Enhancing AR and VR Experiences Using Deep Waveform Prediction Network for High-Speed Wireless Communication

Augmented Reality (AR) and Virtual Reality (VR) technologies have transformed various industries which include healthcare and education together with entertainment and gaming. The research introduces Deep Waveform Prediction Network (DWPN) which represents a new deep learning architecture that optimizes high-speed wireless communication for Augmented Reality (AR) and Virtual Reality (VR) experiences. The DWPN merges CNNs with RNN s together with attention approaches to forecast and optimize wireless signal waveforms regardless of context variations. The model was tested across Wi-Fi 6 and 5G protocols as well as simulated 6G networks through performance assessments of BER along with SNR measurements and latency benchmarks and packet loss rates. The DWPN obtained 94.7% waveform prediction accuracy which reduced BER by 85.71 % while raising SNR by 9.8 dB together with decreasing latency by 42.86% across dynamic conditions. User testing revealed that DWPN delivered important benefits including an 68% decrease in motion sickness and a satisfaction increase to 48.39% among users. Laboratory tests prove that DWPN technology simultaneously improves both wireless communication technical operation and ARlVR user perception quality. DWPN implementation within real-time systems will enable robust ARlVR applications with low latency as well as minimal disturbances in performance. Research will target the development of a scalable model for next-generation 6G networks designed to operate in real-time dynamic systems.