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Next: Summary Up: Multiple User Maximum Likelihood Previous: Reduced Complexity Processing

   
Simulation Results

In this Section we present two sets of simulation results to show the effect that the multiuser PSP and RSSE algorithms presented in this paper have on the performance of MLSE based estimation. For all results, the number of users was K=2, the number of chips per symbol was N=4, the channels each had FIR length M=3, the Gauss-Markov factor was $\alpha = 0.995$, and the Gauss-Markov model noise covariance was ${\bf C}= 0.01 ~ {\bf I}$. For each simulation trial a set of the users' chip impulse responses were generated using the Gauss-Markov model. User 1 was 10dB stronger than user 2.

For the first set of results, a full trellis was used (no RSSE), and PSP was compared to the exhaustive search solutions, that provides the optimum MLSE. The number of symbols per simulation was n=5, and 2000 trials per SNR were run. Figure 1 shows results. Although PSP with L=1 path saved per trellis state (i.e. standard Viterbi) performed significantly worse than exact MLSE, performance was close to exact MLSE for L=3. We have found L=3 to be adequate in all simulations we performed.


\begin{figure}
\centerline{\epsfysize 2.5in \epsfxsize 3.0in \epsfbox{asil01_1.e...
...PSP comparison (user \char93  1 (lower curves) and user \char93 2).
\end{figure}

The second set of results, shown in Figure 2, compare RSSE to full trellis. For both, L=3. The number of symbols per simulation was n=5, and 2000 trials per SNR were run. Figure 2 illustrates that RSSE provides results comparable to full trellis.


\begin{figure}
\centerline{\epsfysize 2.5in \epsfxsize 3.0in \epsfbox{asil01_3.e...
...ellis vs. RSSE (user \char93  1 (lower curves) and user \char93 2).
\end{figure}


next up previous
Next: Summary Up: Multiple User Maximum Likelihood Previous: Reduced Complexity Processing
Rick Perry
2001-11-03