Research on the Performance of an End-to-End Intelligent Receiver with Reduced Transmitter Data

Abstract

A large amount of data transmission is one of the challenges faced by communication systems. In this paper, we revisit the intelligent receiver consisting of a neural network, and we find that the intelligent receiver can reduce the data at the transmitting end while improving the decoding accuracy. Specifically, we first construct a smart receiver model, and then design two ways to reduce the data at the transmitter side, namely, end-of-transmitter data trimming and equal-interval data trimming, to investigate the decoding performance of the receiver under the different trimming methods. The simulation results show that the receiver still has an accurate decoding performance with a small amount of trimming at the end of the transmitter data, while the decoding performance of the smart receiver is better than that of the conventional receiver with complete data when the data is trimmed at equal intervals. Moreover, the receiver with equally-spaced data cropping has a lower BER when the data at the transmitter side is reduced by the same data length. This paper provides a new solution to reduce the amount of data at the transmitter side.

Deep Reinforcement Learning-Assisted Optimization for Resource Allocation in Downlink OFDMA Cooperative Systems

“…DRL can also be applied to different function blocks in communication networks, such as end-to-end design, slice management [ 38 ], mobile edge computing [ 39 ], etc. In [ 40 ], the authors construct a DNN-based end-to-end system optimization model to reduce the data at the transmitter end while improving the decoding accuracy. For the joint optimization of different blocks, the DL approach can utilize a data-driven model based on expert knowledge and a big data system [ 41 , 42 ].…”