20172017 International Conference on Recent Innovations in Signal processing and Embedded Systems (RISE)Requires access

Realization of fixed-point modified D-LMS adaptive filter

Farheen Ali, Sunil Malviya, Neelesh Gupta

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Abstract

This paper presents the Delayed least mean square (D-LMS) adaptive filter for deriving its Architectures for low complexity and high-speed implementation. Among many adaptive filter algorithms that exist in the open journals, this approaches which are derived from the minimization of the mean squared error between the output of the adaptive filter and some preferred signal seems to be the most well-liked. Probably the easiest algorithm belonging to this section is the Least Mean Squared (LMS) algorithm which has the advantage of low complexity and simplicity of implementation. It's a broadly used adaptive algorithzm for its low hardware complexity and robustness. Although in practical applications, several modified LMS algorithm had be proposed. Delayed LMS algorithm is suited in hardware implementation. An efficient architecture for the implementation of a delayed least mean square adaptive filter for achieving area delay power efficient and lower adaptation delay implementation, proposed methodology is of a novel partial product generator and a approach for optimized balanced pipelining across the time-consuming combinational blocks of the structure. The migration of DSP adaptive filter to RTL makes the algorithm much faster.

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What this paper is about

This paper presents the Delayed least mean square (D-LMS) adaptive filter for deriving its Architectures for low complexity and high-speed implementation. Among many adaptive filter algorithms that exist in the open journals, this approaches which are derived from the minimization of the mean squared error between the output of the adaptive filter and some preferred signal seems to be the most well-liked. Probably the easiest algorithm belonging to this section is the Least Mean Squared (LMS) algorithm which has the advantage of low complexity and simplicity of implementation. It's a broadly used adaptive algorithzm for its low hardware complexity and robustness. Although in practical applications, several modified LMS algorithm had be proposed. Delayed LMS algorithm is suited in hardware implementation. An efficient architecture for the implementation of a delayed least mean square adaptive filter for achieving area delay power efficient and lower adaptation delay implementation, proposed methodology is of a novel partial product generator and a approach for optimized balanced pipelining across the time-consuming combinational blocks of the structure. The migration of DSP adaptive filter to RTL makes the algorithm much faster.

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Available abstract

This paper presents the Delayed least mean square (D-LMS) adaptive filter for deriving its Architectures for low complexity and high-speed implementation. Among many adaptive filter algorithms that exist in the open journals, this approaches which are derived from the minimization of the mean squared error between the output of the adaptive filter and some preferred signal seems to be the most well-liked. Probably the easiest algorithm belonging to this section is the Least Mean Squared (LMS) algorithm which has the advantage of low complexity and simplicity of implementation. It's a broadly used adaptive algorithzm for its low hardware complexity and robustness. Although in practical applications, several modified LMS algorithm had be proposed. Delayed LMS algorithm is suited in hardware implementation. An efficient architecture for the implementation of a delayed least mean square adaptive filter for achieving area delay power efficient and lower adaptation delay implementation, proposed methodology is of a novel partial product generator and a approach for optimized balanced pipelining across the time-consuming combinational blocks of the structure. The migration of DSP adaptive filter to RTL makes the algorithm much faster.

Key concepts: Adaptive filter, Least mean squares filter, Computer science, Robustness (evolution), Multidelay block frequency domain adaptive filter, Kernel adaptive filter, Algorithm, Finite impulse response

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