TY - JOUR
T1 - A Two-Stage High-Order Modulation Recognition Based on Projected Accumulated Constellation Vector in Non-Cooperative B5G OSTBC-OFDM Systems
AU - An, Zeliang
AU - Zhang, Tianqi
AU - Ma, Baoze
AU - Xu, Yuqing
N1 - Funding Information:
This work is supported by the National Natural Science Foundation of China (No. 61671095 , 61702065 , 61701067 , 61771085 ), the Project of Key Laboratory of Signal and Information Processing of Chongqing (No. CSTC2009CA2003 ), the Natural Science Foundation of Chongqing (No. cstc2021jcyj-msxmX0836 ) and the Research Project of Chongqing Educational Commission (No. KJ1600427 , KJ1600429 ).
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/11
Y1 - 2022/11
N2 - Blind modulation recognition (BMR) is a pivotal signal processing technology for cognitive receivers to reduce signaling overhead and improve transmission efficiency. Although BMR techniques have been extensively explored for single-carrier systems, only a few research have been published for OSTBC-OFDM systems. More troubling is that high-order modulation types will make the BMR harder because the constellation points are overcrowded. To remedy these flaws, we propose a two-stage high-order BMR approach that leverages low-complexity cumulants and projected accumulated constellation vector (P-ACV) to recognize high-order modulation types (e.g., 2048QAM). Firstly, zero-forcing blind equalization is employed to recover the damaged signal and enhance its feature representation. Next, the first-stage recognition module leverages the fourth-order cumulants to classify 13 modulation types into three groups, including PSK&PAM, square-shaped QAM and cross-shaped QAM groups. Finally, the second-stage recognition module distinguishes the inter-class signal in each group by P-ACV features. The temporal convolution network is built to tap the potentialities of P-ACV and speed up online inference. Numerical results demonstrate that our two-stage algorithm obtains a recognition accuracy of 97.37% when signal-to-noise-ratio (SNR) ≥ 12 dB and performs six times faster than the suboptimal method. Notably, it does not require prior knowledge, such as channel state information (CSI) and SNR conditions.
AB - Blind modulation recognition (BMR) is a pivotal signal processing technology for cognitive receivers to reduce signaling overhead and improve transmission efficiency. Although BMR techniques have been extensively explored for single-carrier systems, only a few research have been published for OSTBC-OFDM systems. More troubling is that high-order modulation types will make the BMR harder because the constellation points are overcrowded. To remedy these flaws, we propose a two-stage high-order BMR approach that leverages low-complexity cumulants and projected accumulated constellation vector (P-ACV) to recognize high-order modulation types (e.g., 2048QAM). Firstly, zero-forcing blind equalization is employed to recover the damaged signal and enhance its feature representation. Next, the first-stage recognition module leverages the fourth-order cumulants to classify 13 modulation types into three groups, including PSK&PAM, square-shaped QAM and cross-shaped QAM groups. Finally, the second-stage recognition module distinguishes the inter-class signal in each group by P-ACV features. The temporal convolution network is built to tap the potentialities of P-ACV and speed up online inference. Numerical results demonstrate that our two-stage algorithm obtains a recognition accuracy of 97.37% when signal-to-noise-ratio (SNR) ≥ 12 dB and performs six times faster than the suboptimal method. Notably, it does not require prior knowledge, such as channel state information (CSI) and SNR conditions.
KW - Beyond 5G
KW - High-order modulation
KW - Orthogonal space-time block coded-orthogonal frequency division multiplexing (OSTBC-OFDM) systems
KW - Projected accumulated constellation vector
KW - Two-stage modulation recognition
UR - http://www.scopus.com/inward/record.url?scp=85133830887&partnerID=8YFLogxK
U2 - 10.1016/j.sigpro.2022.108673
DO - 10.1016/j.sigpro.2022.108673
M3 - Journal article
AN - SCOPUS:85133830887
SN - 0165-1684
VL - 200
JO - Signal Processing
JF - Signal Processing
M1 - 108673
ER -