Statistical QoS Provision in Business-Centric Networks
Chang Wu, Yuang Chen, Hancheng Lu

TL;DR
This paper introduces a novel Business-Centric Network framework that leverages deep reinforcement learning to optimize resource allocation and QoS provisioning, significantly improving spectral and energy efficiency in wireless networks.
Contribution
It proposes a new cross-layer BCN model combined with a DRL framework (COHA-ES) for scalable, efficient QoS management in wireless communication.
Findings
DRL framework converges faster and yields higher rewards.
BCN structure enhances spectral and energy efficiency.
Joint optimization improves QoS metrics like throughput, delay, and reliability.
Abstract
More refined resource management and Quality of Service (QoS) provisioning is a critical goal of wireless communication technologies. In this paper, we propose a novel Business-Centric Network (BCN) aimed at enabling scalable QoS provisioning, based on a cross-layer framework that captures the relationship between application, transport parameters, and channels. We investigate both continuous flow and event-driven flow models, presenting key QoS metrics such as throughput, delay, and reliability. By jointly considering power and bandwidth allocation, transmission parameters, and AP network topology across layers, we optimize weighted resource efficiency with statistical QoS provisioning. To address the coupling among parameters, we propose a novel deep reinforcement learning (DRL) framework, which is Collaborative Optimization among Heterogeneous Actors with Experience Sharing…
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Taxonomy
TopicsCustomer churn and segmentation · Cognitive Computing and Networks · Mobile Agent-Based Network Management
Methodstravel james
