Sampling Rate Optimization for Improving the Cascaded Integrator Comb Filter Characteristics Raouf Amrane * , Youcef Brik, Samir Zeghlache, Mohamed Ladjal, Djamel Chicouche LASS Laboratory, Faculty of technology, University Mohamed Boudiaf of M’sila , B.P.166, Route Ichebilia, M’sila 28000, Algeria Corresponding Author Email: raouf.amrane@univ-msila.dz https://doi.org/10.18280/ts.380110 ABSTRACT Received: 4 February 2020 Accepted: 10 December 2020 The cascaded integrator comb (CIC) filters are characterized by coefficient less and reduced hardware requirement, which make them an economical finite impulse response (FIR) class in many signal processing applications. They consist of an integrator section working at the high sampling rate and a comb section working at the low sampling rate. However, they don’t have well defined frequency response. To remedy this problem, several structures have been proposed but the performance is still unsatisfactory. Thence, this paper deals with the improvement of the CIC filter characteristics by optimizing its sampling rate. This solution increases the performance characteristics of CIC filters by improving the stopband attenuation and ripple as well as the passband droop. Also, this paper presents a comparison of the proposed method with some other existing structures such as the conventional CIC, the sharpened CIC, and the modified sharpened CIC filters, which has proven the effectiveness of the proposed method. Keywords: CIC filter, FIR, frequency response, optimization, sampling rate, filter sharpening 1. INTRODUCTION The filtering operation, in signal processing domain, plays a very important role in the enhancement of the signal quality. This process can be realized by removing some undesirable components or some frequency characteristics from signals (Figure 1). Nowadays, the implication of filters become indispensable for several electronics fields such as in radio, audio, telecommunication, television, radar, information transmission, ...etc. Usually, the filters can be classified as analog or digital [1-4]. In digital signal processing (DSP), there are two kinds of digital filters, the infinite impulse response (IIR) filters and the finite impulse response (FIR) filters. The Cascaded integrator comb filter (CIC) filters, which were initially invented by Hogenauer [5], are a part of FIR filters that mainly used in low-cost implementation of decimation and interpolation. Besides, these filers don't require multipliers and lot of memory space, which make them an economic choice in various applications such as signal analysis, digital communication, compression, denoising, ...etc. CIC filters consist of two connected blocks in cascade, the first block is an integrator component that works at a high sampling rate and the second one is a comb component that works at a low sampling rate. Several works have studied the CIC filters on different applications. A performance evaluation of CIC filters combined with compensation techniques have been proposed to improve the passband response of filters [6]. This combination makes the CIC decimation component followed by the FIR decimation filter. In order to build a structure that can operate at a lower sampling rate while achieving better performances, a double sharpened CIC decimation filter has been presented [7]. This proposed filter consists of three cascading stages as follows: the first stage is the comb decimation filter that handles at the input sampling rate. The second and the third stages are sharpened comb filters operating in low sampling rate, gradually. This scheme can produce the narrow passband droop in the sharpened second stage and then compensate it with the help of third stage. Furthermore, the maximally flat (MF) error minimization method has been used for addressing the problem of passband droop in the compensated CIC filters [8]. The MF method was applied in order to obtain the coefficients of the second and the fourth order compensation filters. The cascading of these two filters generated a sixth order CIC compensation filter that reduced considerably the passband drop of CIC filters. The basic structure of CIC filter has been discussed with the illustration of its important involved parameters [9]. The authors tried to find some problems associated with the filter characteristics and emphasized a solution for improving its performance. Figure 1. Frequency magnitude response of filter Through this study, we found that the CIC filter performances suffer from two major limitations namely the Traitement du Signal Vol. 38, No. 1, February, 2021, pp. 97-103 Journal homepage: http://iieta.org/journals/ts 97