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 two-stage hybrid deep learning estimator is proposed in which least-squares estimates at pilots placed at every twelfth subcarrier are expanded by two-dimensional bilinear interpolation and refined by a time-distributed convolutional neural network coupled with a long short-term memory (LSTM) recurrent stage.
Deep learning precoders, particularly those based on Transformer architectures, offer superior spectral efficiency for Massive MIMO systems but may incur high computational cost. While existing mixed-precision quantization studies on convolutional neural networks establish important energy baselines, traditional discre...
This paper presents a Multiuser Accelerated Interference Cancellation Network (MaIC-Net) to perform massive MIMO detection in the uplink communication system and reveals that the proposed method outperforms existing methods by achieving 0.000319 less symbol error rate (SER) against 8dB Signal-to-Noise-Ratio (SNR) for t...
In multiple-input multiple-output (MIMO) systems, beamforming is a core enabler for concentrating signal power toward targeted users to enhance spectral efficiency and network capacity. However, realizing the full potential of beamforming critically relies on accurate channel state information (CSI). Conventional CSI a...
A signal processing and deep learning co-design framework, where data-driven JAS parallel support detection is followed by least-squares (LS)-based channel reconstruction and a Gram-attention module that incorporates the Gram matrix as a physical prior to guide feature purification, enabling the network to suppress lea...
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