AI 中文总结
针对无蜂窝网络混合离散-连续优化问题，提出基于图分解的RAN框架，利用汉明拓扑邻域建模，引入服务状态图抽象，设计GBSE算法，用于RAN级能效最大化，结果显示该方法在可扩展性等方面有优势且性能优于现有技术。
AI 中文摘要
本文为以用户为中心的无蜂窝大规模多天线网络中出现的混合离散-连续优化问题开发了一种无线接入网络（RAN）框架。该新颖框架通过将可行服务状态空间建模为具有汉明拓扑邻域的图，利用离散聚类决策和连续资源分配变量之间的结构分解。引入服务状态图抽象以实现拓扑感知搜索与评估优化过程，并设计了基于图的搜索与评估（GBSE）算法及其复杂度分析。将RAN级别的能效最大化作为应用与所提出的框架和GBSE算法一起考虑。数值结果表明，最小汉明邻域在基于图的优化中提供了可扩展性和探索能力之间的诱人权衡，并且GBSE优于现有技术。
英文摘要
This letter develops a radio access network (RAN) framework for mixed discrete-continuous optimization problems that arise in user-centric cell=free massive multiple-antenna networks. The novel framework exploits the structural decomposition between discrete clustering decisions and continuous resource allocation variables by modeling the space of feasible serving states as a graph with Hamming-topology neighborhoods. A serving-state graph abstraction is introduced to enable topology-aware search-and-evaluate optimization procedures and a graph-based search-and-evaluate (GBSE) algorithm is devised along with their complexity analysis. Energy efficiency maximization at the RAN level is presented as an application of considered alongside the proposed framework and GBSE algorithm. Numerical results show that minimal Hamming neighborhoods offer an attractive trade-off between scalability and exploration capability in grap-based optimization and GBSE outperforms existing techniques.
Comments6 pages, 1 figure
