PAPR reduction in synchronous CI/MC-CDMA uplink system using genetic algorithms

Sumanta Hati; Santi P. Maity

doi:10.1117/12.2010343

28 January 2013 PAPR reduction in synchronous CI/MC-CDMA uplink system using genetic algorithms

Sumanta Hati, Santi P. Maity

Proceedings Volume 8760, International Conference on Communication and Electronics System Design; 87600F (2013) https://doi.org/10.1117/12.2010343
Event: International Conference on Communication and Electronics System Design, 2013, Jaipur, India

Abstract

Main disadvantage of a multicarrier communication system is high peak to average power ratio (PAPR) of the transmitted signal. The high PAPR causes the performance degradation due to nonlinear distortion in the high power amplifier (HPA). PAPR value is closely depends on transmitted power and correlation properties of the user’s spreading code. High peak and low average value of the transmitted power increases the PAPR. On the other side high auto correlation function (ACF) value and low cross correlation function (CCF) value of the spreading code also increases the PAPR. But to eliminate the multiple access interference (MAI) in receiver end, ACF should be peak and CCF should be posses zero for all time shifts. So, there is a conflicting nature of PAPR and bit error rate (BER). That’s why an exhaustive effort has been made in this paper for proper distribution of transmitted power within an acceptable range of peak transmitted power and evaluates the new spreading codes using Genetic Algorithms (GA). In general, to obtain optimum PAPR reduction using GA, total searching time to find out the proper amplitude and phase parameters must be accomplished. Simulation results show that PAPR and BER performance using this proposed technique is better than conventional network.

Citation Download Citation

Sumanta Hati and Santi P. Maity "PAPR reduction in synchronous CI/MC-CDMA uplink system using genetic algorithms", Proc. SPIE 8760, International Conference on Communication and Electronics System Design, 87600F (28 January 2013); https://doi.org/10.1117/12.2010343

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KEYWORDS

Transmitters

Receivers

Genetic algorithms

Data modeling

Signal to noise ratio

Telecommunications

Correlation function

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