# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 2 Related Work
The research study presented in [23] proposed the Maximum a Normalizing Flow Estimate (MANFE) framework, addressing MIMO signal detection under non-Gaussian or unknown noise distributions. Unlike classical detectors predicated on additive white Gaussian noise (AWGN) assumptions, MANFE leverages normalizing flows to probabilistically estimate noise distributions from raw data in an unsupervised manner, enabling near-ML detection performance in challenging noise environments, including impulsive and heavy-tailed scenarios. To mitigate computational demands, the authors developed a low-complexity variant, G-GAMP MANFE, which employs Generalized Approximate Message Passing (G-GAMP) for preliminary estimates, effectively constraining the ML search space and achieving ML-level performance with significantly reduced complexity. A study presented in [24] proposed a DNN-based partial maximum a posteriori detection technique called PMAP-Net, which achieves benchmark performance in a variety of MIMO settings while drastically lowering computational cost. This study shows that adding DNNs to multi-stage detection frameworks can improve computational efficiency and detection reliability. [PERSON] et al. [25] conducted an extensive survey categorizing DL-based MIMO detectors into fully connected neural networks, convolutional neural networks (CNNs), and unfolded iterative models such as DetNet and OAMP-Net. Their analysis highlights the efficacy of model-driven learning approaches that integrate domain knowledge within neural architectures to achieve near-optimal detection with reduced training complexity. Expanding this perspective, [PERSON] and [PERSON] [26] reviewed a broad spectrum of DL techniques, including Graph Neural Networks (GNNs), Reinforcement Learning (RL), and hybrid unfolding models, underscoring the necessity for scalable, generalizable AI models capable of adapting to diverse user loads, modulation schemes, and channel conditions anticipated in 6G MIMO networks. Focusing on practical implementations, a research study proposed AIDETECT, a lightweight AI-based detector optimized for MIMO systems [3]. Trained on realistic channel models, AIDETECT outperforms traditional and baseline AI detectors in symbol error rate. A subsequent comparative study demonstrated AIDETECT's superiority over advanced models such as DetNet and OAMP-Net2, achieving more than 97% gain at 20 dB signal-to-noise ratio (SNR) while incurring lower training overhead. [PERSON] et al. [27] evaluated multiple unfolding-based detectors, emphasizing the benefits of incorporating algorithmic priors into deep network layers. Their results indicate enhanced generalization across modulation schemes and antenna configurations, establishing deep unfolding architectures as highly suitable for adaptive MIMO detection. Furthermore, the noncoherent detection methods are becoming increasingly relevant in massive MIMO systems, mainly due to the challenges and high overhead involved in acquiring accurate channel state information (CSI) when dealing with a large number of antennas and users. Instead of relying on CSI, noncoherent techniques offer an alternative approach to signal detection that can significantly reduce complexity and resource requirements. For example, a research study presented in [28] proposed space-time block coding schemes using PSK constellations that allow for reliable decoding without needing CSI at either the transmitter or receiver, making them especially useful for uplink scenarios where pilot resources are limited [28]. Other techniques, like autocorrelation-based detection, beamspace transformations, and differential feedback, have also shown good results in separating users in low-SNR and far-field conditions [29; 30]. Overall, these efforts highlight the growing appeal of noncoherent detection as a practical and scalable solution for future massive MIMO networks, especially where coherent methods may be too resource-intensive or impractical.