Implementation separated autoencoder in an OFDM system by Deep Network Designer

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Pooria Tabesh Mehr
Pooria Tabesh Mehr 2020-11-29
评论： Ethem 2025-4-1
I want to implement an autoencoder that separated to an encoder in tramsmitter and a decoder in receiver in an OFDM system . The joint loss function of system is sum of two separated loss functions(L1: reduction of BER, L2: reduction of PAPR). The L2 loss function is only related to encoder and L1 is related to both encoder and decoder. I don't know how design a DNN with a black box between two separated part of that by Deep Network designer? Also I don't know how can I reduce the PAPR that is only related to encoder(i.e. the method of backpropagation in this structure)?

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回答（2 个）

Aditya Patil
Aditya Patil 2020-12-23
You should be able to write custom layers, and import them into Deep Network Designer. However, currently it is not possible to implement the architecture you mentioned with only Deep Network Designer.
Ethem
Ethem 2023-2-17
  2 个评论
CHANNARAM
CHANNARAM 2025-3-31
I aim to implement a Peak-to-Average Power Ratio (PAPR) reduction in MIMO-OFDM systems using a Deep Learning technique, specifically a Convolutional Autoencoder (CAE). The objective is to reduce PAPR, enhance Bit Error Rate (BER) performance, and improve power spectral density. This project will leverage deep learning-based encoding and decoding techniques to optimize signal transmission efficiency in MIMO-OFDM systems. can i know how to implement this type of project.

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