Channels4World：面向无线基础模型的环境级数据集发布

数据集总规模达 524.3 亿个基站-UE 物理配对和 1048.6 亿次频率特定的信道评估。其核心是场景中心化设计：几何结构、设备布设、信道数据与多径记录在同一场景内跨频段对齐，使环境广度与场景内观测密度可以独立调节。论文还提供确定性的 90K/10K 场景级训练-测试划分，并公开多个规模的预定义子集。

实验结果显示，在相同训练曝光量下，用更广泛场景集合训练的模型在留出场景上表现更好，但等回合对照实验表明场景数量本身无法完全解释这一增益。衍射分析表明其显著影响保留链路的可用性与多径结构，而路径保留策略可在几乎不损失聚合功率表征的前提下大幅降低存储开销。

CHANNELS4WORLD: An Environment-Scale Dataset for Wireless Foundation Models

Wireless foundation models learn reusable representations across tasks, configurations, and deployment environments. Their pretraining requires environmental breadth, yet wireless datasets often sample few sites densely. We introduce Channels4World, a scene-centered dataset of 100,000 distinct outdoor RF scenes and a procedural method for generating them. Each scene links a generated environment and RF material assignments to 32 base-station (BS) positions, 16,384 UE positions, and aligned variable-length multipath and ordered interaction records at 3.5 and 7~GHz. The corpus comprises 52.43 billion physical BS-UE pairs, 104.86 billion frequency-specific evaluations, and a 29.14-TB packaged payload. Scene identity aligns geometry, placements, channels, and interactions across frequencies, allowing environmental breadth and within-scene observation density to vary independently. The public release offers predefined subsets at multiple scales and separate access to corpus components. A deterministic 90K/10K scene-level train-test split keeps both frequencies and all scene components together. We characterize morphology, RF materials, and transceiver placement across three generation profiles. Controlled propagation and path-retention analyses show that diffraction materially affects retained-link availability and multipath structure, while the retention policy reduces storage substantially with little loss in represented aggregate power. Under matched training exposure, models trained on broader scene collections perform better on held-out scenes, although an equal-episode control shows that scene count alone cannot explain the gain. By making environmental breadth explicit and controllable, Channels4World supports the development and evaluation of wireless foundation models for generalization across deployment environments.

This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include Channels4World, 无线基础模型, 数据集, 信道建模.

Source attribution: curated from arXiv cs.LG (@Joonkyu Jang, Hyung-Joo Moon, Kwang Soon Kim, Seong-Lyun Kim, Jakob Hoydis, Robert W. Heath,, Chan-Byoung Chae).