Conditional generative adversarial network technology for OFDM system receiver signal detection

In response to the limited detection accuracy of traditional orthogonal frequency division multiplexing systems in complex wireless channel environments, this study first uses conditional generative adversarial networks to construct a single input/output orthogonal frequency division multiplexing system signal detection model. On this basis, deep complex neural networks and quadratic concatenation of conditional information matrices are introduced to optimize the structure of conditional generative adversarial networks. Ultimately, a signal detection model for orthogonal frequency division multiplexing systems with multiple inputs and outputs is proposed. The experiment showed that the mean square error of channel detection for this new model could reach as low as 0.2. Compared to other advanced detection models, the channel equalization error of this new model was the lowest at 1.23%. In urban, suburban, and indoor environments, the channel equalization error of the research model was the lowest at 1.23%, the signal reception success rate was the highest at 98.72%, the detection accuracy was the highest at 96.45%, and the average detection time was the shortest at 11.62ms. The data demonstrate that the improved model exhibits significant advantages in signal detection precision and computational efficiency, especially in complex environments where it demonstrates higher robustness and adaptability. This provides a new solution for detecting orthogonal frequency division multiplexing signals in complex environments, with high application prospects.