The transition of cellular networks to (i) software-based systems on commodity hardware and (ii) platforms for services beyond connectivity introduces critical system-level challenges. As sensing emerges as a key feature toward 6G standardization, supporting Integrated Sensing and Communication (ISAC) with limited bandwidth and piggybacking on communication signals, while maintaining high reliability and performance, remains a fundamental challenge. In this paper, we provide two key contributions. First, we present a programmable, open-source framework for processing PHY/MAC signals through real-time, GPU-accelerated Artificial Intelligence (AI) applications on the edge Radio Access Network (RAN) infrastructure. Building on the Open RAN dApp architecture, the framework interfaces with a GPU-accelerated gNB based on NVIDIA Aerial Testbed (ATB), feeding PHY/MAC data to custom AI logic with a framework overhead of 150 us, multiple inference engines, and support for several AI backends. We evaluate the framework on multiple GPU platforms with and without hardware-level GPU isolation. Second, we demonstrate the framework capabilities through cuSense, an indoor localization dApp that consumes uplink DMRS channel estimates, removes static multipath components, and runs a neural network to infer the position of a moving person. Evaluated on a 3GPP-compliant 5G NR deployment, cuSense achieves a mean localization error of 77 cm, with 75% of predictions falling within 1 meter, without dedicated sensing hardware or modifications to the RAN stack or signals. The framework is released as open source, providing a reference design for future AI-native RANs and ISAC applications.

翻译：蜂窝网络向（i）基于通用硬件的软件化系统以及（ii）超越连接性的服务平台过渡，带来了关键的系统级挑战。随着感知成为6G标准化的关键特性，如何在有限带宽下借助通信信号实现集成感知与通信（ISAC），同时保持高可靠性和高性能，仍是一项根本性挑战。本文提出两项核心贡献。首先，我们构建了一个可编程开源框架，支持在边缘无线接入网络（RAN）基础设施上，通过实时GPU加速人工智能（AI）应用处理PHY/MAC信号。该框架基于开放RAN dApp架构，与基于NVIDIA Aerial测试平台（ATB）的GPU加速gNB进行接口交互，以150微秒的框架开销将PHY/MAC数据馈送至定制AI逻辑，支持多种推理引擎及多个AI后端。我们评估了该框架在多个GPU平台上（含/不含硬件级GPU隔离）的性能表现。其次，通过cuSense室内定位dApp展示了框架能力：该应用利用上行链路DMRS信道估计，移除静态多径分量，并运行神经网络推断移动目标位置。在符合3GPP规范的5G NR部署中评测显示，cuSense平均定位误差为77厘米，75%的预测结果误差在1米以内，且无需专用感知硬件或对RAN协议栈/信号进行修改。该框架已作为开源发布，为未来AI原生RAN及ISAC应用提供参考设计。