Pseudo Genetic Algorithm Assisted Multiuser Detection In DS-CDMA

With the increase of the number of users, Multiple Access Interference (MAI) becomes the main jamming of the CDMA communication system. To suppress MAI is the major obstacle for CDMA communication to improve its performance.Multiuser detection (MUD) has proved to be a promising way to suppress MAI, as well as to suppress Additive White Gaussian Noise (AWGN) and multipath interference. Owing to the outstanding global search ability, genetic algorithm (GA) has been introduced in the multiuser detection techniques and has greatly improved MUD. Random individual initialization of GA tends to generate too many eccentric and homogeneous individuals which cause slow and premature convergence. It needs many operations (selection strategy, incest prevention and mutation) to improve, which consume too much computation and lose many good genes. As a result, the traditional GA (TGA) and the MUD based TGA need be improved. For the reason mentioned above, the following work has been done in this literature:①A pseudo genetic algorithm is introduced in the literature, in which the proposed complementary-parent strategy initializes every other pair of parents with dynamically or statically complementary chromosomes (such as 010101…0101 and 101010…1010). Crossover of every generation is only performed between the offspring from the same parents, during which the parents are completely replaced by their own children. Higher population diversity is got without gene lost at all, by which search ability is enhanced. Incest prevention, selection strategies and mutation are unnecessary and consequently cancelled (so it is named pseudo genetic algorithm, PGA).②The model of MUD based on PGA is built using Matlab. There are two users in the experiment. Signal spectra of the 2 users are spread by a Gold code of which the period is 31. The communication channel is supposed to be Rayleigh multipath fading channel and AWGN channel. To compare the performance of bit error rate, a similar model of MUD based on the TGA is built, too. The simulations work out the plot of BER vs. SNR, which shows that the MUD based on PGA gains a preferable advantage over the MUD based on TGA on the facet of receive performance.③Performances such as the elitist search speed and computation complexity are studied in an example of searching the maximum solution of an function. As indicated by the simulation results, the speed of elitist search is accelerated greatly and computation complexity is reduced by half. Analysis of variance (ANOVA) shows that the pseudo genetic algorithm has a higher level of consistency. All these results suggested that MUD based on PGA takes advantages of simpler structures, faster and steadier convergence over the MUD based on TGA.All the work mentioned above come to conclusions that the PGA-MUD, compared to the TGA-MUD, has merits of smaller BER under the same SNR, smaller computing complexity, simpler structure , faster and steadier convergence.