ItemConference Paper

Design of Computationally Efficient FRM Based Reconfigurable Filter Structure for Spectrum Sensing in Cognitive Radio for IoT Networks

Title
Design of Computationally Efficient FRM Based Reconfigurable Filter Structure for Spectrum Sensing in Cognitive Radio for IoT Networks
Creator
Varghese R.C.; Amir A.
Description
A low computational complexity FIR bank of filters are essential for spectrum sensing in wireless networks. FRM is a widely used method to generate a sharp transition width sub-bands or channels. The intention of this work is to design multiple non-uniform sharp transition width FIR bank of filter with low computational complexity for spectrum sensing in cognitive radio for IoT networks. The design parameters of the proposed structure are calculated in an efficient way. The proposed structure is designed based on the FRM filter and complex exponential modulation technique (CEMT). The performance of the proposed structure is illustrated with the help of an example. Result indicates that the number of multipliers of the proposed structure is less compared to other existing techniques. 2022 IEEE.
Source
IEEE Region 10 Humanitarian Technology Conference, R10-HTC, Vol-2022-September, pp. 281-287.
Date
2022-01-01
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Complex exponential modulation technique (CEMT); FIR filters; Frequency response masking (FRM)
Coverage
Varghese R.C., Christ (Deemed to Be University), Dept. of Ece, Bangalore, India; Amir A., Christ (Deemed to Be University), Dept. of Ece, Bangalore, India
Rights
Restricted Access
Relation
ISSN: 25727621; ISBN: 978-166540156-2
Format
Online
Language
English
Type
Conference paper

Collection

Conference Papers

Citation

Varghese R.C.; Amir A., “Design of Computationally Efficient FRM Based Reconfigurable Filter Structure for Spectrum Sensing in Cognitive Radio for IoT Networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 27, 2025, https://archives.christuniversity.in/items/show/20218.

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