# Research on the Performance of an End-to-End Intelligent Receiver with Reduced Transmitter Data
## 1 Introduction  
With the in-depth study of communication theories such as channel coding [1; 2], and channel estimation [3; 4], system capacity has gradually approached the Shannon limit [5]. Moreover, the wide application of different communication technologies will generate a large amount of data, which are needed to support large-scale connections in the limited spectrum of resources and to maintain a lower delay, which pose challenges to traditional communication systems [6]. Along with the wide application of deep learning (DL) algorithms in many fields, such as natural language processing (NLP) [7; 8] and image processing [9; 10], researchers are increasingly interested in applying deep learning algorithms to wireless signal processing. Some research has been undertaken to implement some of the functions of communication systems through deep learning algorithms, while some research has also realized the function of some modules of communication systems by deep learning algorithms. For example, by replacing the channel estimation module in orthogonal frequency division multiplexing (OFDM) systems by deep learning image processing techniques [11]. [PERSON] et al. [12] used a deep learning algorithm to deal with serious channel damage in indoor visible light communication (VLC). Moreover, there are some studies that optimize receiver module processing by deep learning algorithms to improve the performance of communication systems. As long as the proper training is performed, they all outperform conventional receivers [13; 14; 15]. Other researchers have considered replacing all modules at the receiving end of the communication system through deep learning algorithms to design an end-to-end intelligent receiver (IR). Among them, [PERSON] et al. [16] have carried out some pioneering work in this area, where they used a fully connected network to implement the channelestimation and channel detection part of an OFDM communication system. The simulation results showed that the deep learning-based approach could implement the entire process of decoding at the receiver side of conventional communication with a performance comparable to that of the minimum mean square error estimator. [PERSON] et al. [17] proposed a model-driven receiver, which combined deep learning with expert knowledge to replace the conventional OFDM system receiver. Different from the design of [PERSON] et al., which used a block-by-block signal processing approach, the receiver consisted of a two-part network of channel estimation and channel detection, and each sub-network was constructed by a deep neural network (DNN) and initialized using existing simple and conventional solutions. The simulation results showed that the algorithm had a lower bit error rate at a low signal-to-noise ratio, and the computational complexity and decoding accuracy of the algorithm were better than those proposed by [PERSON] et al. [PERSON] et al. [18] designed different end-to-end receiver models, which extracted the features of input signals through 1D dense neural networks, and which used multiple binary classifiers in the final classification layer to achieve a bit by bit information flow recovery. The simulation results showed that the depth receiver was superior to the traditional hard decision method in terms of the bit error rate in a variety of different channel environments. These previous works have shown that receiver models constructed by combining deep learning algorithms with traditional communications have better performances than traditional receivers. For the research of an intelligent receiver or replacing a communication module by deep learning, the received data is the same as that of a traditional receiver, and the performance of the receiver is evaluated by comparing the bit error rate (BER) or symbol error rate (SER); however, there is little research on whether the intelligent receiver can achieve accurate decoding by reducing the data at the transmitter.