NeuralEmu - Transforms 5G Interactive Application Testing, Slashing RAN Emulation Error by 5x

Background

The proliferation of interactive applications, such as cloud gaming, augmented reality, and autonomous robotics, demands networks capable of delivering high bandwidth and ultra-low latency. While 5G networks are designed to meet these requirements, their link performance remains highly volatile due to dynamic channel fading, user mobility, and complex, proprietary Radio Access Network (RAN) scheduling algorithms. Consequently, developers face a critical need for robust evaluation environments. Accurately testing how network protocols behave under fluctuating 5G conditions is essential to ensure seamless user experiences and optimize congestion control before real-world deployment.

Despite this need, existing evaluation methodologies suffer from significant limitations. Live network testing captures authentic conditions but fundamentally lacks reproducibility and scalability. Alternatively, full-stack simulators rely on oversimplified, non-commercial scheduler models and incur prohibitive computational overhead. Furthermore, state-of-the-art trace-driven emulators treat cellular link capacity as a static trace, which critically breaks the closed-loop causality between the application and the network. In reality, an application’s transmission pattern dictates RAN buffer occupancy, prompting base stations to dynamically adjust resource allocation. Because static emulators ignore this bidirectional feedback loop, they fail to accurately capture multi-user contention, ultimately distorting application performance metrics.

Technology

The machine learning-based 5G network emulation system replicates black-box cellular base station scheduling to dynamically regulate packet release. Operating as a Linux middlebox with GPU-accelerated inference, it utilizes a dual-submodel neural scheduler. This scheduler features a physical resource block allocation model using recurrent encoders to capture cross-user contention, alongside a modulation and coding scheme selection model for channel state. Additionally, a traffic reconstruction model infers background users’ ingress data to accurately simulate multi-user environments. The system calculates these outputs to determine per-user transmission budgets while emulating time-division duplex slot patterns, packet loss, and retransmission delays.

This solution is uniquely differentiated because it overcomes the limitations of traditional trace-driven emulators that treat cellular link capacity as a static trace. Legacy tools break the closed-loop causality between an application’s send pattern and the network’s scheduling decisions. In contrast, this technology functions as a digital twin that preserves closed-loop, multi-user feedback dynamics at sub-millisecond timescales. It achieves up to a 5x reduction in emulation error compared to static tools. By accurately capturing the bidirectional causality between application dynamics and commercial radio access network schedulers, it provides a scalable, repeatable foundation for benchmarking next-generation network protocols.

Benefits

• Preserves closed-loop causal feedback between dynamic application traffic and real-time network scheduling decisions- Accurately models multi-user contention and background traffic dynamics using a traffic reconstruction model- Provides high-fidelity emulation at sub-millisecond granularity including slot patterns and retransmission delays- Achieves high-throughput and low-latency packet processing through kernel-bypass sockets and GPU-accelerated inference- Reduces overall emulation error by up to five times compared to static record-and-replay tools- Improves emulation accuracy for interactive applications such as web browsing, video streaming, and cloud gaming- Delivers a scalable, repeatable, and vendor-agnostic testing environment for next-generation network protocols

Applications

• Cloud gaming performance testing- Augmented reality application testing- 5G congestion control benchmarking- Autonomous robotics network testing- Video streaming adaptation testing

Additional info

This machine learning-based 5G network emulator replicates black-box cellular scheduling to preserve closed-loop application-RAN dynamics. Using AF_XDP kernel-bypass and GPU inference, it features a background traffic reconstructor and a dual-submodel neural scheduler with recurrent and attention layers. The scheduler predicts physical resource block allocations and modulation schemes from buffer states and channel metrics, dynamically establishing per-user transmission budgets while simulating TDD patterns, packet loss, and HARQ/RLC retransmission latency at sub-millisecond granularity.