KAN-and-Attention Based Precoding for Massive MIMO ISAC Systems

Precoding technology is one of the core technologies that significantly impacts the performance of massive Multiple-Input Multiple-Output (MIMO) Integrated Sensing and Communication (ISAC) systems. Traditional precoding methods, due to their inherent limitations, struggle to adapt to complex channel conditions. Although more advanced neural network-based precoding schemes can accommodate complex channel environments, they suffer from high computational complexity. To address these issues, this paper proposes a KAN-and-Attention based ISAC Precoding (KAIP) scheme for massive MIMO ISAC systems. KAIP extracts channel interference features through multi-layer attention mechanisms and leverages the nonlinear fitting capability of the Kolmogorov–Arnold Network (KAN) to generate precoding matrices, significantly enhancing system performance. Simulation results demonstrate that compared with conventional precoding schemes, the proposed KAIP scheme exhibits significant performance enhancements, including a 70% increase in sum rate (SR) and a 96% decrease in computing time (CT) compared with fully connected neural network (FCNN) based precoding, and a 4% improvement in received power (RP) over the precoding based on convolutional neural network (CNN).