Fifth-generation (5G) networks offer high-speed data transmission with low latency, increased base station volume, improved quality of service (QoS), and massive multiple-input-multiple-output (M-MIMO) channels compared to 4G long-term evolution (LTE) networks. However, the COVID-19 pandemic has disrupted the achievement of mobility and handover (HO) in 5G networks due to significant changes in intelligent devices and high-definition (HD) multimedia applications. Consequently, the current cellular network faces challenges in propagating high-capacity data with improved speed, QoS, latency, and efficient HO and mobility management. This comprehensive survey paper specifically focuses on HO and mobility management issues within 5G heterogeneous networks (HetNets). The paper thoroughly examines the existing literature and investigates key performance indicators (KPIs) and solutions for HO and mobility-related challenges while considering applied standards. Additionally, it evaluates the performance of current models in addressing HO and mobility management issues, taking into account factors such as energy efficiency, reliability, latency, and scalability. Finally, this paper identifies significant challenges associated with HO and mobility management in existing research models and provides detailed evaluations of their solutions along with recommendations for future research.
Sign language plays a vital role in enabling interaction for individuals with hearing and speech impairment, making accurate alphabet-level recognition a fundamental requirement for accessible human-computer interaction systems. This paper investigates visual deep learning approaches for interpreting sign language alphabets from image data. A systematic framework is developed to assess the effectiveness of transfer learning-based convolutional neural networks in capturing discriminative hand gesture features. Using a benchmark American Sign Language (ASL) alphabet dataset, several advanced architectures, including ConvNeXtXLarge, EfficientNet, VGG19, and ResNet-50, are examined under a unified experimental protocol. The comparative analysis reveals that ConvNeXtXLarge achieves the uppermost recognition performance, attaining an accuracy of 99.81%, while EfficientNet, VGG19, and ResNet-50 also demonstrate strong results with accuracy of 99.68%, 99.31%, and 97.29%, respectively. These findings emphasize the effectiveness of modern transfer learning strategies in enhancing visual representation learning for fine-grained motion recognition. The proposed evaluation framework offers practical insights into model selection for scalable and reliable sign language interpretation systems, contributing to the advancement of inclusive assistive technologies and real-world visual language understanding applications.