Deep Analog-to-Digital Converter for Wireless Communication
Ashkan Samiee, Yiming Zhou, Tingyi Zhou, Bahram Jalali

TL;DR
This paper introduces a deep learning-based approach to improve the performance of high-speed, multi-channel analog-to-digital converters in wireless communication, reducing calibration complexity and enhancing accuracy.
Contribution
The paper presents a novel deep learning method for real-time ADC calibration that outperforms traditional digital signal processing techniques in complex, high-speed environments.
Findings
Approximately 5-bit increase in dynamic range.
Significant reduction in symbol error rate.
Effective real-time compensation of ADC non-idealities.
Abstract
With the advent of the 5G wireless networks, achieving tens of gigabits per second throughputs and low, milliseconds, latency has become a reality. This level of performance will fuel numerous real-time applications, such as autonomy and augmented reality, where the computationally heavy tasks can be performed in the cloud. The increase in the bandwidth along with the use of dense constellations places a significant burden on the speed and accuracy of analog-to-digital converters (ADC). A popular approach to create wideband ADCs is utilizing multiple channels each operating at a lower speed in the time-interleaved fashion. However, an interleaved ADC comes with its own set of challenges. The parallel architecture is very sensitive to the inter-channel mismatch, timing jitter, clock skew between different ADC channels as well as the nonlinearity within individual channels. Consequently,…
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