AI 中文总结
研究用于构建信道增益图的信道测量位置选择问题，基于高斯随机场理论开发自适应离散化策略，通过贪心算法和模拟退火算法解决组合优化问题，相比均匀离散化显著降低平均均方误差，建立空间测量理论框架并提供实际指导。
AI 中文摘要
信道知识图（CKM）被视为未来第六代（6G）网络的一项有前景的技术，有助于实现环境感知的无线通信、传感和定位。基于数据的CKM构建利用空间相关性原理，基于有限测量数据完成CKM，引发了“何处进行信道测量”的问题。本文研究了基于数据的高效CKM构建的空间测量策略，考虑了一种特定类型的CKM——信道增益图（CGM）。总体目标是选择信道测量位置的子集，以最小化全局CGM构建的平均均方误差（AMSE）。为将无限测量位置减少到有限集，将底层物理空间离散化为有限数量的立方网格点，并制定组合优化问题以从中选择测量位置。为解决该问题，采用了贪心算法和模拟退火（SA）两种代表性算法，并讨论了它们各自的优点。为克服传统均匀离散化的精度-复杂度权衡，从高斯随机场理论的角度开发了一种自适应离散化策略，以最小化从原始连续场到其近似离散表示在均方意义上的信息损失。与均匀离散化相比，所提出的自适应离散化策略在降低AMSE方面实现了显著的性能提升，建立了空间测量的理论框架并为实施提供了实际指导。
英文摘要
Channel knowledge map (CKM) is regarded as a promising technology for future sixth-generation (6G) networks, facilitating environmental-aware wireless communication, sensing, and localization. Research works on CKM construction can be classified as model-based methods and data-based approaches. Specifically, data-based CKM construction exploits the fundamental principle of spatial correlation to complete CKM based on limited measurement data, leading to the question of "where to perform channel measurements". In this paper, we study the spatial measurement strategy for efficient data-based CKM construction, and consider a specific type of CKM named channel gain map (CGM). The general objective is to select a subset of locations for channel measurements, so as to minimize the average mean-squared-error (AMSE) of the global CGM construction. In order to reduce the infinite measurement locations to a finite set, we discretize the underlying physical space into a finite number of cubic grid points, and formulate a combinatorial optimization problem to select measurement locations from them. In order to solve the proposed problem, we employ two representative algorithms, namely the greedy algorithm and the simulated annealing (SA), and discuss their respective advantages. To overcome the accuracy-complexity trade-off of traditional uniform discretization, we develop an adaptive discretization strategy from the viewpoint of Gaussian random field theory to minimize the information loss from the original continuous field to its approximated discrete representation in the mean-squared sense. Compared to uniform discretization, the proposed adaptive discretization strategy achieves a significant performance gain in terms of AMSE-reduction, establishing the theoretical framework of spatial measurement and providing practical guidance for implementation.
