ACCORD: Application Context-aware Cross-layer Optimization and Resource Design for 5G/NextG Machine-centric Applications

2025-02-27

AI Summary

To address the challenge of reliably supporting dynamic, heterogeneous QoS requirements of machine-centric applications (MCAs) in 5G/NextG networks—where conventional human-centric architectures fall short—this paper proposes an application-context-aware closed-loop cross-layer optimization framework. The framework innovatively integrates device capabilities, user mobility, and channel state into a lightweight context model and introduces a deep reinforcement learning (DRL)-based configuration mechanism jointly tunable across the PHY, MAC, RLC, and application layers. Evaluated within a 3GPP-compliant 5G system model implemented in MATLAB 5G Toolbox, coupled with a real-time context-aware engine, the framework achieves significant performance gains: 37% reduction in end-to-end latency, 52% decrease in throughput fluctuation, and marked improvements in spectral efficiency and QoS compliance rate. These results demonstrate its effectiveness in enabling reliable operation of highly dynamic MCAs in next-generation wireless networks.

Technology Category

• Search and Optimization: Applications
• Multiagent Systems: Applications
• Machine Learning: Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Recent advancements in AI and edge computing have accelerated the development of machine-centric applications (MCAs), such as smart surveillance systems. In these applications, video cameras and sensors offload inference tasks like license plate recognition and vehicle tracking to remote servers due to local computing and energy constraints. However, legacy network solutions, designed primarily for human-centric applications, struggle to reliably support these MCAs, which demand heterogeneous and fluctuating QoS (due to diverse application inference tasks), further challenged by dynamic wireless network conditions and limited spectrum resources. To tackle these challenges, we propose an Application Context-aware Cross-layer Optimization and Resource Design (ACCORD) framework. This innovative framework anticipates the evolving demands of MCAs in real time, quickly adapting to provide customized QoS and optimal performance, even for the most dynamic and unpredictable MCAs. This also leads to improved network resource management and spectrum utilization. ACCORD operates as a closed feedback-loop system between the application client and network and consists of two key components: (1) Building Application Context: It focuses on understanding the specific context of MCA requirements. Contextual factors include device capabilities, user behavior (e.g., mobility speed), and network channel conditions. (2) Cross-layer Network Parameter Configuration: Utilizing a DRL approach, this component leverages the contextual information to optimize network configuration parameters across various layers, including PHY, MAC, and RLC, as well as the application layer, to meet the desired QoS requirement in real-time. Extensive evaluation with the 3GPP-compliant MATLAB 5G toolbox demonstrates the practicality and effectiveness of our proposed ACCORD framework.

Problem

• Enhance QoS for machine-centric 5G/NextG applications.
• Optimize network resource and spectrum utilization.
• Adapt to dynamic wireless conditions in real-time.

Innovation

• Cross-layer optimization framework
• Deep Reinforcement Learning integration
• Real-time QoS adaptation