Exploiting Non-uniform Quantization for Enhanced ILC in Wideband Digital Pre-distortion

TL;DR

This paper demonstrates that non-uniform quantization, achieved by lowering the reference level before analog-to-digital conversion, significantly enhances iterative learning control performance in wideband digital pre-distortion, especially for OFDM signals.

Contribution

It provides a mathematical explanation and experimental validation showing how non-uniform quantization improves ILC by reducing quantization noise in wideband signals.

Findings

3 dB EVM improvement with lower reference level

15 dB NMSE improvement for WiFi OFDM signals

Non-uniform quantization reduces quantization noise in low-amplitude signals

Abstract

In this paper, it is identified that lowering the reference level at the vector signal analyzer can significantly improve the performance of iterative learning control (ILC). We present a mathematical explanation for this phenomenon, where the signals experience logarithmic transform prior to analogue-to-digital conversion, resulting in non-uniform quantization. This process reduces the quantization noise of low-amplitude signals that constitute a substantial portion of orthogonal frequency division multiplexing (OFDM) signals, thereby improving ILC performance. Measurement results show that compared to setting the reference level to the peak amplitude, lowering the reference level achieves 3 dB improvement on error vector magnitude (EVM) and 15 dB improvement on normalized mean square error (NMSE) for 320 MHz WiFi OFDM signals.