Low Complexity Deep Learning Augmented Wireless Channel Estimation for Pilot-Based OFDM on Zynq System on Chip

• Published in: IEEE transactions on circuits and systems. I, Regular papers Vol. 71; no. 5; pp. 2334 - 2347
• Main Authors: Sharma, Animesh, Haq, Syed Asrar Ul, Darak, Sumit J.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-05-2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Bit error rate; Channel estimation; Co-design; Complexity; convolution neural network; Deep learning; deep neural network; Errors; Field programmable gate arrays; hardware-software co-design; Heuristic algorithms; Machine learning; Mean square errors; Microprocessors; OFDM; OFDM channel estimation; Power consumption; Resource utilization; Signal processing; Signal to noise ratio; State of the art; System on chip; Transceivers; Wireless communication; zynq system-on-chip
• ISSN: 1549-8328; 1558-0806
• DOI: 10.1109/TCSI.2024.3371780

Channel estimation (CE) is one of the critical signal-processing tasks of the wireless physical layer (PHY). Recent deep learning (DL) based CE have outperformed statistical approaches such as least-square-based CE (LS) and linear minimum mean square error-based CE (LMMSE). However, existing CE approaches have not yet been realized on system-on-chip (SoC). The first contribution of this paper is to efficiently implement the existing state-of-the-art CE algorithms on Zynq SoC (ZSoC), comprising of ARM processor and field programmable gate array (FPGA), via hardware-software co-design and fixed point analysis. We validate the superiority of DL-based CE and LMMSE over LS for various signal-to-noise ratios (SNR) and wireless channels in terms of mean square error (MSE) and bit error rate (BER). We also highlight the high complexity, execution time, and power consumption of DL-based CE and LMMSE approaches. To address this, we propose a novel compute-efficient LS-augmented interpolated deep neural network (LSiDNN) based CE algorithm and realize it on ZSoC. The proposed LSiDNN offers 88-90% lower execution time and 38-85% lower resource utilization than state-of-the-art DL-based CE for identical MSE and BER. LSiDNN offers significantly lower MSE and BER than LMMSE, and the gain improves with increased mobility between transceivers. It offers 75% lower execution time and 90-94% lower resource utilization than LMMSE.