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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Learning Vector Quantization Network for PAPR Reduction in Orthogonal Frequency Division Multiplexing Systems
verfasst von : Seema Khalid, Syed Ismail Shah, Jamil Ahmad
Erschienen in: Unconventional Computation
Verlag: Springer Berlin Heidelberg
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Major drawback of Orthogonal Frequency Division Multiplexing (OFDM) is its high Peak to Average Power Ratio (PAPR) that exhibits inter modulation noise when the signal has to be amplified with a non linear high power amplifier (HPA). This paper proposes an efficient PAPR reduction technique by taking the benefit of the classification capability of Learning Vector Quantization (LVQ) network. The symbols are classified in different classes and are multiplied by different phase sequences; to achieve minimum PAPR before they are transmitted. By this technique a significant reduction in number of computations is achieved.