In high-density wireless systems like massive MIMO (mMIMO), channel estimation becomes more than a performance metric; it becomes a critical component of system efficiency. As the spectrum becomes more congested and mobile environments become more dynamic, traditional estimation methods struggle to keep up with the speed and complexity of modern networks.
Altera’s FPGAi solution brings a new level of intelligence and efficiency to this process. Using Robust Neural Networks deployed on Agilex™ SoC FPGAs, we deliver fast and accurate channel coefficient estimation with significantly lower hardware usage and latency compared to traditional methods like Minimum Mean Square Error (MMSE).
Neural Networks Meet Next-Gen RAN
Conventional estimation approaches are challenged by their sensitivity to real-world noise and computational intensity, especially as antenna counts and modulation schemes scale up. Altera’s AI-based channel estimation model takes inputs like Signal-to-Noise Ratio (SNR) and delay spread to generate more accurate, context-aware estimations of the wireless channel.
Leveraging Altera’s DSP Builder Software and its seamless integration into Quartus® Prime Software, this implementation achieves:
● High-throughput channel estimation under dynamic radio conditions
● Reduced FPGA device resources used, compared to the non-AI implementation, lowers device power, resulting in lower OpEx:
● 67% reduction in DSP blocks
● 58% reduction in M20K blocks
● Lower latency due to optimized inference pipeline on FPGA fabric
● Enhanced scalability for 5G and future 6G RAN architectures
Why This Matters for mMIMO and Beyond
As networks evolve toward Open RAN and increasingly virtualized infrastructures, the ability to deploy compact, efficient, and flexible AI models becomes a key differentiator. FPGAs, with their parallel processing capabilities and programmability, offer an ideal platform for executing AI-enhanced signal processing tasks like channel estimation.
Altera’s AI-native Agilex™ FPGA device architecture allows you to move intelligence closer to the signal, making your RAN faster, leaner, and more adaptable. Whether you're building high-mobility use cases or designing energy-aware small cells, this approach delivers high performance and lower FPGA power, resulting in lower wireless operator operational expenses (OpEx).
See AI-Based Channel Estimation in Action
This demo is part of our broader effort to redefine Radio Access Network with FPGAi, highlighting how engineers can blend AI with signal processing to unlock new wireless capabilities. With tools that support rapid deployment and tuning of neural models in hardware, we’re accelerating the path from algorithm to silicon.
Watch the full demo: https://www.youtube.com/watch?v=_gdhdqEAD4w
Explore more FPGAi-based wireless innovations: https://www.altera.com/fpga-solutions/wireless
Learn more about Altera software: FPGA AI Suite | DSP Builder
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