new micro genetic algorithm for multi user detection in w cdma n.
Skip this Video
Loading SlideShow in 5 Seconds..
New Micro Genetic Algorithm for multi-user detection in W CDMA PowerPoint Presentation
Download Presentation
New Micro Genetic Algorithm for multi-user detection in W CDMA

New Micro Genetic Algorithm for multi-user detection in W CDMA

103 Views Download Presentation
Download Presentation

New Micro Genetic Algorithm for multi-user detection in W CDMA

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. New Micro Genetic Algorithm for multi-user detection in WCDMA AZMI BIN AHMAD Borhanuddin Mohd Ali, Sabira Khatun, Azmi Hassan Dept of Computer and Communication System Engineering, Faculty of Engineering University Putra Malaysia.

  2. Schedule • Multi-user WCDMA Tx and Rx • GA module • New selection method • Result • Discussion

  3. Multiple User Data Transmission • In a multi-user environment, signals from multiple user are transmitted from similar WCDMA transmitter (mobile station for uplink) • These signals interfered with each other and resulted in Multiple Access Interference (MAI) and Inter-Symbol Interference (ISI).

  4. Signal received • AT the receiver, signal received consisted of multiple signals (multi-bit, multi-user, multi-path) accumulated plus interference and noise.

  5. A single WCDMA transmitter

  6. WCDMA Receiver + GA module

  7. At the receiver • Multi-path signal can be solve with RAKE Receiver with Maximal Ratio Combining which is part of the receiver. • Multi-user signal will be separated by the De-Scrambler and De-Spreader. The result is the estimation of the data which are the output of the Matched Filter.

  8. GA Module • The outputs of the Matched Filter is used as input to the GA module. • Only the I part of the received signal will be used. The Q part is used as control parameters. • The module is based on microGA type of Genetic Algorithms method. In this method a small population size is used for faster convergence.

  9. new uGA selection method • Population initializes by mutating the original estimated data from matched filter output. • The individuals in the population are evaluated and ranked descending. • The crossover process will used single-point crossing.

  10. Selection (N population) • Best-fit individual with be crossover with the least fit individual. • Bestfit+1 will be crossover with the Leastfit-1. • For N population: - 1-10, 2-9, 3-8, 4-7, 5-6

  11. For Best-fit and Least-fit • Best-fit individual is automatically selected. • Best-fit individual is crossover with the least-fit individual. The better fit of the resulting offspring is selected. • The least-fit individual will be discarded.

  12. Rest of the populations • From crossover of (2-9,3-8,4-7,5-6) • Each crossover will produced 2 offspring • 2 parent + 2 offspring =4 subpopulation • The subpopulation will be evaluated and ranked. • The two better-fit individuals will be selected for new population. The other two will be discarded.

  13. Generations • New generation is created. • The process will proceed for 10 generations. • The final best-fit individual in the final generation will be selected as the optimized solution.

  14. Results…

  15. Result..cont

  16. Results…cont

  17. Results…cont

  18. Comparison of Computational Complexity Scenario K-user, one bit symbol processing • SGA will perform in K*K*100 • uGA perform in K*5*30 but as number of K increased the population size couldn’t cover the whole search space • uGA w/newSelect perform in K*K/2*10 similar to uGA but better coverage of search space.

  19. Conclusion • Result didn’t show much differences between uGA and the uGA w/newSelect • But from error statistic the uGA w/newSelect show a little improvement in number of error corrected. • Calculation wise the uGA w/newSelect compute in less time, so it’s suitable for use in realtime.

  20. Thank You Questions / Suggestions