PMID- 33668245
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20210311
LR  - 20210315
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 21
IP  - 5
DP  - 2021 Feb 24
TI  - Automatic Modulation Recognition Based on a DCN-BiLSTM Network.
LID - 10.3390/s21051577 [doi]
LID - 1577
AB  - Automatic modulation recognition (AMR) is a significant technology in 
      noncooperative wireless communication systems. This paper proposes a deep complex 
      network that cascades the bidirectional long short-term memory network 
      (DCN-BiLSTM) for AMR. In view of the fact that the convolution operation of the 
      traditional convolutional neural network (CNN) loses the partial phase 
      information of the modulated signal, resulting in low recognition accuracy, we 
      first apply a deep complex network (DCN) to extract the features of the modulated 
      signal containing phase and amplitude information. Then, we cascade bidirectional 
      long short-term memory (BiLSTM) layers to build a bidirectional long short-term 
      memory model according to the extracted features. The BiLSTM layers can extract 
      the contextual information of signals well and address the long-term dependence 
      problems. Next, we feed the features into a fully connected layer. Finally, a 
      softmax classifier is used to perform classification. Simulation experiments show 
      that the performance of our proposed algorithm is better than that of other 
      neural network recognition algorithms. When the signal-to-noise ratio (SNR) 
      exceeds 4 dB, our model's recognition rate for the 11 modulation signals can 
      reach 90%.
FAU - Liu, Kai
AU  - Liu K
AD  - School of Communication and Information Engineering, Shanghai University, 
      Shanghai 200444, China.
FAU - Gao, Wanjun
AU  - Gao W
AUID- ORCID: 0000-0001-6042-9886
AD  - School of Communication and Information Engineering, Shanghai University, 
      Shanghai 200444, China.
FAU - Huang, Qinghua
AU  - Huang Q
AD  - School of Communication and Information Engineering, Shanghai University, 
      Shanghai 200444, China.
LA  - eng
GR  - 61571279/National Natural Science Foundation of China/
PT  - Journal Article
DEP - 20210224
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC7956213
OTO - NOTNLM
OT  - automatic modulation recognition
OT  - bidirectional long short-term memory network
OT  - convolutional neural network
OT  - deep complex network
COIS- The authors declare no conflict of interest.
EDAT- 2021/03/07 06:00
MHDA- 2021/03/07 06:01
PMCR- 2021/02/24
CRDT- 2021/03/06 01:01
PHST- 2021/01/17 00:00 [received]
PHST- 2021/02/15 00:00 [revised]
PHST- 2021/02/19 00:00 [accepted]
PHST- 2021/03/06 01:01 [entrez]
PHST- 2021/03/07 06:00 [pubmed]
PHST- 2021/03/07 06:01 [medline]
PHST- 2021/02/24 00:00 [pmc-release]
AID - s21051577 [pii]
AID - sensors-21-01577 [pii]
AID - 10.3390/s21051577 [doi]
PST - epublish
SO  - Sensors (Basel). 2021 Feb 24;21(5):1577. doi: 10.3390/s21051577.