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Low Complexity Deep Learning Augmented Wireless Channel Estimation for Pilot-Based OFDM on Zynq System on Chip
  • Animesh Sharma ,
  • Syed Asrar ul haq ,
  • sumit darak
Animesh Sharma
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Syed Asrar ul haq
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sumit darak
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The feasibility study of deep learning (DL) approaches for reliable, flexible, and high throughput wireless physical layer (PHY) has received significant interest from academia and industry. Channel estimation (CE) is one of the critical signal processing units of the receiver PHY. Recent DLbased CE approaches have outperformed state-of-the-art statistical approaches such as least-square-based CE (LS) and linear minimum mean square error-based CE (LMMSE), requiring hardware-friendly arithmetic computations than LMMSE. The first contribution is optimizing the existing state-of-the-art CE algorithms for realization on system-on-chip (SoC) via hardwaresoftware co-design and fixed point analysis, followed by performance comparison for various signal-to-noise ratios (SNR) and wireless channels. We highlight the high complexity, execution time, and power consumption of existing DL-based CE and LMMSE approaches, along with the poor performance of the LS. We develop a novel compute-efficient LS-augmented interpolated deep neural network (LSiDNN) based CE algorithm and realize it on SoC. We demonstrate substantial savings in complexity and execution time without significant degradation in functional accuracy. Specifically, the proposed LSiDNN approach offers 88-90% lower execution time and 38-85% lower resources than state-of-the-art DL-based CE. LSiDNN significantly outperforms LMMSE, and the gain improves with increased mobility. It also offers 75% lower execution time and 90-94% lower resource utilization than LMMSE