# Research on the Performance of an End-to-End Intelligent Receiver with Reduced Transmitter Data
## 5 Conclusions  
A large amount of data transmission is one of the challenges of communication systems, and this paper explored whether an intelligent receiver with reduced transmit-side data had the ability to recover data accurately. It was concluded that the communication system consisting of intelligent receivers, not only has the advantage of a low BER, but also has another advantage of reducing the data at the transmitting end. In other words, a communication system consisting of end-to-end intelligent receivers has the same advantage as semantic communication, i.e., a reduction in transmit-side data, which provide new ideas for reducing the amount of data transmitted by a system. In addition, unlike semantic communication that optimizes coding to reduce the transmit-side data, end-to-end smart receivers achieve a transmit-side data reduction by a simple cropping of the data after channel coding.  
Figure 12: Intelligent receiver performance under different cropped data in Rayleigh channel.  
Figure 13: Comparison of equally spaced cropped data.  
To demonstrate that an end-to-end intelligent receiver still has the ability to decode effectively with reduced transmit-side data, we proposed two ways to reduce the transmit-side data, namely, a transmit-side data end-crop and an equal-interval data cut, and we verified that the receiver has the ability to decode accurately when a small amount of transmit-side data is end-cropped, but the receiver decoding ability decreases as the number of cuts increases. In contrast, for the equal-interval cropping of data, the receiver has the ability to recover the original information bits from the cropped data. With equal-interval cropping, the receiver still has a high decoding performance while reducing the data at the transmitter side, and this performance is better than the conventional decoding method.  
The next work will investigate whether the receiver has the ability to decode accurately under data cropping using different coding methods, such as BLAST, STTC and differential space-time coding in MIMO systems. In addition, the optimal data cropping method corresponding to different channel coding methods, and the optimal data cropping size corresponding to the current cropping method, will be continued to be investigated.  
Conceptualization, A.W.; data curation, M.W. and Y.Z.; formal analysis, M.W., A.W. and Y.Z.; investigation, J.C.; methodology, M.W., A.W. and Y.Z.; project administration, A.W.; software, M.W. and Y.Z.; supervision, A.W.; writing--original draft, A.W.; writing--review and editing, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China, Theory and method of marine communication channel identification and model optimization', Project No. U19B2015 and Shaanxi Province Key Industrial Innovation Chain Project, 'Emergency rescue key technology research and equipment development for high-rise building mega-fire', Project No. 2019 ZDLSF07-06.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.