# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 2 Related Work  
The rapid evolution of wireless communication technologies has established MIMO systems as a cornerstone for the realization of 6G networks. By leveraging large antenna arrays, MIMO systems substantially enhance spectral efficiency, data throughput, and connectivity [16]. However, the scaling of MIMO architectures introduces considerable challenges, notably in managing the increased channel complexity and meeting the stringent requirements for real-time signal detection [17]. These challenges limit the exploitation of MIMO's full potential in emerging 6G environments. Consequently, the literature encompasses extensive research on both conventional and AI-based detection techniques. This section presents a comprehensive review of traditional MIMO detection methods, explores the emerging role of AI and DL approaches, and identifies critical research gaps that underpin the motivation for developing optimized AI-driven architectures such as the proposed MIMONet. [PERSON] and [PERSON] [2] provide a seminal survey categorizing MIMO detection algorithms into three principal classes: optimal, linear, and nonlinear methods. Their analysis elucidates the theoretical performance bounds and practical constraints of these categories, particularly in large-scale MIMO contexts. Optimal detection methods, typified by MLD, achieve a minimum BER by exhaustively evaluating all possible transmitted signal vectors. Nevertheless, the computational complexity of MLD grows exponentially with the number of transmit antennas and modulation order, rendering it impractical for real-time applications in systems utilizing high-order Quadrature Amplitude Modulation (QAM) or massive antenna arrays [18, 19]. To address these computational challenges, linear detection schemes such as ZF and MMSE detectors have been widely adopted. The ZF detector mitigates inter-stream interference through channel matrix inversion, albeit at the expense of noise amplification in poorly conditioned channels. MMSE detection improves noise resilience by incorporating statistical noise properties into the estimation process. However, both approaches suffer from degraded BER performance under high noise levels or spatially correlated fading scenarios [20, 21]. Despite their favorable computational efficiency, these linear methods inherently struggle to suppress residual interference and face scalability limitations in large-scale MIMO deployments [2]. The traditional MIMO detection techniques provide a robust theoretical and practical foundation due to their analytical tractability and efficiency, and their scalability constraints in ma-MIMO systems have considerable interest in AI-driven alternatives. AI-based detection frameworks, particularly those employing DL, have gained traction for their capacity to learn complex channel characteristics and offer adaptive, computationally efficient solutions capable of surpassing conventional performance limits. Among AI-aided methods, [PERSON] et al. [22] introduced WeSNet, a weight-scaling neural network architecture designed for complexity-scalable MIMO detection. Building upon the Detection Network (DetNet) paradigm, WeSNet employs monotonically non-increasing profile functions to modulate layer-wise weights adaptively during inference, enabling partial weight computation and reducing computational complexity by approximately 50% without compromising detection accuracy. Extensions such as L-WeSNet and R-WeSNet incorporate trainable weight profiles and log-regularized sparsity constraints, respectively, facilitating the dynamic pruning of redundant network parameters with minimal BER impact. Performance evaluations demonstrated that WeSNet achieved a tenfold reduction in computational complexity relative to ML detection while outperforming DetNet across diverse MIMO configurations. This work exemplifies how deep unfolding and weight modulation techniques can yield efficient detectors tailored for resource-constrained 6G applications.