Type of Document Master's Thesis Author Yang, Ning URN etd-03042009-040425 Title Multiuser detection for CDMA systems with convolutional coding Degree Master of Science Department Electrical Engineering Advisory Committee
Advisor Name Title Woerner, Brian D. Committee Chair Gray, Festus Gail Committee Member Rappaport, Theodore S. Committee Member Keywords
- interference cancellation
Date of Defense 1995-10-17 Availability restricted AbstractIn Code Division Multiple Access (COMA) systems, the multiple access interference limits the capacity of current systems which use a matched filter or correlation receiver. It has been shown by previous research that multiuser detection receivers employing interference cancellation techniques can significantly improve the capacity of COMA systems. Error correction coding is also an important technique for overcoming severe channel degradation. In this project, we investigate the performance of multiuser receivers which use the combination of interference cancellation techniques and error correction codes.
Specifically, we look at the combination of multistage interference cancellation and convolutional coding. Two different combination schemes are proposed and the performance of these two schemes is studied. The first scheme is a partitioned approach where the multistage interference cancellation is in front of the decoder and is performed on the coded data. The second scheme is an integrated approach where the decoded data is used to reconstruct the transmitted signals in order to do interference cancellation.
Both of these two schemes result in significant performance improvement over a receiver using either multistage interference cancellation or convolutional coding techniques only, for a reasonable range of operating points. The first scheme is recommended due to its soft-decision estimation of transmitted signal and implementation considerations. The analytical results for the first scheme are presented. Simulation results for both of these two schemes are obtained and compared.
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