Presentation on theme: "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."— Presentation transcript:
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.
Schedule Multi-user WCDMA Tx and Rx GA module New selection method Result Discussion
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).
Signal received AT the receiver, signal received consisted of multiple signals (multi-bit, multi-user, multi-path) accumulated plus interference and noise.
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.
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.
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.
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
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.
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.
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.
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 couldnt cover the whole search space uGA w/newSelect perform in K*K/2*10 similar to uGA but better coverage of search space.
Conclusion Result didnt 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 its suitable for use in realtime.