AI 中文总结
研究蜂窝网络中MAC调度器，提出开源框架MAC-Gyver，能在OpenAirInterface调度器内开发评估调度应用程序，通过类型化接口暴露观察与控制，保留底层协议和执行路径，评估两个用例展示其灵活性与功能。
AI 中文摘要
蜂窝网络正在将人工智能（AI）集成到无线接入网络控制中。MAC调度器是一个很有前景的目标，因为它在竞争的延迟、吞吐量和可靠性要求下，在每个时隙分配有限的资源频谱。然而，大多数基于学习的调度器仅在模拟中进行评估。生产调度器难以修改，实际压力测试需要的无线电硬件超出大多数实验室所能提供的。我们提出了MAC-Gyver，一个用于开发和评估直接在OpenAirInterface调度器内执行的调度应用程序的开源框架。它通过类型化接口公开调度器观察和控制，同时保留底层协议和实时执行路径。相同的应用程序可以在空中和mac-emu（一个无物理层模拟器）上运行，该模拟器以实时时隙速度在一台主机上为多达90个用户执行未修改的OpenAirInterface第2层堆栈，并具有符合3GPP的信道模型。为展示MAC-Gyver的灵活性，我们评估了两个用例。一个主动上行链路调度器预测数据包到达并将中位数往返延迟大致减半。一个频率选择上行链路调度器从每个PRB探测观察中选择连续子带，并针对离线调度上限在移动性和功率受限的操作点进行评估。它们共同展示了相同的生产堆栈如何成为一个AI游乐场，通过互补的调度用例支持实现、受控评估和空中验证。
英文摘要
Cellular networks are integrating Artificial Intelli- gence (AI) into radio access network control. The MAC scheduler is a promising target because it allocates a limited resource, spectrum, at every slot, under competing latency, throughput, and reliability requirements. However, most learning-based sched- ulers are evaluated only in simulation. Production schedulers are difficult to modify, and realistic stress tests require more radio hardware than most laboratories can provide. We present MAC-Gyver, an open-source framework for developing and evaluating scheduling applications that execute directly inside the OpenAirInterface scheduler. It exposes scheduler observations and controls through typed interfaces while preserving the underlying protocol and real-time execution paths. The same applications run over the air and in mac-emu, a PHY-less emulator that executes the unmodified OpenAirInterface Layer 2 stack for up to 90 users on one host at real-time slot pace, with a 3GPP-compliant channel model. To showcase the flexibility of MAC-Gyver, we evaluate two use cases. A proactive uplink scheduler predicts packet arrivals and roughly halves median round-trip latency. A frequency-selective uplink scheduler selects contiguous sub-bands from per-PRB sounding observations and is evaluated across mobility and power-limited operating points against an offline scheduling ceiling. Together, they show how the same production stack can be an AI playground that supports implementation, controlled evaluation, and over-the-air validation through complementary scheduling use cases.
