Adaptive Deep Learning Technique With Deep Feature Extraction for Accurate Path Loss Estimation in Millimeter‐Wave Wireless Communication Environments

Millimeter‐wave (mmWave) communication plays a crucial role in wireless systems due to its high data rate capabilities and suitability for 5th generation (5G) networks. However, mmWave signals confront significant propagation issues, which include greater path loss, major attenuation from blockages, and sparse multipath propagation, constraining coverage and consistency. Accurate path loss validation is a serious and composite task for successful network planning, optimization, and resource allocations. To overcome these limitations, effective deep learning–based path loss estimation in mmWave communication systems is developed in this research work. Initially, the required data are collected from the standard datasets and given to the preprocessing phase. Once the data are preprocessed, they are given into the deep feature extraction phase, and it is done by applying the Pyramid Multihead Convolutional Cross Attention Network (PMC‐CANet). The ability to ensure the efficiency of next‐generation wireless networks is what makes it effective in feature extraction tasks. Finally, the path loss estimation process is performed on the extracted deep features through Adaptive Residual Bidirectional Gated Recurrent Unit (AR‐BiGRU), where several parameters are tuned using the Updated Random Attribute–based Sculptor Optimization (URA‐SO). One of the primary advantages of using AR‐BiGRU with USOA for path loss estimation is its ability to process large, high‐dimensional datasets, which can include not only geographical and environmental information but also temporal data, such as time‐of‐day or seasonal variations in path loss. The optimal solution outcome can be achieved by using the developed model. Then, its effectiveness is validated by comparing it with other existing models. This proposed system provides a consistent and best solution for tackling the problems of mmWave signal attenuation, thus enhancing the effectiveness and performance of next‐generation wireless networks. The outcomes reveal that the proposed URA‐SO‐AR‐BiGRU obtained an accuracy of 97.12% when taking the batch size as 15, leading to highly reliable and precise path loss estimations.