A Method of Calculating the Quantization Threshold for a VLBI DBBC and Its FPGA Implementation

With the development of digital signal processing circuits, especially the using of programmable components, digital equipments are replacing analog ones. The VLBI2010 defines a modern digital backend system using digital logic circuits to perform VLBI data acquisition. A Digital Base-Band Converter (DBBC), which digitizes the broadband Intermediate Frequency (IF) analog signals output by the radio receiver and transforms them into base-band signals through different channels, is the core component of a VLBI data acquisition system. It has many advantages over the traditional Analog Base-Band Converter (ABBC) such as better bandpass properties and higher signal-to-noise ratios in long-baseline measurements. In order to obtain the 2-bit output of a DBBC, the converted base-band signals are compared with a quantization threshold by a digital AGC and transformed into 2-bit quantized signals. A currently used 2-bit digital AGC is based on the traditional AGC method, which calculates the threshold value from the average power as obtained by taking squares, additions, and square roots of the base-band signals. In this paper, we present a new method to calculate the quantization threshold. Through counting and analyzing the states of all bits of the digital signals, the new method determines the best-fit threshold value according to the statistics of the distribution of the digital-signal bits, so as to conform the statistics to prescribed number proportions of bits. The threshold is updated every N input signals with the best-fit approach to achieve dynamic quantization. With this method, which avoids complex calculations in the traditional method, the number of resources occupied in the digital AGC module is reduced. The simulation results of an FPGA design of the method have verified its feasibility. Key parts of the 2-bit quantization and the related theory of quantization noise are also briefly introduced in the paper. The optimal quantization thresholds and the quantitative weights based on the theory are calculated by the MATLAB in the simulations.