Design and Real-Time Implementation of an Intelligent Wireless Receiver System 


Vol. 51,  No. 2, pp. 374-381, Feb.  2026
10.7840/kics.2026.51.2.374


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  Abstract

This paper presents the design and implementation of an intelligent wireless reception system that operates in real-time by applying Machine Learning to the physical layer of a wireless receiver. The proposed system introduces a Neural Receiver, which replaces the channel estimation and phase noise estimation functions in the conventional OFDM-based wireless reception structure with a Transformer-based neural network model, executing it in real-time on a GPU. The receiver is connected via a high-speed interface structure among FPGA, CPU, and GPU. The neural receiver is implemented in a structure that allows slot-wise parallel processing on the GPU by optimizing a PyTorch-based machine learning model with TensorRT. In indoor environment tests, after channel and phase correction, the proposed intelligent physical layer receiver achieved an average SNR performance improvement of over 3dB compared to conventional hardware receivers. Furthermore, the system's practical feasibility was demonstrated by real-time processing of up to 7 slots within a mid-frame using a parallel structure with up to 8 neural receiver instances.

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[IEEE Style]

J. Son, Y. Kim, Y. R. Lee, J. Bae, "Design and Real-Time Implementation of an Intelligent Wireless Receiver System," The Journal of Korean Institute of Communications and Information Sciences, vol. 51, no. 2, pp. 374-381, 2026. DOI: 10.7840/kics.2026.51.2.374.

[ACM Style]

Jungbo Son, Yunjoo Kim, Yu Ro Lee, and JungSook Bae. 2026. Design and Real-Time Implementation of an Intelligent Wireless Receiver System. The Journal of Korean Institute of Communications and Information Sciences, 51, 2, (2026), 374-381. DOI: 10.7840/kics.2026.51.2.374.

[KICS Style]

Jungbo Son, Yunjoo Kim, Yu Ro Lee, JungSook Bae, "Design and Real-Time Implementation of an Intelligent Wireless Receiver System," The Journal of Korean Institute of Communications and Information Sciences, vol. 51, no. 2, pp. 374-381, 2. 2026. (https://doi.org/10.7840/kics.2026.51.2.374)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880