通过运营商间资源和服务共享实现共生 STIN:用户协会和无线电资源的联合协调

Shizhao He;Jungang Ge;Ying-Chang Liang;Dusit Niyato
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引用次数: 0

摘要

在即将到来的 6G 时代,空地一体化网络(STIN)是支持泛在连接的关键架构。运营商间的资源和服务共享是实现这一庞大网络、高效利用资源和降低建设成本的有效途径。鉴于运营商的合理性,STIN 中的资源和服务配置应同时关注系统的整体性能和运营商的个体利益。受新兴的共生通信促进不同无线电系统间互利的启发,我们在本文中从共生通信的角度研究了 STIN 中的资源和服务共享。我们特别考虑了由地面网络运营商(GNO)和卫星网络运营商(SNO)组成的 STIN。具体来说,我们的目标是通过联合优化用户关联、资源分配和波束成形,最大化整个 STIN 的加权和速率(WSR)。此外,我们还引入了共享系数来表征运营商的收益。如果只关注 WSR 的最大化,运营商可能会遭受收益损失。为了追求互利,我们提出了互利约束 (MBC),以确保每个运营商都能获得收益。然后,我们开发了一种基于连续凸近似(SCA)方法的集中算法。考虑到集中式算法难以实现,我们提出了一种基于拉格朗日对偶分解和共识交替方向乘法(ADMM)的分布式算法。最后,我们进行了大量的数值模拟来证明这两种算法的有效性,分布式优化算法的性能接近集中式算法。研究结果还表明,所提出的 MBC 能够使运营商实现互利共赢,实现共生的资源和服务共享模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Toward Symbiotic STIN Through Inter-Operator Resource and Service Sharing: Joint Orchestration of User Association and Radio Resources
The space-terrestrial integrated network (STIN) is a pivotal architecture to support ubiquitous connectivity in the upcoming 6G era. Inter-operator resource and service sharing is a promising way to realize such a huge network, utilizing resources efficiently and reducing construction costs. Given the rationality of operators, the configuration of resources and services in STIN should focus on both the overall system performance and individual benefits of operators. Motivated by emerging symbiotic communication facilitating mutual benefits across different radio systems, we investigate the resource and service sharing in STIN from a symbiotic communication perspective in this paper. In particular, we consider a STIN consisting of a ground network operator (GNO) and a satellite network operator (SNO). Specifically, we aim to maximize the weighted sum rate (WSR) of the whole STIN by jointly optimizing the user association, resource allocation, and beamforming. Besides, we introduce a sharing coefficient to characterize the revenue of operators. Operators may suffer revenue loss when only focusing on maximizing the WSR. In pursuit of mutual benefits, we propose a mutual benefit constraint (MBC) to ensure that each operator obtains revenue gains. Then, we develop a centralized algorithm based on the successive convex approximation (SCA) method. Considering that the centralized algorithm is difficult to implement, we propose a distributed algorithm based on Lagrangian dual decomposition and the consensus alternating direction method of multipliers (ADMM). Finally, we provide extensive numerical simulations to demonstrate the effectiveness of the two proposed algorithms, and the distributed optimization algorithm can approach the performance of the centralized one. The results also reveal that the proposed MBCs can enable operators to achieve mutual benefits and realize a symbiotic resource and service sharing paradigm.
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