A DNN-based MIMO signal detector using transformer architecture for next-generation wireless networks
Gevira Omondi, Thomas Otieno Olwal · Journal of Information and Intelligence · 2025
Multiple input multiple output (MIMO) communication systems have emerged as a key technol-ogy to enhance spectral efficiency and reliability in wireless communications. In recent years, deep neural network (DNN)-based approaches have shown promise in addressing the challenges of MIMO signal detection. Among these approaches, the Transformer architecture, known for its effectiveness in capturing long-range dependencies in sequential data, has gained significant attention. Therefore, this paper proposes a revolutionary DNN-based MIMO signal detection scheme using the Transformer-based architecture. This novel scheme leverages the multi-head self-attention mechanism inherent in Transformer architectures, which enables the model to capture both spatial and temporal dependencies in MIMO channels, thereby improving symbol detection accuracy and robustness under varying channel conditions. The proposed scheme's bit error rate (BER) performance is compared with traditional methods through simulations. The results show that the proposed method achieves a signal-to-noise ratio (SNR) gain of nearly 1.5 dB against the traditional detection methods, with the optimal maximum likelihood detector (MLD) only outperforming it by < 0.5 dB. • Transformer-based MIMO signal detection. • Deep learning for wireless communications. • Open access journals on signal processing. • Next-generation wireless communication systems.