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SPREAD-SPECTRUM COMMUNICATION SYSTEMS CORRELATED WIIH THE PROPOSED WAVELET-BASED SIGNAL PROCESSING FOR NOISE SUPPRESSION

Le Tien Thuong 1
Hoang Dinh Chien 1
Ho Quang Dung 1
Pham Thi Thuy Ngoc 1
Ha Hoang Kha 1
Volume & Issue: Vol. 3 No. 5&6 (2000) | Page No.: 45-62 | DOI: 10.32508/stdj.v3i5&6.3564
Published: 2000-06-30

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This article is published with open access by Viet Nam National University, Ho Chi Minh City, Viet Nam. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0) which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Abstract

Code-Division Multiple-Access (CDMA) implemented with spread-spectrum signaling is one of the most promising multiplexing technologies for integrated and cellular communication services. The advantages of Spread Spectrum (SS) for these services include superior in multipath environments, flexibility in the location of channels, privacy, and increased capacity in fading channel. Also a significantly attractive feature of spread spectrum CDMA is the ability of SS systems to share the bandwidth with narrowband communication systems without undue degradation of either system's performance. However, it has been demonstrated that the performance of spread spectrum in the presence of narrowband interference can be enhanced significantly through the use of noise suppression prior to despreading. Not only does suppression improve error-rate performance, but it also leads to increase CDMA cellular system capacity and improves acquisition capability of communication systems [14]. The paper is organized into three parts including most of our thoughtful works related to the topic. The first part of this paper introduces the two most common techniques used in SS communications systems - direct-sequence SS, frequency-hoping SS, and other related topics. The wavelets and filter bank are discussed. Then noise suppression algorithms using wavelets are proposed to improve SS systems' performance. The second part is concerned about computer simulation programmed in the Matlab package with version 5.3. The purpose of this part is to give an aid in understanding the SS system performance in noisy environment. The system performance in Gaussian noise and narrowband interference environment with noise suppression algorithms using wavelets will be concentrated and simulation results will be presented by plotting BER curves corresponding to different values of SNR between -4 dB and 8 dB. Consequently, the paper ends with conclusions drawn from simulation and directs to some further studies for developing this work.

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