Enhancing Channel Estimation and Efficiency in Millimeter‐Wave Massive Multiple Input Multiple Output Systems by Exploiting Reconfigurable Intelligent Surface and Gegenbauer Graph Neural Networks for High‐Resolution Signal Reconstruction

ABSTRACT A reconfigurable intelligent surface (RIS)–aided millimeter (mm)‐wave massive multiple input multiple output (MIMO) system improves network capacity and signal quality by dynamically managing the propagation environment, resulting in more efficient and reliable wireless communication. However, channel acquisition remains a major challenge in deploying RIS‐assisted communication systems because of the large amount of reflective elements in an RIS, which are passive devices lacking active transmission or reception capabilities. In this research, enhancing channel estimation and efficiency in millimeter‐wave massive multiple input multiple output systems by exploiting reconfigurable intelligent surface and Gegenbauer graph neural networks for high‐resolution signal reconstruction ( CE ‐MWMMIMO‐RIS‐GGNN) is proposed. Initially, a dataset is generated by modeling the base station (BS) with a uniform planar array (UPA). Gegenbauer graph neural networks (GGNN) are then employed to enhance channel estimation by mapping the received measurements to the RIS channel, capturing its distribution characteristics for better channel reconstruction. The GGNN is enhanced through multi‐agent cubature Kalman optimizer (MACKO) by optimizing its parameters, which ensures improved accuracy in channel estimation and better overall communication system performance. The proposed technique is executed in Python, and the effectiveness of the proposed approach is evaluated using performance criteria, with normalized mean square error (NMSE) and mean square error (MSE). The proposed method significantly outdoes existing methods regarding NMSE, MSE, and channel estimation time across various uplink SNR levels and training overheads. The proposed method achieves an NMSE of −25 dB at an SNR of 40 dB, compared to −5 to −10 dB for the existing methods such as Channel estimation for reconfigurable intelligent surface aided millimeter‐wave massive multiple‐input multiple output system with deep residual attention network ( CE RIS‐MWMMIMO‐DRA‐Net), deep learning‐based channel estimation for wideband hybrid mmWave massive MIMO (CEWH‐MWMMIMO‐DNN) and one‐bit mmWave MIMO channel estimation utilizing deep generative networks (OMWMIMO‐CE‐DGN) respectively. The method also demonstrates a channel estimation time of 9.08 s, which is faster than the existing methods, offering a marked improvement in processing efficiency. This makes it more suitable for large‐scale wireless networks and enhances system responsiveness in dynamic environments.