Current and future applications demand ultra-low latency and consistent throughput, yet frequently traverse 5G cellular networks, so cope with volatile packet dynamics, as 5G base station schedulers dynamically react to user workloads and wireless channel conditions. The task of evaluating network algorithms in these environments is hamstrung by current tools: record-and-replay emulators sever the feedback interaction that exists between application end points and a commercial operator's proprietary 5G scheduler, while full-stack simulators rely on overly simplistic scheduling logic. To bridge this reality gap, we present NeuralEmu, a high-fidelity, machine learning-based emulation framework that learns complex 5G scheduler resource allocation behaviors directly from extremely high-resolution network telemetry tools. The first emulator to handle multiple clients, NeuralEmu utilizes machine learning to dynamically predict resource block allocations and modulation schemes based on instantaneous user buffer occupancy and channel states. To capture realistic cross-user contention, a traffic reconstruction model inverts cellular network scheduling results to recover the underlying traffic patterns of uncontrolled background users. Implemented as an high-performance Linux middlebox emulator, NeuralEmu reduces emulation error relative to the state of the art for various network applications including but not limited to 55% for web-page load time, 57% for WebRTC encoder bit rate, and 51% for cloud gaming packet one-way delay, providing an accurate, standardized testing ground for tomorrow's real-time interactive network protocols and applications.

翻译：当前及未来的应用对超低延迟和一致吞吐量提出了严苛要求，然而这些应用常需穿越5G蜂窝网络，因而需应对数据包动态波动——5G基站调度器会根据用户工作负载和无线信道条件动态作出反应。在此类环境中评估网络算法的任务受限于现有工具：记录回放仿真器割裂了应用端点与商业运营商专有5G调度器之间的反馈交互，而全栈模拟器则依赖过于简化的调度逻辑。为弥合这一现实鸿沟，我们提出NeuralEmu——一种基于机器学习的高保真仿真框架，它可直接从超高分辨率网络遥测工具中学习复杂5G调度器的资源分配行为。作为首个支持多客户端的仿真器，NeuralEmu利用机器学习基于瞬时用户缓冲区占用率和信道状态动态预测资源块分配与调制方案。为捕获真实的跨用户竞争，流量重构模型通过逆向蜂窝网络调度结果，恢复不受控背景用户底层的流量模式。NeuralEmu实现为高性能Linux中间盒仿真器，在各类网络应用中将仿真误差较现有技术降低：网页加载时间降低55%、WebRTC编码器比特率降低57%、云游戏数据包单向延迟降低51%，从而为未来实时交互式网络协议与应用提供精确且标准化的测试平台。