IoT-based semantic PCA for extended reality communications

This work addresses the challenge of efficient Extended Reality (XR) communication in 6 G networks, where signals acquired by IoT sensors are transmitted for remote XR content reconstruction. Given the energy constraints of IoT devices and the low-latency demands of XR services, lightweight and fast encoding is essential. We propose a Semantically Weighted Principal Component Analysis (SWPCA), which selects components based on their relevance to XR rendering objectives-such as preserving object shape or motion. SWPCA applies data-dependent weights that reflect the saliency of sensor signals for reconstructing XR objects from the measurements. We analytically introduce SWPCA and apply it to XR gesture representation using wearable IoT measurements. We evaluate SWPCA performances within both an ideal and a realistic 6G end-to-end communication framework. SWPCA significantly reduces transmission bandwidth while preserving critical information for accurate XR gesture reconstruction. SWPCA provides a robust and fast encoding solution for real-time XR applications in energy/bandwidth constrained environments.