Conditional generative adversarial network technology for OFDM system receiver signal detection.

ABSTRACT: 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 ad

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12520388 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature