IPOSEE: Intelligent and Proactive Optimisation for Service-centric Wireless Networks (UoS joint proposal lead partner)

IPOSEE：以服务为中心的无线网络的智能和主动优化（UoS 联合提案牵头合作伙伴）

• 批准号: EP/X038971/1
• 负责人: Xiaoli Chu
• 金额: $ 26.77万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX038971%2F1
• 关键词: IPOSEE; Intelligent; Proactive; Optimisation; Service

Wireless networks have become indispensable to citizens, enterprises and vertical industries, e.g., transport (including autonomous vehicles and drones), logistics, utilities and manufacturing, because wireless connectivity is essential to the digital transformation of industrial and business processes and customer experiences. As a result, wireless networks are facing increasingly diverse service requirements, which indicate that existing reactive network management will be insufficient, while intelligent and proactive control of service-centric networks becomes essential. Future intelligent wireless networks rely on several multidisciplinary breakthroughs: (i) Artificial Intelligence (AI) algorithms that can accurately predict spatial-temporal patterns of service demand and thereby drive proactive optimisation of wireless networks, (ii) reconfigurable radio access networks (RAN) and wireless environments, and (iii) automated wireless service provisioning with reduced cost, improved performance and greater reliability. Current research to automate the optimisation of service-centric wireless networks using data-driven AI is facing many open challenges that need to be urgently addressed. In this project, we will take advantage of growing data availability and advanced data science technologies, as well as AI algorithms and techniques to deliver the above identified multidisciplinary breakthroughs, thereby enabling reliable automated wireless service provisioning.

无线网络已成为公民、企业和垂直行业（例如交通（包括自动驾驶汽车和无人机）、物流、公用事业和制造业）不可或缺的一部分，因为无线连接对于工业和业务流程以及客户体验的数字化转型至关重要。因此，无线网络面临着日益多样化的业务需求，这表明现有的被动式网络管理已经不够，而以业务为中心的网络的智能和主动控制变得至关重要。未来的智能无线网络依赖于多项多学科突破：(i) 人工智能 (AI) 算法，可以准确预测服务需求的时空模式，从而推动无线网络的主动优化；(ii) 可重构无线接入网络 (RAN) 和无线环境，以及 (iii) 自动化无线服务供应，降低成本、提高性能和提高可靠性。当前使用数据驱动的人工智能自动优化以服务为中心的无线网络的研究面临着许多亟待解决的开放挑战。在这个项目中，我们将利用不断增长的数据可用性和先进的数据科学技术以及人工智能算法和技术来实现上述多学科突破，从而实现可靠的自动化无线服务配置。

项目成果

MADDPG-Based Joint Service Placement and Task Offloading in MEC Empowered Air-Ground Integrated Networks

MEC授权空地综合网络中基于MADDPG的联合服务部署和任务卸载

• DOI: http://dx.10.1109/jiot.2023.3326820
• 发表时间: 2024
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Du J

On the Performance of an Integrated Communication and Localization System: An Analytical Framework

综合通信和定位系统的性能：分析框架

• DOI: http://dx.10.1109/tvt.2024.3364257
• 发表时间: 2024
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: Gao Y

Performance Analysis for Ambient Backscatter Communications With Hybrid Long-Short Packets

使用混合长短数据包的环境反向散射通信的性能分析

• DOI: 10.1109/lwc.2024.3369067
• 发表时间: 2024-05-01
• 期刊: IEEE Wireless Communications Letters
• 影响因子: 6.3
• 作者: Jiayao Shi;Liqin Shi;Yinghui Ye;Yuzhi Zhang;Xiaoli Chu
• 通讯作者: Xiaoli Chu