Advanced spatial adaptive channel estimation for efficient mmWave communication

This study explores the intricacies posed by the unique features of 5G/6G wireless sensor networks (WSNs) to guarantee dependable and long-lasting connectivity. The increasing energy consumption in 5G/6G networks due to higher data rates and more complex architectures emphasizes the necessity for energy-efficient techniques. The WSN resources are limited, specially designed resource allocation and management techniques are essential. In this paper, a unique analogue combining design called advanced spatial adaptive channel estimation (ASACE) and an optimization model for channel state information (CSI) estimation that takes use of the low-rank characteristics of channel matrix sparsity are presented. Gradient descent (GD) optimization is incorporated to improve the suggested approach, demonstrating improvements in residual errors and computing complexity. The optimization problem aims to find the gains and orientations of wideband channel paths. Moreover, a comparative analysis is conducted between the suggested model and many cutting-edge methods, emphasizing error minimization. This thorough analysis offers a nuanced viewpoint on the effectiveness and efficiency of the suggested ASACE approach in the context of wideband cross-entropy (CE) and optimization, which makes a significant contribution to the area.