1 
An application of independent component analysis to DSCDMA detectionFang, Yue 30 October 2006
This work presents the application of the theory and algorithms of Independent Component Analysis (ICA) to blind multiuser symbol estimation in downlink of DirectSequence Code Division Multiple Access (DSCDMA) communication system. The main focus is on blind separation of convolved CDMA mixture and the improvement of the downlink symbol estimation. Term blind implies that the separation is performed based upon the observation only. Since the knowledge of system parameter is available only in the downlink environment, the blind multiuser detection algorithm is an attractive option in the downlink.<p>Firstly, the basic principles of ICA are introduced. The objective function and optimization algorithm of ICA are discussed. A typical ICA method, one of the benchmark methods for ICA, FastICA, is considered in details. Another typical ICA algorithm, InfoMAX, is introduced as well, followed by numerical experiment to evaluate two ICA algorithms.<p>Secondly, FastICA is proposed for blind multiuser symbol estimation as the statistical independence condition of the source signals is always met. The system model of simulation in downlink of DSCDMA system is discussed and then an ICA based DSCDMA downlink detector has been implemented with MATLAB. A comparison between the conventional Single User Detection (SUD) receiver and ICA detector has been made and the simulation results are analyzed as well. The results show that ICA detector is capable of blindly solving multiuser symbol estimation problem in downlink of DSCDMA system.<p>The convergence of ICA algorithm is, then, discussed to obtain more stable simulation results. A joint detector, which combines ICA and SUD and where ICA is considered as an additional element attached to SUD detector, has been implemented. It was demonstrated that the joint detector gives the lowest error probability compared to conventional SUD receiver and pure ICA detector with training sequences.

2 
An application of independent component analysis to DSCDMA detectionFang, Yue 30 October 2006 (has links)
This work presents the application of the theory and algorithms of Independent Component Analysis (ICA) to blind multiuser symbol estimation in downlink of DirectSequence Code Division Multiple Access (DSCDMA) communication system. The main focus is on blind separation of convolved CDMA mixture and the improvement of the downlink symbol estimation. Term blind implies that the separation is performed based upon the observation only. Since the knowledge of system parameter is available only in the downlink environment, the blind multiuser detection algorithm is an attractive option in the downlink.<p>Firstly, the basic principles of ICA are introduced. The objective function and optimization algorithm of ICA are discussed. A typical ICA method, one of the benchmark methods for ICA, FastICA, is considered in details. Another typical ICA algorithm, InfoMAX, is introduced as well, followed by numerical experiment to evaluate two ICA algorithms.<p>Secondly, FastICA is proposed for blind multiuser symbol estimation as the statistical independence condition of the source signals is always met. The system model of simulation in downlink of DSCDMA system is discussed and then an ICA based DSCDMA downlink detector has been implemented with MATLAB. A comparison between the conventional Single User Detection (SUD) receiver and ICA detector has been made and the simulation results are analyzed as well. The results show that ICA detector is capable of blindly solving multiuser symbol estimation problem in downlink of DSCDMA system.<p>The convergence of ICA algorithm is, then, discussed to obtain more stable simulation results. A joint detector, which combines ICA and SUD and where ICA is considered as an additional element attached to SUD detector, has been implemented. It was demonstrated that the joint detector gives the lowest error probability compared to conventional SUD receiver and pure ICA detector with training sequences.

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