发表机构
University of Manitoba（曼尼托巴大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于正式协同设计理论的组合驱动方法论，以应对B6G网络中通信-控制协同设计的模块化、需求追踪和可行性分析挑战，并通过无线辅助机器人案例验证其与优化驱动方法的互补性。
AI 中文摘要
预期中的超越第六代（B6G）移动宽带网络应用将需要在网络架构内协同设计通信与控制子系统。现有的协同设计方法主要采用优化驱动的方式，通过联合优化问题集成子系统。虽然这种方法在相对简单的系统中有效，但此类公式在（i）模块化子系统表示，（ii）跨子系统追踪需求传播，以及（iii）系统分析设计空间可行性方面面临挑战，特别是当交互子系统的数量和复杂性增加时。在本文中，我们基于协同设计的正式理论提出了协同设计的组合视角。我们考察了该理论引入的组合概念如何应对传统优化驱动方法的挑战。基于这一视角，我们提出了一种用于B6G网络中通信-控制协同设计的组合驱动方法论，并通过一个无线辅助机器人控制案例研究加以说明。随后，我们讨论了组合驱动与优化驱动的协同设计方法如何互补，以及这种互补性为何可能对B6G网络有益。最后，我们指出了实际采用所提方法论的关键研究挑战和未来方向。
英文摘要
Anticipated applications of beyond sixth-generation (B6G) mobile broadband networks will require the co-design of communication and control subsystems within the network architecture. Existing co-design approaches are predominantly optimization-driven, integrating subsystems through joint optimization problems. While this approach is effective in relatively simple systems, such formulations present challenges in (i) modular subsystem representations, (ii) tracing the propagation of requirements across subsystems, and (iii) systematically analyzing design-space feasibility, particularly as the number and complexity of interacting subsystems increase. In this article, we present a compositional perspective on co-design based on the formal theory of co-design. We examine how the notion of composition introduced by this theory can address the challenges of the conventional optimization-driven approach. Building on this perspective, we propose a composition-driven methodology for communication-control co-design in B6G networks and illustrate it through a wireless-assisted robotic control case-study. We then discuss how the composition-driven and optimization-driven co-design approaches can complement each other and why this complementarity may be beneficial for B6G networks. Finally, we identify key research challenges and future directions toward the practical adoption of the proposed methodology.
