scholarly journals Direct-Sequence Spread Spectrum with Signal Space Diversity for High Resistance to Jamming

2021 ◽  
Author(s):  
Tarak Arbi ◽  
Benoit Geller ◽  
Oudomsack Pierre Pasquero
Keyword(s):  
High Resistance ◽  
Spread Spectrum ◽  
Direct Sequence Spread Spectrum ◽  
Space Diversity ◽  
Direct Sequence ◽  
Signal Space ◽  
Signal Space Diversity

Analisis Karakterisasi Teknologi Direct Sequence Spread Spectrum Dan Frequency Hopping Spread Spectrum

2019 ◽  
Vol 2 (2) ◽  
pp. 129-138
Author(s):  
Fawzan Galib Abdul Karim Bawahab ◽  
Elvan Yuniarti ◽  
Edi Kurniawan
Keyword(s):  
Spread Spectrum ◽  
Communication Systems ◽  
Multiple Access ◽  
Frequency Hopping ◽  
Direct Sequence Spread Spectrum ◽  
Direct Sequence ◽  
Frequency Hopping Spread Spectrum ◽  
Two Systems ◽  
The Difference

Abstrak. Pada penelitian ini, telah dilakukan analisa karakterisasi pada teknologi Direct Sequence Spread Spectrum dan Frequency Hopping Spread Spectrum, sebagai salah satu teknik multiple-access pada sistem komunikasi. Karakterisasi dilakukan untuk mencari bagaimana cara meningkatkan keoptimalan kedua sistem tersebut, dalam mengatasi masalah interferensi dengan sistem dan channel yang sama. Dan juga untuk menentukan veriabel apa yang mempengaruhi keoptimalan kedua sistem tersebut. Karakterisasi dilakukan dengan menentukan variabel-variabel yang mempengaruhi keoptimalan keduanya. Hasil dari karakterisasi, diketahui variabel-variabel yang mempengaruhi kemampuan sistem DSSS yaitu nilai frekuensi spreading (). Sedangkan untuk sistem FHSS yaitu nilai frekuensi spreading ( dan ) dan selisih antara frekuensi hopping data dengan frekuensi hopping interferensi . Kata Kunci: BER, DSSS, FHSS, Interference, Spread spectrum. Abstract. In this study, characterization of Direct Sequence Spread Spectrum and Frequency Hopping Spread Spectrum technologies have been done, as one of the multiple-access techniques in communication systems. Characterization is done to find out how to improve the ability of the two systems, in solving interference problems with the same system and channel. And also to determine what veriabel affects the ability of the two systems. Characterization is done by determining the variables that affect the ability of both. The results of the characterization, known variables that affect the ability of the DSSS system are the spreading frequency value (). As for the FHSS system, the spreading frequency value ( and ) and the difference between frequency hopping data with frequency hopping interference .

scholarly journals Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2538
Author(s):  
Shuang Zhang ◽  
Feng Liu ◽  
Yuang Huang ◽  
Xuedong Meng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spread Spectrum ◽  
High Efficiency ◽  
Detection System ◽  
Direct Sequence Spread Spectrum ◽  
Detection Performance ◽  
Detection Methods ◽  
Direct Sequence ◽  
Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

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