A Unified QoS-Aware Multiplexing Framework for Next Generation Immersive Communication with Legacy Wireless Applications

2025-04-30

AI Summary

In scenarios where immersive communications (e.g., AR/VR, holographic telepresence) coexist with traditional services (eMBB, URLLC), static network slicing causes throughput degradation and queue buildup. Method: This paper proposes a unified QoS-aware multiplexing framework featuring an adaptive adversarial slicing (Ad2S) mechanism, integrated with measurement extrapolation–Kalman filtering (ME-KF) to enable cross-timescale resource coordination under non-stationary channel conditions. A dynamic weighted short-term throughput optimization model is formulated based on Lyapunov drift theory, supporting tunable trade-offs between throughput and user experience. Contribution/Results: Experiments demonstrate an average throughput gain of 3.86 Mbps, a 63.96% reduction in end-to-end latency, and a 24.36% decrease in convergence time compared to baseline approaches.

Technology Category

• Machine Learning: Hardware-aware ML
• Multiagent Systems: Adversarial Agents
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Immersive communication, including emerging augmented reality, virtual reality, and holographic telepresence, has been identified as a key service for enabling next-generation wireless applications. To align with legacy wireless applications, such as enhanced mobile broadband or ultra-reliable low-latency communication, network slicing has been widely adopted. However, attempting to statistically isolate the above types of wireless applications through different network slices may lead to throughput degradation and increased queue backlog. To address these challenges, we establish a unified QoS-aware framework that supports immersive communication and legacy wireless applications simultaneously. Based on the Lyapunov drift theorem, we transform the original long-term throughput maximization problem into an equivalent short-term throughput maximization weighted by virtual queue length. Moreover, to cope with the challenges introduced by the interaction between large-timescale network slicing and short-timescale resource allocation, we propose an adaptive adversarial slicing (Ad2S) scheme for networks with invarying channel statistics. To track the network channel variations, we also propose a measurement extrapolation-Kalman filter (ME-KF)-based method and refine our scheme into Ad2S-non-stationary refinement (Ad2S-NR). Through extended numerical examples, we demonstrate that our proposed schemes achieve 3.86 Mbps throughput improvement and 63.96% latency reduction with 24.36% convergence time reduction. Within our framework, the trade-off between total throughput and user service experience can be achieved by tuning systematic parameters.

Problem

• Unified QoS framework for immersive and legacy wireless apps
• Optimizing throughput and latency via adaptive network slicing
• Dynamic resource allocation for varying channel conditions

Innovation

• Unified QoS-aware framework for immersive and legacy applications
• Ad2S scheme for invarying channel statistics networks
• ME-KF-based method for tracking network channel variations