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In this book, we analyzed the performance of DFT spread OFDM method for reducing the PAPR in MIMO-OFDM. The simulation results shows IFDMA method is best among the OFDMA, LFDMA and IFDMA. But the disadvantage of IFDMA scheme is that we are losing user diversity and investigates one of the bottleneck problems that exist in MIMO-OFDM wireless communication system. High Peak-Average Power Ratio (PAPR of OFDM signal), and discusses how to reduce it by different effective system. High Peak-Average Power Ratio (PAPR of OFDM signal), and discusses how to reduce it by different effective algorithms.…mehr

Produktbeschreibung
In this book, we analyzed the performance of DFT spread OFDM method for reducing the PAPR in MIMO-OFDM. The simulation results shows IFDMA method is best among the OFDMA, LFDMA and IFDMA. But the disadvantage of IFDMA scheme is that we are losing user diversity and investigates one of the bottleneck problems that exist in MIMO-OFDM wireless communication system. High Peak-Average Power Ratio (PAPR of OFDM signal), and discusses how to reduce it by different effective system. High Peak-Average Power Ratio (PAPR of OFDM signal), and discusses how to reduce it by different effective algorithms. First part begins with the review of PAPR reduction techniques like clipping and filtering, selected mapping (SLM), partial transmit sequence (PTS) and DFT Spreading. Their capabilities and drawbacks are compared on the basis of simulation results obtained. We are mainly focusing on the signal scrambling technology, and discuss it by observing the MATLAB simulation results. In the signal scrambling technology, we study the method of selected mapping and partial transmit sequence.
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Autorenporträt
Yuvraj Singh Ranawat is Assistant Professor at the Electrical Engineering Department, Mewar University Chittorgarh, Rajasthan, India. He has M.Tech in Digital Communication from Mewar University bestowed in 2015. He is interested in Analog Electronics, Signal and system, EMFT, Digital Electronics and softwares like MATLAB, Magus and Scilab.