通过运营商间资源与服务共享迈向共生SAGIN：用户关联与无线资源的联合编排
Towards Symbiotic SAGIN Through Inter-operator Resource and Service Sharing: Joint Orchestration of User Association and Radio Resources
AI总结：
本文从共生通信视角研究SAGIN中运营商间资源与服务共享，通过联合优化用户关联、资源分配和波束成形最大化系统加权和速率，并引入互惠互利约束及分布式算法确保各运营商收益。
摘要：
空间-空-地一体化网络（SAGIN）是支持即将到来的6G时代泛在连接的关键架构。运营商间的资源与服务共享是实现如此庞大网络的一种有前景的方式，能够高效利用资源并降低建设成本。鉴于运营商的理性，SAGIN中的资源和服务配置应同时关注整体系统性能和运营商的个体收益。受促进不同无线系统间互惠互利的新兴共生通信启发，本文从共生通信的角度研究SAGIN中的资源与服务共享。具体而言，我们考虑由地面网络运营商（GNO）和卫星网络运营商（SNO）组成的SAGIN。具体来说，我们旨在通过联合优化用户关联、资源分配和波束成形，最大化整个SAGIN的加权和速率（WSR）。此外，我们引入共享系数来表征运营商的收入。仅关注最大化WSR时，运营商可能会遭受收入损失。为了追求互惠互利，我们提出了一种互惠互利约束（MBC），以确保每个运营商都能获得收入收益。然后，我们开发了一种基于逐次凸逼近（SCA）方法的集中式算法。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乘子共识交替方向法（ADMM）的分布式算法。最后，我们提供了广泛的数值仿真，以证明所提出的两种算法的有效性，并且分布式优化算法能够逼近集中式算法的性能。
英文摘要：
The space-air-ground integrated network (SAGIN) 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 SAGIN 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 SAGIN from a symbiotic communication perspective in this paper. In particular, we consider a SAGIN 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 SAGIN 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
