A Convolutional Self-Attention Network for CSI Reconstruction in MIMO System

A convolutional self-attention network-based channel state information reconstruction method is presented to address the issue of low accuracy in Multiple-Input Multiple-Output (MIMO) at a high compression rate. First, an encoder-decoder structure-based model built. The feature extracted by encoder’s network, and compressed adding attention block. At same time, nonuniform quantized prevent transmission process from using up too much bandwidth. dequantization module block are added decoder reduce impact noise on matrix, converting continuous value into discrete increase long-time cosine annealing training approach. According simulation results, when compared CsiNet, Lightweight CNN, CRNet, CLNet, convergence speed improved 17.64%, indoor precision average 37.4%, outside 32.5% under all compressions.