THz RHS Transceiver for Low-Latency Multi-User VR Transmission with MEC

2026-03-02

AI Summary

This work addresses the high end-to-end latency in multi-user virtual reality (VR) systems under resource constraints, particularly in mobile scenarios with heterogeneous fields of view (FoVs). To tackle this challenge, the authors propose an efficient transmission architecture that integrates terahertz communication, mobile edge computing (MEC), and reconfigurable holographic surfaces (RHS). By jointly optimizing content prefetching, rendering offloading, and holographic beamforming—and introducing a holographic pattern division multiple access (HDMA) mechanism—the complex joint optimization problem is decomposed across time scales into combinatorial and convex subproblems. Closed-form solutions are further derived for the special case of homogeneous FoVs. Experimental results demonstrate that the proposed approach significantly reduces end-to-end latency under stringent memory and power constraints, thereby enhancing system responsiveness and resource utilization efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Hardware-aware ML
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

This paper investigates a Terahertz (THz)-enabled mobile edge computing (MEC)-assisted virtual reality (VR) system using reconfigurable holographic surfaces (RHS) as transceiver for multi-user beamforming and holographic-pattern division multiple access (HDMA). We develop an end-to-end model for the 3D field-of-view (FoV) generation pipeline and optimize content prefetching, rendering offloading under memory and power constraints, and beamforming accommodating user movement by adjusting holographic pattern weights for beamshaping and feeds power allocation for excitation amplitude adjustment. For homogeneous FoVs, we derive closed-form policies for prefetching 2D or 3D FoVs or direct transmission of 3D FoVs. For heterogeneous FoVs, we exploit the timescale separation between prefetching/rendering and fast RHS beamforming, decomposing the optimization into a rendering-prefetching combinatorial optimization problem and a short-timescale beamforming convex optimization problem. Simulations show significant latency reductions under tight resource constraints.

Problem

• THz
• MEC
• VR
• RHS
• low-latency

Innovation

• Reconfigurable Holographic Surfaces (RHS)
• Terahertz (THz) communication
• Holographic-Pattern Division Multiple Access (HDMA)
• Mobile Edge Computing (MEC)
• Low-latency VR