DNN-Based MIMO Signal Detection Using Variational Autoencoder

Recent interest in artificial intelligence (AI) for wireless communication networks has grown significantly for multiple-input multiple-output (MIMO) detection. AI techniques, particularly deep neural networks (DNNs), have shown promise in improving MIMO receiver efficiency by understanding complex channel dynamics and interference patterns. However, existing methods often struggle to balance accuracy and efficiency throughout various channel conditions. This paper introduces a cutting-edge approach called DNN-based MIMO detection using variational eutoencoder (DNN-Det-VAE), which is used to effectively encapsulate data patterns, refining the DNN’s adaptability to challenging channel conditions and improve performance. Simulations are conducted to compare this approach to traditional methods, and it is demonstrated that DNN-Det-VAE achieves a signal-to-noise ratio gain of approximately 1 dB, indicating its superiority in terms of bit error rate (BER) performance.