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

2025-07-25

AI Summary

In multi-user wireless VR applications for 6G semantic metaverse—such as 3D VR gaming, AI avatars, and remote collaboration—conventional transceiver designs suffer severe spectral and energy efficiency degradation when the number of users exceeds the number of base station antennas, resulting in low-rank channel conditions. To address this, we propose a joint optimization framework integrating nonlinear transceivers and resource allocation, combining generalized decision-feedback equalization, superposition coding, and dirty-paper precoding. We further introduce a deep reinforcement learning (DRL) agent to enable real-time, near-optimal power allocation and decoding order design, reducing computational complexity by over 5×. Compared to OMA, NOMA, and MC-NOMA, our method improves sum rate by 39%, 28%, and 16%, respectively, and reduces power consumption by 75%, 45%, and 40% at the same target rate. The DRL policy achieves 83% of the global optimal performance with a 5× speedup in solution time.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Search and Retrieval-Augmented AI: Web search models and ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Addresses wireless VR challenges in multi-user 6G networks
• Optimizes energy allocation and decoding order for VR applications
• Uses DRL for real-time resource allocation with low complexity

Innovation

• Optimal nonlinear transceivers for uplink and downlink
• Deep reinforcement learning for real-time optimization
• Energy allocation and decoding order optimization