Comparative Analysis of Various Adaptive Filter Structures Using Simulink
Adaptive digital filter finds its prime applications in the field of science and industry. It is a core technology in the field of Digital Signal Processing. Adaptive digital filter plays a vital role to enhance the performance of the system as well as to
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Abstract Adaptive digital filter finds its prime applications in the field of science and industry. It is a core technology in the field of Digital Signal Processing. Adaptive digital filter plays a vital role to enhance the performance of the system as well as to reduce the resource utilization. Digital signal processing processes with the digital signal using complex techniques from basic filters and signal transform. The design is implemented using the MATLAB tools which had enabled the design of basic building block faster and more accurate. In this paper, we have implemented the various adaptive filter structures using Simulink. Adaptive filter structure such as NLMS-, RLS-, and BLMS-based model is implemented and error is estimated based on it. Original signal and noisy signal are added together and applied as an input to various adaptive filter modela. Thus, the desired signal that is the original signal is recovered by using NLMS, RLS, and BLMS. Keywords Adaptive filter structures
Simulink NLMS RLS BLMS
1 Introduction In data transmission, it is difficult to achieve high-speed data transmission at low error rates. From multiple paths, the transmitted signal usually arrives at the receiver side due to which distortion and noise appear in the received signal, which results in the overlap of the received symbols that is often referred to as inter-symbolinterference (ISI), limiting the maximum data rate. When the size of a cell M.B. Dembrani (&) K.B. Khanchandani Anita Zurani Department of Electronics and Telecommunication Engineering, Shri Sant Gajanan Maharaj College of Engineering, Shegaon, India e-mail: [email protected] K.B. Khanchandani e-mail: [email protected] Anita Zurani e-mail: [email protected] © Springer Science+Business Media Singapore 2017 R. Singh and S. Choudhury (eds.), Proceeding of International Conference on Intelligent Communication, Control and Devices, Advances in Intelligent Systems and Computing 479, DOI 10.1007/978-981-10-1708-7_99
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decreases, co-channel interference (CCI) becomes another factor which degrades the system performance. The removal of these strong interfering signals can improve the coverage and radio transmission quality.
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Adaptive Filters
When an information signal is transmitted to the receiver from the transmitter through a communication channel, there is a possibility to add multiple interference signals due to multipath propagation in the channel. Such interference cancelation is one of the applications where adaptive filters are used. In this paper, we have proposed various adaptive digital filter designs, which are based on NLMS, RLS, and BLMS algorithm for error estimation and cancelation. This adaptive filter design is based on stable FIR filters. The design and performance of such adaptive filters based on the following trade-off parameters such as the convergence rate and filter coefficient [1, 2]. The source signal is corrupted by the noise signal when it is transmitted through the communication chan
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