AI Enabled 6G for Semantic Metaverse: Prospects, Challenges and Solutions for Future Wireless VR

TL;DR

This paper proposes AI-enabled wireless techniques for 6G to support immersive VR, using advanced transceivers, optimization, and deep reinforcement learning to significantly improve data rates and power efficiency.

Contribution

It introduces a novel AI-driven resource allocation framework for 6G VR networks, combining optimal transceivers, advanced optimization, and DRL for real-time performance.

Findings

Enhanced sum data rate by up to 39% over industry standards

Achieved power savings of up to 75% for VR data transmission

DRL-based framework is 5x faster and reaches 83% of the optimal performance

Abstract

Wireless support of virtual reality (VR) has challenges when a network has multiple users, particularly for 3D VR gaming, digital AI avatars, and remote team collaboration. This work addresses these challenges through investigation of the low-rank channels that inevitably occur when there are more active users than there are degrees of spatial freedom, effectively often the number of antennas. The presented approach uses optimal nonlinear transceivers, equivalently generalized decision-feedback or successive cancellation for uplink and superposition or dirty-paper precoders for downlink. Additionally, a powerful optimization approach for the users' energy allocation and decoding order appears to provide large improvements over existing methods, effectively nearing theoretical optima. As the latter optimization methods pose real-time challenges, approximations using deep reinforcement learning (DRL) are used to approximate best performance with much lower (5x at least) complexity. Experimental results show significantly larger sum rates and very large power savings to attain the data rates found necessary to support VR. Experimental results show the proposed algorithm outperforms current industry standards like orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), as well as the highly researched methods in multi-carrier NOMA (MC-NOMA), enhancing sum data rate by 39%, 28%, and 16%, respectively, at a given power level. For the same data rate, it achieves power savings of 75%, 45%, and 40%, making it ideal for VR applications. Additionally, a near-optimal deep reinforcement learning (DRL)-based resource allocation framework for real-time use by being 5x faster and reaching 83% of the global optimum is introduced.