Analysis and Implementation of Block Least Mean Square Adaptive Filter using Offset Binary Coding

In this paper, we present a new mathematical analysis and implementation of block-least-mean-square (BLMS) for finite-impulse-response (FIR) adaptive filter using offset binary coding (OBC). The proposed approach is based on distributed arithmetic (DA) in which OBC combination of input samples are s...

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Veröffentlicht in:IEEE International Conference on Circuits and Systems (Online) S. 1 - 5
Hauptverfasser: Khan, M. Tasleem, Shaik, Rafi Ahamed
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2018
Schlagworte:
Adders
block least mean square (BLMS)
Complexity theory
Distributed arithmetic (DA)
Encoding
Finite impulse response filters
finite-impulse response (FIR)
look-up table (LUT)
Mathematical analysis
offset binary coding (OBC)
Table lookup
ISSN:2379-447X
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Beschreibung
Zusammenfassung:In this paper, we present a new mathematical analysis and implementation of block-least-mean-square (BLMS) for finite-impulse-response (FIR) adaptive filter using offset binary coding (OBC). The proposed approach is based on distributed arithmetic (DA) in which OBC combination of input samples are stored in look-up-table (LUT). The filter output and weight adaptation terms are computed by successive shift-and-accumulation (SA) of LUT contents. The recursive use of OBC scheme have reduced the complexity of LUT. Also, we suggested new structure for LUT update unit which involve fewer adders. In addition, a new SA unit is proposed to include the offset term in OBC representation of weights and error signals. Application Specific Integrated Circuit (ASIC) synthesis results show that the proposed scheme utilizes 51.73% less area, consumes 48.32% less power, provides nearly 2.33 times higher throughput for 32 nd order filter with block length of 4 over the best existing scheme.
ISSN:2379-447X
DOI:10.1109/ISCAS.2018.8350946