# Research on the Performance of an End-to-End Intelligent Receiver with Reduced Transmitter Data
## 2 System Model
### Intelligent Receiver  
The intelligent receiver recovers the transmitter-side data interfered by the channel into the original binary bit data by a neural network. Previous works have proposed different intelligent receiver design schemes, and the receiver model in the literature of [18] was chosen in this paper for the following main reasons, firstly, the receiver in the literature of [18] uses the idea of per-bit decoding; therefore, the receiver in the literature of [18] has a lower BER compared to other receivers. Secondly, the MIMO system in this study was the same as in the literature of [18]; therefore, the receiver model shown in the literature of [18] was chosen.  
For the intelligent receiver model proposed in reference [18], its composition is shown in the following Figure 4. The intelligent receiver is composed of a feature extraction module and an original binary bit data recovery module. The feature extraction module is composed of DenseNet [19]. Compared with a traditional convolutional neural network, the dense neural network connects all feature extraction layers through a unique dense connection mechanism. Each layer is connected with all channel dimensions of the previous layer, and this layer is then used as the input of the next layer to achieve feature reuse. At the same time, DenseNet improves the gradient back propagation, making the network easier to train. In addition, the densely-connected network reduces the network parameters to some extent and because of the densely-connected mechanism, it has fewer parameters than the traditional convolutional network, because it does not need to re-learn the redundant feature map.  
For the binary data recovery module, the literature in [18] uses multiple binary classifiers to convert the decoding problem into a probability problem between 0 and 1, through the softmax activation function in the following formula. In other words, for the input IQ  
Figure 4: Principle of intelligent receiver model.  
Figure 3: MIMO wireless channel model.  
data, the binary bit data of a corresponding bit is fixed, so that when the data volume increases, the intelligent receiver can achieve accurate decoding through significant training:  
\[softmax(x_{i})=\frac{\exp(x_{i})}{\sum_{j}\exp(x_{j})} \tag{4}\]  
The intelligent receiver network structure is shown in Figure 5 below. The model extracts IQ data features through DenseNet, uses a dense connection mechanism to achieve a reuse of the IQ data, and achieves per-bit decoding through multiple binary classifiers. The TransitionBlock in this model represents the densely connected block and the DenseBlock represents the densely connected layer. Figure 5 contains four dense connection blocks and four dense connection layers. The K in TransitionBlock (K) represents the size of the convolution core in the TransitionBlock. For the convolution kernel settings in the TransitionBlock, please refer to the literature of [18]. For the 1D IQ data input to the model, it is first passed through 64 1D convolutional kernels of the size 31 \(\times\) 1 for the initial feature extraction and filtering, and then the features are input to the feature extraction layer consisting of four TransitionBlocks as well as the DenseBlock for the in-depth feature extraction, and finally, the binary bits are decoded by bit by constructing multiple binary classifiers.  
Figure 5: Network structure of intelligent receiver.