On the Implementation of Integer and Non-Integer Sampling Rate Conversion

The main focus in this thesis is on the aspects related to the implementation of integer and non-integer sampling rate conversion (SRC). SRC is used in many communication and signal processing applications where two signals or systems having different sampling rates need to be interconnected. There are two basic approaches to deal with this problem. The first is to convert the signal to analog and then re-sample it at the desired rate. In the second approach, digital signal processing techniques are utilized to compute values of the new samples from the existing ones. The former approach is hardly used since the latter one introduces less noise and distortion. However, the implementation complexity for the second approach varies for different types of conversion factors. In this work, the second approach for SRC is considered and its implementation details are explored. The conversion factor in general can be an integer, a ratio of two integers, or an irrational number. The SRC by an irrational numbers is impractical and is generally stated for the completeness. They are usually approximated by some rational factor. The performance of decimators and interpolators is mainly determined by the filters, which are there to suppress aliasing effects or removing unwanted images. There are many approaches for the implementation of decimation and interpolation filters, and cascaded integrator comb (CIC) filters are one of them. CIC filters are most commonly used in the case of integer sampling rate conversions and often preferred due to their simplicity, hardware efficiency, and relatively good anti-aliasing (anti-imaging) characteristics for the first (last) stage of a decimation (interpolation). The multiplierless nature, which generally yields to low power consumption, makes CIC filters well suited for performing conversion at higher rate. Since these filters operate at the maximum sampling frequency, therefore, are critical with respect to power consumption. It is therefore necessary to have an accurate and efficient ways and approaches that could be utilized to estimate the power consumption and the important factors that are contributing to it. Switching activity is one such factor. To have a high-level estimate of dynamic power consumption, switching activity equations in CIC filters are derived, which may then be used to have an estimate of the dynamic power consumption. The modeling of leakage power is also included, which is an important parameter to consider since the input sampling rate may differ several orders of magnitude. These power estimates at higher level can then be used as a feed-back while exploring multiple alternatives. Sampling rate conversion is a typical example where it is required to determine the values between existing samples. The computation of a value between existing samples can alternatively be regarded as delaying the underlying signal by a fractional sampling period. The fractional-delay filters are used in this context to provide a fractional-delay adjustable to any desired value and are therefore suitable for both integer and non-integer factors. The structure that is used in the efficient implementation of a fractional-delay filter is know as Farrow structure or its modifications. The main advantage of the Farrow struc-