Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be ef...Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be efficient to separate sources with different distributions.So to solve the problem of pdf mismatch and the separation of hybrid mixture in BSS,the generalized Gaussian model(GGM)is introduced to model the pdf of the sources since it can provide a general structure of univariate distributions.Its great advantage is that only one parameter needs to be determined in modeling the pdf of different sources,so it is less complex than Gaussian mixture model.By using maximum likelihood(ML)approach,the convergence of the proposed algorithm is improved.The computer simulations show that it is more efficient and valid than conventional methods with fixed pdf estimation.展开更多
A new technique for turbo decoder is proposed by using a local subsidiary maximum likelihood decoding and a probability distributions family for the extrinsic information. The optimal distribution of the extrinsic inf...A new technique for turbo decoder is proposed by using a local subsidiary maximum likelihood decoding and a probability distributions family for the extrinsic information. The optimal distribution of the extrinsic information is dynamically specified for each component decoder.The simulation results show that the iterative decoder with the new technique outperforms that of the decoder with the traditional Gaussian approach for the extrinsic information under the same conditions.展开更多
文摘Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be efficient to separate sources with different distributions.So to solve the problem of pdf mismatch and the separation of hybrid mixture in BSS,the generalized Gaussian model(GGM)is introduced to model the pdf of the sources since it can provide a general structure of univariate distributions.Its great advantage is that only one parameter needs to be determined in modeling the pdf of different sources,so it is less complex than Gaussian mixture model.By using maximum likelihood(ML)approach,the convergence of the proposed algorithm is improved.The computer simulations show that it is more efficient and valid than conventional methods with fixed pdf estimation.
基金Supported by the National Aeronautical Foundation of Science and Research of China (No.00F52048)
文摘A new technique for turbo decoder is proposed by using a local subsidiary maximum likelihood decoding and a probability distributions family for the extrinsic information. The optimal distribution of the extrinsic information is dynamically specified for each component decoder.The simulation results show that the iterative decoder with the new technique outperforms that of the decoder with the traditional Gaussian approach for the extrinsic information under the same conditions.
