Blind identification algorithm of time-domain STBC-OFDM based on feature sequence

被引：0
|
作者：
Yu K. [1 ]
Zhang L. [1 ]
Yan W. [1 ]
Jin K. [2 ]
机构：
[1] Information Fusion Institute, Naval Aviation University, Yantai
[2] School of Basis of Aviation, Naval Aviation University, Yantai
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2021年 / 47卷 / 08期
关键词：
Blind signal identification; Fourth-order eigenvector; Signal and information processing; Space Time Block Codes (STBC); Time-domain characteristic sequence;
D O I：
10.13700/j.bh.1001-5965.2020.0262
中图分类号：
学科分类号：
摘要：
In order to effectively solve the problem of low SNR adaptability in the blind identification process of STBC-OFDM signals, an identification algorithm for constructing feature sequences in time domain is proposed under the premise of known OFDM block size. This algorithm deduces the time-domain features and the fourth-order eigenvectors of the STBC received signals and constructs the feature sequences. By detecting the feature sequences, the four STBC-OFDM signals are identified. The derivation and simulation results show that this algorithm does not need priori information such as channel, noise, modulation mode and starting position of OFDM block, and has good recognition performance under low SNR conditions. It has good robustness to frequency offset, Doppler frequency shift and time delay, requires low calculation amount, and thus has high practical value. © 2021, Editorial Board of JBUAA. All right reserved.
引用
收藏
页码：1524 / 1532
页数：8
相关论文
共 17 条
  • [1] PATIL V M, UJJINIMATAD R, PATIL S R., Correction to:Signal detection in cognitive radio networks over AWGN and fading channels, International Journal of Wireless Information Networks, 25, 1, (2018)
  • [2] KUMAR M, MAJHI S., Joint signal detection and synchronization for OFDM based cognitive radio networks and its implementation, Wireless Networks, 25, 2, pp. 699-712, (2019)
  • [3] FERNANDO X, SULTANA A, HUSSAIN S, Et al., Resource allocation in OFDM-based cognitive radio systems, Cooperative spectrum sensing and resource allocation strategies in cognitive radio networks, pp. 59-72, (2019)
  • [4] ALAMOUTI S M., A simple transmit diversity technique for wireless communications, IEEE Journal on Select Areas in Communications, 16, 8, pp. 1451-1458, (1998)
  • [5] DOBRE O A., Signal identification for emerging intelligent radios:Classical problems and new challenges, IEEE Instrumentation & Measurement Magazine, 18, 2, pp. 11-18, (2015)
  • [6] BARAZIDEH R, NIKNAM S, NATARAJAN B., Impulsive noise detection in OFDM-based systems:A deep learning perspective
  • [7] PATRA J P, SINGH P., A novel LMMSE-EM channel estimator for high mobility STBC-OFDM system, Journal of Circuits, Systems and Computers, 28, 13, (2019)
  • [8] ELDEMERDASH Y A, MAREY M, DOBRE O A, Et al., Fourth-order statistics for blind classification of spatial multiplexing and Alamouti space-time block codes signals, IEEE Transaction on Communication, 61, 6, pp. 2420-2431, (2013)
  • [9] MAREY M, DOBRE O A, INKOL R., Blind STBC identification for multiple-antenna OFDM systems, IEEE Transaction on Communication, 62, 5, pp. 1554-1567, (2014)
  • [10] KARAMI E, DOBRE O., Identification of SM-OFDM and AL-OFDM signals based on their second-order cyclostationarity, IEEE Transactions on Vehicular Technology, 64, 3, pp. 942-953, (2015)
12