The Gaussian distributed OFDM signal with high fluctuation introduces nonlinear distortion, out-of-band radiation due to strict requirement on linearity in power amplifier. In 1997, Muller and Huber proposed partial transmit sequences algorithm based on least PAPR (Peak to Average Power Ratio) in order to reduce the fluctuation. On the other hand, when dividing the sequences into more parts, the computation increases exponentially. Furthermore, this algorithm often focuses on the compression of the highest power, which does not mean that the overall system performance should be improved. This paper proposes a novel partial transmit sequences algorithm based on least clipping noise. Specifically, in the selection of optimal parameters in partial transmit sequences to modify the phases of input symbols, total clipping noise (nonlinear distortion) is considered instead of the highest power. This algorithm is extensively studied with the well-known model of power amplifier. It is shown that the system performance with 2 partitions based on least clipping noise is close to that with 3 partitions based on least PAPR, and performance with 3 partitions based on least clipping noise is even better than that with 4 partitions based on least PAPR. This way, the required computation is effectively reduced.
The design of many industrial and engineering systems can often be accomplished using flow graphs of various types. Examples include manufacturing processes and data processing applications, Graph Transformation Expert System, is an expert system which has been developed by WUT for applying techniques of artificial intelligence to the architectural design of data and signal processing systems. Software and hardware architectures may be defined for such systems using data flow graphs, in which nodes represent data processing steps and directed areas represent the `flow' of data between the processing steps. Starting with a user- defined generic processing graphic, this expert will transform the graph by applying transformation rules in order to specialize the processing graph to satisfy specified design goals and/or hardware constraints. Although the particular application for which this expert is designed is that of data and signal processing systems, it can provide an expert system framework for other problems specified graphically; for example, manufacturing systems, information systems, and product distribution systems.
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