A Novel Time Delay Estimation and Calibration Method of TI-ADC Based on a Coherent Optical Communication System

Abstract

In optical communication systems, coherent detection is a standard method. The received signal enters the digital domain after passing through a time-interleaved analog-to-digital converter (TI-ADC). However, the time delay of the ADC brings noise into the signal, which decreases the signal quality; therefore, ADC calibration is essential. At present, there are many calibration methods for time delay, but their performances are not satisfactory at a high sampling frequency. This paper presents a method of time delay estimation and calibration in a coherent optical communication system. First, the expected maximum (EM) method is used to roughly estimate the time delay and then transfer the estimated value into the trained back propagation (BP) neural network to generate more accurate results. Second, the sampled signal is reconstructed, and then a finite impulse response (FIR) filter is designed to compensate for the time delay. There are several advantages of the proposed method compared with previous works: the convergence with a BP network is faster, the estimation accuracy is higher, and the calibration does not affect the sample operation of the ADC working in the background mode. In addition, the proposed calibration method does not need additional circuits and its low power consumption provides more sources for dispersion compensation, error correction, and other subsequent operations in the coherent optical communication system. Based on the quadrature phase shift keying (QPSK) system, the proposed method was implemented in a 16-channel/8-bit, 40-GS/s ADC. After estimation and calibration, the relative error of estimation was below 1%, the signal noise distortion rate (SNDR) reached 55.9 dB, the spurious free dynamic range (SFDR) improved to 61.2 dB, and the effective number of bits (ENOB) was 6.7 bits. The results demonstrate that the proposed method has a better calibration performance than other methods.

Error-Backpropagation-Based Background Calibration of TI-ADC for Adaptively Equalized Digital Communication Receivers

“…This group may meet Criteria 1 and 4 but not 2 and 3. • Group 5 (G5): Techniques based on Machine Learning (ML) algorithms [37]- [39]. These techniques share with the one proposed in this paper the idea of using ML algorithms to estimate all the mismatches in a TI-ADC, although in substantially different ways.…”

Enabling Fine Sample Rate Settings in DSOs with Time-Interleaved ADCs

“…Time interleaved structures are complemented with a digital circuit that combines the sample streams and carries out further real-time operations to produce a calibrated response and/or enable different acquisition modes. The most common approaches exploit polyphase filters, which are suitable to manage parallel data streams [ 33 , 34 , 35 ]. Unfortunately, they lack however of adequate reconfigurability features to enable fine sample rate selection capabilities.…”