NeuralEmu: in situ Measurement-Driven, ML-based, High-Fidelity 5G Network Emulation

ArXi:2604.26080v1 Announce Type: cross 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.