# Research on the Performance of an End-to-End Intelligent Receiver with Reduced Transmitter Data
## 3 Transmitter Data Clipping  
The MIMO system shown above can be simplified into the basic framework shown in Figure 6 below. The original bit data is encoded by the source, transmitted to the wireless channel after the channel coding, and then decoded by the corresponding receiver. When the mode of the source coding and channel coding is not considered, the process of the source coding and channel coding can be further simplified into the following formula:  
\[Y_{1}=f_{1}(X) \tag{5}\]  
\[Y_{2}=f_{2}(Y_{1}) \tag{6}\]  
In the above equation, \(f_{1}\) and \(f_{2}\) represent the source coding and channel coding processes, respectively. The data sent by the transmitter can be further expressed as the following equation:  
\[Y_{2}=g(X)=f_{2}(f_{1}(x)) \tag{7}\]  
With the rapid development of communication system theory, source coding improves the effectiveness of information transmission, and channel coding improves the reliability of communication. The original bit data is sent to the channel after being converted by a rigorous formula. The architecture of the receiver of the communication system is shown in Figure 7 below. As shown in Figure 7a, the conventional receiver mainly includes channel decoding and source decoding in the decoding process. The model shown in Figure 7b is an intelligent receiver model. Compared with the traditional receiver with a detailed formula derivation to realize the decoding, the intelligent receiver model often uses the strong nonlinear data  
Figure 6: Basic framework of transmitter of traditional communication system. Figure 7: The architecture of the receiver of the communication system. fitting ability of a convolutional neural network (CNN), long short-term memory (LSTM) and other deep learning networks to decode the received data. Another reason why the intelligent receiver can decode accurately is that the data at the transmitter side is derived by a rigorous formula, and the data input to the neural network still has a certain regularity even under the noise interference, so that the neural network can realize the process of channel decoding and source decoding at the same time after significant training, and then complete the decoding. Taking the intelligent receiver based on a CNN designed in the literature of [18] as an example, which converts IQ data into a one-dimensional matrix input to the network, it achieves the extraction of the input data features through a densely connected convolutional network (DenseNet), and sets multiple binary classifiers in the fully connected layer to achieve the bit-by-bit recovery of the original bit signal. In the task of image classification or reconstruction by a CNN, the image an often pre-scaled to maintain the same size. In addition, as shown in Figure 8a, the CNN can still recognize the animal \"sheep\" in the image by cutting the image. In the task of image recognition, the size of an image is reduced by reducing and cropping the image, and the CNN model still has the ability to recognize graphic information. The CNN-based decoding mode adopted in the literature of [18] is the same as the principle of image recognition and the classification task. The CNN can still realize decoding after cutting the image. Consequently, for receivers using the same model, as shown in Figure 8b, we will now explore whether the intelligent receiver model designed based on a CNN has an accurate decoding capability when reducing the data at the transmitter of the communication system. Unlike the image data reduction method, in order to reduce the IQ data sent by the transmitter in the communication system, this study only considers reducing the data at the transmitter through clipping. Additionally, where end cropping is the simplest way to reduce the data, this paper proposes an equal interval reduction in the launch data. Figure 8: Data cutting idea. (**a**) CNN recognition under different input data. (**b**) Data clipping idea at transmitter.