Abstract
The decorrelating decision-feedback (DDF) multiuser detector based on Cholesky factorization has been proven to improve the performance of the users in the detection process. For relatively low crosscorrelation values between user signals this detector performs quite well. The detector described in this paper employs two triangular matrices (upper and lower) and soft output information to improve the data estimates over the DDF detector. Significant performance gains can be achieved over the DDF. Also, the users tend to have their bit error probabilities clustered. Thus, the performance of a certain user is less dependent on its position in the detection process than for the DDF.
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