Multi-Decimation Stage Filtering for Sigma Delta ADCs: Design and Optimization

• Matlab
• Academia / Research
• Multirate DSP

During my research on digital FIR decimation filters I have been developing various Matlab scripts and functions. In which I have decided later on to consolidate it in a form of a toolbox. I have developed this toolbox to assist and automate the process of designing the multi-stage decimation filter(s). The toolbox is published as an open-source at the MathWorks web-site. My dissertation is open for public online as well. The toolbox has a wide set of examples to guide the user through the steps. Furthermore, there is a design template. I hope you all enjoy and get the best of it. I will be glad for your constructive feedback.

The Multi-Stage Decimation toolbox (MSD-toolbox) is developed in Matlab language. Designing a decimation filter system starts with definite specifications such as the sampling frequency ($f_s$), oversampling ratio (OSR), passband frequency ($f_{pb}$), signal-to-noise ratio (SNR, regarded also as ADC resolution), in-band noise (IBN), and a stimuli bit-stream from sigma delta modulator. It should be noted that, the transition band-width ($\Delta f=(f_{sb}-f_{pb})/f_{sb}$) could be provided at the input specifications instead of the passband frequency. The stimuli bit-stream permits accurate analysis for the intra decimation stages.
Moreover, involving the stimuli affords performing spectral analysis in addition to the frequency domain analysis for the decimation filter. Whereas, involving the acceptable tolerance in the IBN maintains additional flexibility for filter coefficient optimization. The stimuli bit-stream can be imported from DelSig-toolbox [1], DISCO-toolbox [2]. The MSD-toolbox has several routines and sub-programs for calculating implementation parameters and performance evaluation. The sub-programs and routines are based on state-of-the-art algorithms. The essential routines are:

1. Calculating $k$ and $M$ ($R_{T}(k,M)$)
2. Calculating $h_k$ and $Q$ (P-M-E)
3. Coefficient optimization (Optimization)
4. Cost estimation (Cost)

The optimal number of decimation stages and the decimation factor at each stage is calculated for minimum computational effort ($R_T$), as given in $$ {R_T} = {D_\infty }{f_s}\sum\limits_{i = 1}^k {\frac{{{M_i}}}{{\left( {\prod\limits_{j = 1}^i {{M_j}} } \right)\left( {1 - \frac{{{f_{sb}} + {f_{pb}}}}{{{f_s}}}\prod\limits_{j = 1}^i {{M_j}} } \right)}}} $$ The problem is constrained for $k\in\lbrace2,3,4\rbrace$ and $M_k=2$ for even $M$'s, where $M$ is the overall decimation factor (equivalent to OSR), $f_s$ is the sampling frequency, $f_{pb}$ is the passband frequency, $f_{sb}$ is the stopband frequency, and $D_\infty$ is a function in passband ($\delta_{pb}$) and stopband ($\delta_{sb}$) ripples, as depicted in $$ {D_\infty } = {\log _{10}}{\delta _{sb}}[0.005309{({\log _{10}}{\delta _{pb}})^2} + 0.07114{\log _{10}}{\delta _{pb}} - 0.4761] $$ $$ - [0.00266{({\log _{10}}{\delta _{pb}})^2} + 0.5941{\log _{10}}{\delta _{pb}} + 0.4278] $$ where $k$ is calculated for minimum $R_T$ at distinct values of $M$.
Detailed analysis for optimizing the number of decimation stages and the decimation factor for each stage is given in [3], [4], and [5].

The passband ripples ($\delta_{pb}$) and stopband ripple ($\delta_{sb}$) are calculated using iterative simulations preserving a predefined acceptable tolerance in the in-band noise (IBN). This is done by designing a single stage filter and tuning the $\delta_{pb}$ and $\delta_{sb}$, by defining a certain range or a set of discrete values. The $\delta_{sb}$ remains the same for all the $k$-stages. While, the $\delta_{pb_{i}}=\delta_{pb}/k$ for stage $i$.

The exact number of decimation stages and the dedicated decimation factor for each stage were calculated in addition to the given design specifications, which sustain all the necessary parameters required for calculating the filter coefficients ($h_k$) for each decimation filter stage employing the Parks-McClellan Equiripple. Following the filter coefficient ($h_k$) calculation comes the quantization bit-width ($Q$) calculation step in order to compute the scaled filter coefficient set ($\hat h_k$).
Initially, a set of parameters is calculated for each stage, such as baseband frequency ($f_B$), passband frequency ($f_{pb}$), and stopband frequency ($f_{sb}$). The $f_B$ is calculated by $$ {f_B} = \frac{{{f_s}}}{{2{\rm{OSR}}}} $$ where OSR is the oversampling ratio and $f_s$ is the sampling frequency. The $f_{pb}$ and the $f_{sb}$ are calculated by the equations given below. However, the intermediate stopband frequency ($f_{sb_{i}}$) is calculated by $$ {f_{pb}} = {f_B}(1 - \Delta f) $$ $$ {f_{sb}} = {f_B} $$ $$ {f_{s{b_i}}} = \frac{{{f_s}}}{{\prod\limits_{i = 1}^k {{M_i}} }} - {f_B}\\ $$ FIR filter has distinct implementation types, such as standard FIR (FIR), half-band (HB), and multi-band (MB) types. The FIR filter implementation type affects the filter response and coefficients. The Parks-McClellan Equiripple (P-M-E) algorithm is used to obtain the FIR filter coefficients ($h_k$) for the predefined filter types except the CIC filter type. Subsequently, the required quantization bit-width ($Q$) is calculated iteratively preserving minimum mean error (ME) or minimum increase in the in-band noise (IBN).

Samples

[1] R. Schreier and G.C. Temes. Understanding delta-sigma data converters. IEEE press Piscataway, NJ, 2005.
[2] A. Buhmann, M. Keller, M. Maurer, M. Ortmanns, and Y. Manoli. Disco - a toolbox for the discrete-time simulation of continuous-time sigma-delta modulators using matlab. In Proc. Midwest Symposium on Circuits and Systems (MWSCAS'07), pages 1082--1085, Aug. 2007.
[3] R. Crochiere and L. Rabiner. Optimum FIR digital filter implementations for decimation, interpolation, and narrow-band filtering. 23(5):444--456, 1975.
[4] M. Coffey. Optimizing multistage decimation and interpolation processing. IEEE Signal Processing Letters, 10(4):107--110, 2003.
[5] M. Coffey. Optimizing Multistage Decimation and Interpolation Processing: Part II. IEEE Signal Processing Letters, 14(1):24--26, 2007.

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