Idea
Predictive 5G uplink scheduling platform cutting URLLC latency by 50% with 90% less resource overhead.
Research Paper
Core Innovation
This paper introduces AUGUSTE, an online machine learning-based MAC scheduler that predicts uplink packet arrivals to proactively allocate resources before scheduling requests. Unlike Configured Grant methods limited to periodic traffic, AUGUSTE adapts dynamically to diverse URLLC patterns, balancing learning and confident scheduling phases to optimize latency and resource overhead trade-offs.
Why It Matters
5G URLLC applications require ultra-low latency and high reliability, but current uplink scheduling incurs significant delays and resource waste. AUGUSTE reduces round-trip latency to meet stringent 5G targets while drastically lowering resource consumption, enabling scalable, efficient support for industrial automation, V2X, and edge control systems. This improves network responsiveness and cost-efficiency for critical real-time services.
Market Size (TAM)
$20–50B TAM for 5G URLLC network infrastructure; $2–10B SAM from telecom operators and industrial IoT sectors. Driven by growing demand for real-time wireless control and autonomous systems.
Potential Customers & Pain Points
- Telecom operators – Need to meet URLLC latency SLAs efficiently
- Industrial automation firms – Require reliable low-latency wireless control
- Automotive OEMs and V2X providers – Need ultra-responsive vehicle communication
- Edge computing providers – Demand optimized uplink scheduling for real-time inference.
Business Model
Licensing the AUGUSTE scheduling software to telecom operators and network equipment manufacturers; offering integration and customization services for industrial and automotive clients.
Competitive Landscape
- Configured Grant Scheduling solutions
- 5G network equipment vendors
- URLLC optimization startups
Implementation Challenges
- Integration complexity with existing 5G network stacks
- Adoption resistance due to cross-layer synchronization challenges
- Need for real-world validation across diverse URLLC use cases
Validation Strategy
- Deploy AUGUSTE in multiple 5G operator testbeds to measure latency and overhead improvements
- Pilot with industrial automation and V2X partners to validate real-world performance
- Collect long-term operational data to refine machine learning models and demonstrate scalability
Research Paper Overview
AUGUSTE: Online-Learning dApp for Predictive URLLC Scheduling
Summary
AUGUSTE is a learning-based MAC scheduling framework that predicts uplink packet arrivals to proactively allocate 5G resources, reducing latency and overhead. It achieves median round-trip times around 10 ms, halving the baseline latency while using only 7-10% resource overhead, validated on a real 5G testbed with diverse URLLC traffic patterns.