# Statistical QoS Provision in Business-Centric Networks

arXiv: https://arxiv.org/abs/2408.15609 · HTML: https://mlsanity.com/paper/2408.15609/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 28 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [6G Network AI Architecture for Everyone-Centric Customized Services](https://mlsanity.com/paper/2205.09944/) (2022)
- [Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications](https://mlsanity.com/paper/2212.14749/) (2022)
- [User-centric Cell-free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions](https://mlsanity.com/paper/2104.14589/) (2021)
- [Downlink Resource Allocation in Multiuser Cell-free MIMO Networks with User-centric Clustering](https://mlsanity.com/paper/2108.06316/) (2021)
- [Cross-Layer Optimization for Statistical QoS Provision in C-RAN with Finite-Length Coding](https://mlsanity.com/paper/2311.09879/) (2023)
- [Distributed Resource Allocation Optimization for User-Centric Cell-Free MIMO Networks](https://mlsanity.com/paper/2110.08154/) (2021)
- [Uplink resource allocation optimization for user-centric cell-free MIMO networks](https://mlsanity.com/paper/2406.05576/) (2024)
- [User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks](https://mlsanity.com/paper/2302.01471/) (2023)
- [Statistical QoS Provisioning Analysis and Performance Optimization in xURLLC-enabled Massive MU-MIMO Networks: A Stochastic Network Calculus Perspective](https://mlsanity.com/paper/2302.10092/) (2023)
- [Streaming 360-degree VR Video with Statistical QoS Provisioning in mmWave Networks from Delay and Rate Perspectives](https://mlsanity.com/paper/2305.07935/) (2023)

## Built on by (most cited first, 1 of 1)

- [AoI-Aware Task Offloading and Transmission Optimization for Industrial IoT Networks: A Branching Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2510.16414/) (2025)

## Abstract (arXiv)

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 (COHA-ES). Power and sub-channel (SC) Actors representing multiple APs are jointly optimized under the unified guidance of a common critic. Additionally, we introduce a novel multithreaded experience-sharing mechanism to accelerate training and enhance rewards. Extensive comparative experiments validate the effectiveness of our DRL framework in terms of convergence and efficiency. Moreover, comparative analyses demonstrate the comprehensive advantages of the BCN structure in enhancing both spectral and energy efficiency.