;7(1):370-8.
Journal Categories
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Technology
Electrical engineering
Electronics
Nuclear engineering
Telecommunication
Technology
Technology (General)
Industrial engineering
Management engineering
Information technology
Refrences
Title Journal Journal Categories Citations Publication Date
10.1109/LCOMM.2021.3098748
10.1109/MNET.010.2100152
10.1109/ICMLA51294.2020.00185
10.1109/TCAD.2018.2858384
10.1145/3038912.3052577
Citations
Title Journal Journal Categories Citations Publication Date
IoT network traffic classification: a deep learning method with Fourier transform-assisted hyperparameter optimization

 Frontiers in Physics
  • Science: Physics
  • Science: Physics
  • Science: Physics
 2023
Citations Analysis
Category Category Repetition
Science: Physics1
The category Science: Physics 1 is the most commonly referenced area in studies that cite this article.