MaIC-Net: Learnale weight scabling and complexity scalable deep neural network for massive mimo detection

Low-Complexity Partially-Ignored BP Detection for Massive MIMO Systems With High-Order Modulation

Deep Learning-Driven Joint Antenna Selection for MIMO Systems: Optimizing Capacity and Gain via Improved ResNet

To address the limitations of conventional deep learning models in antenna selection for Multiple Input Multiple Output (MIMO) systems—particularly in terms of insufficient feature extraction and degraded channel state information (CSI) modeling—this paper proposes a structurally enhanced ResNet-based joint transmit–re...

WeFTNet: DNN-Aided Weighted Fourier Transform Approximate MMSE Detection for Massive MIMO

The minimum mean square error (MMSE) detector is widely adopted for massive MIMO systems, but its reliance on matrix inversion results in prohibitive computational complexity. To solve this problem, linear iterative detectors, e.g., weighted Neumann series approximation (wNSA), can circumvent matrix inversion and subst...

DEEP LEARNING-BASED BLIND CHANNEL ESTIMATION IN MASSIVE MIMO SYSTEMS UNDER SPATIAL CORRELATION

The study aims to develop a resource-efficient method for blind channel estimation that is invariant to antenna array topology, with the goal of minimizing signaling overhead and maximizing throughput capacity under conditions of complex spatial correlation.

A Deep Learning-based Low-Complexity SVD Approximation for Large-Scale MIMO Systems

The cubic complexity of singular value decomposition (SVD) has become a major bottleneck for real-time processing in large-scale multiple-input multiple-output (MIMO) systems. To address this issue, in this paper, we propose a deep learning-based low-complexity SVD approximation method tailored for such systems. The pr...

A Graph Foundation Model for Large-Scale MIMO Detection

Large-scale multiple-input multiple-output (MIMO) detection is fundamental to modern wireless networks but constrained by performance-complexity trade-offs. Existing detectors, whether classical or learning-based, often fall short in either scalability or generalizability across heterogeneous scenarios. To overcome the...