AI-Based Timing Synchronization for Wireless RAN using Altera Agilex™ 7 SoC FPGAs

Eliminating GNSS dependency with intelligent holdover and adaptive clock correction on FPGA 

Accurate timing is foundational to the performance and stability of today’s Radio Access Networks (RAN). From coordinated multi-point (CoMP) transmissions to low-latency scheduling and synchronization across base stations, wireless infrastructure depends on precise frequency and phase alignment. 

Traditionally, this synchronization is achieved through GNSS, PTP, and SyncE protocols. However, when GNSS signals are interrupted—due to urban canyon effects, indoor deployment, jamming, or spoofing—systems must revert to holdover, often leading to degraded accuracy, increased jitter, and service disruptions. 

AI-Enhanced Holdover: Predicting Clock Drift with Machine Learning 

Altera’s innovative approach introduces AI-driven timing holdover using Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) neural networks trained to recognize and predict clock drift patterns in real time. These models are deployed directly onto Agilex™ 7 SoC FPGAs, ensuring ultra-low-latency adaptation during GNSS signal loss. 

By adjusting the Digital Phase-Locked Loop (DPLL) dynamically based on learned environmental behavior, this method: 

• Maintains stable frequency synchronization during GNSS outages 

• Reduces power and maintenance needs by up to 10x 

• Adapts to temperature, voltage, and aging-induced oscillator drift 

• Ensures precise, real-time clock correction for next-generation RAN deployments 

Built for Resilience in Open and Edge RAN 

Developed using MATLAB and implemented with Altera’sFPGA AI Suite and Quartus® Prime software and PTP Servo IP, this solution is validated through multi-day drift simulations and stress-tested across environmental variables. It delivers consistent timing resilience even in non-ideal deployment environments—making it well-suited for Open RAN, private 5G, and remote edge installations where GNSS isn’t guaranteed. 

FPGAi: Intelligence Where It Matters 

As networks push further to the edge, and timing challenges become more dynamic, FPGAi enables system architects to embed intelligence within hardware to autonomously adapt. This AI-native synchronization solution is a prime example of how programmable logic and neural inference work together to improve RAN reliability and reduce the Total Cost of Ownership (TCO). 

Watch the full demo to see how AI-based timing sync can transform your network architecture.

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