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OFDM Receiver Design With Learning-Driven Automatic Modulation Recognition
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DOI：10.1109/TCCN.2023.3327561.png)
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Abstract
En 中文
The orthogonal frequency-division multiplexing (OFDM) is widely used in modern radio communications because of its efficient spectrum utilization. As we know, the adaptive modulation can efficiently improve the spectrum utilization of OFDM systems, in comparison with the non-adaptive modulation. For this, we design a learning-driven automatic modulation recognition (AMR) receiver for the OFDM system in this paper. The AMR receiver is composed of the robust channel estimation neural network (RCE-NET), the modulation recognition neural network (MR-NET) and the decoding network (DE-NET). Specifically, the RCE-NET estimates a channel state information by the least squares estimation, and the MR-NET realizes the modulation recognition based on the constellation diagram of the equalized signals. After that, the corresponding DE-NET is used for decoding according to the recognition result of the MR-NET. In order to verify the robustness of the designed receiver, we conduct the simulations in different channel models. Simulation results demonstrate the efficiency of our proposed AMR receiver on the channel estimation, modulation recognition and decoding.
Keywords:
Modulation
OFDM
Receivers
Channel estimation
Symbols
Convolutional neural networks
Fading channels
deep learning
channel estimation
modulation recognition
adaptive modulation
wireless communications
Journal
I
IF:
7
Papers:
1.5K
Citations:
5.5K