Fig 1.
Key problems in conventional MU-MIMO systems.
The figure demonstrates the multi-dimensional issues including heterogeneity in user equipment, large data quantities, real-time accuracy requirements, and the necessity for low signalling overhead. The creation of CSI-free architectures is driven by these difficulties.
Fig 2.
Conceptual evolution from CSI-dependent to CSI-free AI-native architectures.
The conventional method (left) necessitates explicit channel estimation blocks, which cause slowness and errors. By combining these features into a single learning framework, the suggested AI-native method (right) removes pilot overhead.
Fig 3.
Comparison of signal processing paradigms.
(Left) Explicit channel matrices are used in classical linear detection. (Centre) Iterative SOR techniques reveal consecutive stages of over-relaxation. (Right) Deep learning-based methods use data to directly learn the mapping.
Table 1.
Identified research gaps and RQ alignment.
Fig 4.
FLOPs vs. number of users (log scale).
In contrast to the scaling of conventional MMSE detectors and the
scaling of full-graph GNN approaches, the suggested residual-aided framework exhibits
scaling, allowing for feasible deployment in massive MIMO systems.
Fig 5.
Detailed computational complexity comparison.
FLOPs (in millions) versus number of users K for the proposed DU-SOR method compared to MMSE, OAMP-Net, and DeepRx baseliness. The proposed method demonstrates consistently lower computational requirements across all user counts.
Table 2.
Hardware resource consumption (K = 64 users).
Fig 6.
Inference latency versus number of users.
Latency (ms) as a function of the number of users K for the proposed DU-SOR method and baseline approaches. The sparse attention mechanism enables the proposed method to maintain lower latency compared to baseline methods across all user counts. The secondary axis displays the Energy-Delay Product (EDP).
Fig 7.
Spectral efficiency vs. SNR showing an 18% gain.
By removing pilot overhead, the suggested CSI-free architecture significantly increases spectral efficiency, especially at higher SNR values.
Fig 8.
Spectral efficiency versus SNR comparison.
Detailed spectral efficiency (bits/s/Hz) as a function of SNR (dB) for the proposed DU-SOR method and baseline approaches. The 18% improvement over conventional MMSE-based systems is consistent across the evaluated SNR range.