Comb-Based Filters for Sampling Rate Conversion

2009 ◽  
pp. 316-346
Author(s):  
Ljiljana Milic
Keyword(s):  
Communication Systems ◽  
Moving Average ◽  
Sampling Rate ◽  
Software Radio ◽  
Satellite Communications ◽  
Comb Filter ◽  
Single Chip ◽  
Comb Filters ◽  
The Individual ◽  
Rate Conversion

Comb filters are developed from the structures based on the moving average (boxcar) filter. The combbased filter has unity-valued coefficients and, therefore, can be implemented without multipliers. This filter class can operate at high frequencies and is suitable for a single-chip VLSI implementation. The main applications are in communication systems such as software radio and satellite communications. In this chapter, we introduce first the concept of the basic comb filter and discuss its properties. Then, we present the structures of the comb-based decimators and interpolators, discuss the corresponding frequency responses, and demonstrate the overall two-stage decimator constructed as the cascade of a comb decimator and an FIR decimator. In the next section, we expose the application of the polyphase implementation structure, which is aimed to reduce the power dissipation. We consider techniques for sharpening the original comb filter magnitude response and emphasize an approach that modifies the filter transfer function in a manner to provide a sharpened filter operating at the lowest possible sampling rate. Finally, we give a brief presentation of the modified comb filter based on the zero-rotation approach. Chapter concludes with several MATLAB Exercises for the individual study. The reference list at the end of the chapter includes the topics of interest for further research.

Methods for Improving Alias Rejections in Comb Filters

2018 ◽  
pp. 4746-4760
Author(s):  
Gordana Jovanovic Dolecek
Keyword(s):  
Chebyshev Polynomials ◽  
Sampling Rate ◽  
Comb Filter ◽  
Comb Filters ◽  
Decimation Filter

Downsampling is the process of decreasing the sampling rate of signal by an integer. This process may introduce the unwanted spectrum replica called aliasing. To avoid aliasing the signal must be filtered by decimation filter prior downsampling. Decimation consists of filtering and downsampling. The most simple decimation filter is comb filter usually used in the first stage of decimation. However, comb filter does not provide a good aliasing rejection. This paper presents the methods for improving alias rejection of comb filters. The methods are based on comb zero rotation, cosine filters, Chebyshev polynomials, and cascade of combs with different parameters.

Methods for Improving Alias Rejections in Comb Filters

2019 ◽  
pp. 891-909
Author(s):  
Gordana Jovanovic Dolecek
Keyword(s):  
Chebyshev Polynomials ◽  
Sampling Rate ◽  
Comb Filter ◽  
Comb Filters ◽  
Decimation Filter

Downsampling is the process of decreasing the sampling rate of signal by an integer. This process may introduce the unwanted spectrum replica called aliasing. To avoid aliasing, the signal must be filtered by decimation filter prior to downsampling. Decimation consists of filtering and downsampling. The simplest decimation filter is comb filter usually used in the first stage of decimation. However, comb filter does not provide a good aliasing rejection. This chapter presents methods for improving alias rejection of comb filters. The methods are based on comb zero rotation, cosine filters, Chebyshev polynomials, and cascade of combs with different parameters.

scholarly journals Modified CIC Filter for Rational Sample Rate Conversion

1970 ◽  
Vol 4 (1) ◽  
pp. 15-20 ◽  
Author(s):  
Gordana Jovanovic Dolecek
Keyword(s):  
Impulse Response ◽  
Software Defined Radio ◽  
Conversion Factor ◽  
Sampling Rate ◽  
Prime Numbers ◽  
Comb Filter ◽  
Triangular Form ◽  
Cic Filter ◽  
Sample Rate ◽  
Rate Conversion

The modification of the conventional CIC (cascadedintegrator-comb) filter for rational sample rate conversion (SRC) in software defined radio (SWR) systems is presented here. The conversion factor is a ratio of two mutually prime numbers, where the decimation factor M can be expressed as a product of two integers. The overall filter realization is based on a stepped triangular form of the CIC impulse response, the corresponding expanded cosine filter, and sine-based compensation filter. This filter performs sampling rate conversion efficiently by using only additions/subtractions making it attractive for software defined radio (SWR) applications.

FIR Filters for Sampling Rate Conversion

2009 ◽  
pp. 103-135
Author(s):  
Ljiljana Milic
Keyword(s):  
Transfer Functions ◽  
Finite Impulse Response ◽  
Sampling Rate ◽  
High Rate ◽  
Fir Filters ◽  
Implementation Problem ◽  
Sampling Rate Conversion ◽  
The Individual ◽  
Rate Conversion

The role of filters in sampling-rate conversion process has been discussed in Chapters II and III. Filters are used to suppress aliasing in decimators and to remove images in interpolators. The overall performance of a decimator or of an interpolator mainly depends on the characteristics of antialiasing and antiimaging filters. In Chapter III, we have considered the typical filter specifications and several methods for designing filter transfer functions that can meet the specifications. In this chapter, we are dealing with the implementation aspects of decimators and interpolators. The implementation problem arises from the unfavorable facts that filtering has to be performed on the side of the high-rate signal: in decimation filtering precedes the down-sampling, and in interpolation up-sampling precedes filtering. The goal is to construct a multirate implementation structure providing the arithmetic operations to be performed at the lower sampling rate. In this way, the overall workload in the sampling-rate conversion system can be decreased by the conversion factor M (L). The multirate filter implementation means that down-sampling or up-sampling operations are embedded into the filter structure. In this chapter, we are focused on the structures developed for finite impulse response (FIR) filters. The nonrecursive nature of FIR filters offers the opportunity to create implementation schemes that significantly improve the overall efficiency of FIR decimators and interpolators. This chapter concentrates on the direct implementation forms for decimators and interpolators and the implementation forms based on the polyphase decompositions. Memory saving solutions for polyphase decimators and interpolators are also presented. Finally, the efficiency of FIR polyphase decimators and interpolators is discussed. The chapter concludes with MATLAB exercises for the individual study.

scholarly journals Decimation by non-integer factor in software radio receivers

2003 ◽  
Vol 16 (3) ◽  
pp. 365-375 ◽  
Author(s):  
Djordje Babic ◽  
Markku Renfors
Keyword(s):  
Sampling Rate ◽  
Software Radio ◽  
Radio Receiver ◽  
Integer Number ◽  
Analog To Digital Converter ◽  
Digital Converter ◽  
Analog To Digital ◽  
Interpolation Filters ◽  
Radio Receivers ◽  
Rate Conversion

The sampling rate conversion is a critical functionality of the software radio receiver. Because the signals of different system standards have incommensurate symbol/sampling rates and a common Analog-to Digital Converter (ADC) is to be used for all supported standards, the decimation factor may become very difficult non-integer number. This paper gives overviews and comparisons of two efficient fractional decimator structures based on Cascaded Integrator-Comb (CIC) filters and low order polynomialbased interpolation filters.

IIR Filters to Sampling Rate Conversion

2009 ◽  
pp. 136-170
Author(s):  
Ljiljana Milic
Keyword(s):  
Transfer Function ◽  
High Performance ◽  
Sampling Rate ◽  
Infinite Impulse Response ◽  
Iir Filters ◽  
Iir Filter ◽  
Nonlinear Phase ◽  
Sampling Rate Conversion ◽  
The Individual ◽  
Rate Conversion

Infinite impulse response (IIR) filters are used in applications where the computational efficiency is the highest priority. It is well known that an IIR filter transfer function is of a considerably lower order than the transfer function of an FIR equivalent. The drawbacks of an IIR filter are the nonlinear phase characteristic and sensitivity to quantization errors. In multirate applications, the computational requirements for FIR filters can be reduced by the sampling rate conversion factor as demonstrated in Chapter IV. However, such a degree of computation savings cannot be achieved in multirate implementations of IIR filters. This is due to the fact that every sample value computed in the recursive loop is needed for evaluating an output sample. Based on the polyphase decomposition, several techniques have been developed which improve the efficiency of IIR decimators and interpolators as will be shown later on in this chapter. In this chapter, we consider first the direct implementation structures for IIR decimators and interpolators. In the sequel, we demonstrate the computational requirements for direct form IIR decimators and interpolators. The polyphase decomposition of an IIR transfer function is explained with its application to decimation and interpolation. Then, we demonstrate an efficient IIR polyphase structure based on all-pass subfilters, which is applicable to a restricted class of decimators and interpolators. In this chapter, we discuss the application of the elliptic minimal Q factor (EMQF) filter transfer function in constructing high-performance decimators and interpolators. The chapter concludes with a selection of MATLAB exercises for the individual study.

INNOVATIVE METHOD OF SATELLITE COMMUNICATION

2019 ◽  
Author(s):  
Teodor Narytnik ◽  
Vladimir Saiko
Keyword(s):  
Communication Systems ◽  
High Speed ◽  
Satellite Communication ◽  
Radio Channel ◽  
Satellite Communications ◽  
Service Area ◽  
Satellite Systems ◽  
Orbit Satellite ◽  
Geometric Size ◽  
Distributed Satellite

The technical aspects of the main promising projects in the segments of medium and low-orbit satellite communication systems are considered, as well as the project of the domestic low-orbit information and telecommunications system using the terahertz range, which is based on the use of satellite platforms of the micro- and nanosatellite class and the distribution of functional blocks of complex satellite payloads more high-end on multiple functionally related satellites. The proposed system of low-orbit satellite communications represents the groupings of low-orbit spacecraft (LEO-system) with the architecture of a "distributed satellite", which include the groupings of the root (leading) satellites and satellite repeaters (slaves). Root satellites are interconnected in a ring network by high-speed links between the satellites. The geometric size of the “distributed satellite” is the area around the root satellite with a radius of about 1 km. The combination of beams, which are formed by the repeater satellites, make up the service area of the LEO system. The requirements for the integrated service area of the LEO system (geographical service area) determine the requirements for the number of distributed satellites in the system as a whole. In the proposed system to reduce mutual interference between the grouping of the root (leading) satellites and repeater satellites (slaves) and, accordingly, minimizing distortions of the information signal when implementing inter-satellite communication, this line (radio channel) was created in an unlicensed frequency (e.g., in the terahertz 140 GHz) range. In addition, it additionally allows you to minimize the size of the antennas of such a broadband channel and simplify the operation of these satellite systems.

scholarly journals Design of PCB Impedance Matching Inductors and Antennas for Single-Chip Communication Systems

2008 ◽  
Vol 2008 ◽  
pp. 1-7 ◽  
Author(s):  
J. Chung ◽  
S. Hamedi-Hagh
Keyword(s):  
Dielectric Constant ◽  
Resonance Frequency ◽  
Communication Systems ◽  
Return Loss ◽  
Impedance Matching ◽  
Portable Device ◽  
Single Chip ◽  
Matching Network

This paper presents the design of an inductor and an antenna for a portable device with GPS and FM capabilities. The inductor is designed to operate at the lower frequency FM band as part of a matching network and the antenna is designed to operate at the higher frequency GPS L1 band. The FR4 PCB used has a thickness of 1.6 mm with a dielectric constant of 3.8 and has two metallization layers. The inductor is designed with 1.5 mm trace width, 3.5 turns, and has a dimension of 14.5 mm × 14.5 mm. It has an inductance of 95 nH, a resistance of 2.9 Ω, a self-resonance frequency of 500 MHz, and a maximum Q of 51 from 100 MHz to 200 MHz (FM band). The antenna has a dimension of 49 mm × 36 mm and is designed to operate at 1.5754 GHz L1 band. It also has a return loss of −36 dB and a measured bandwidth of 250 MHz.

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