- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
This paper studies three of the most important optimization algorithms belonging to Natural Computation (NC): genetic algorithm (GA), tabu search (TS) and simulated quenching (SQ). A concise overview of these methods, including their fundamentals, drawbacks and comparison, is described in the first half of the paper. Our work is particularized and focused on a specific application: joint channel estimation and symbol detection in a Direct-Sequence/Code-Division Multiple-Access (DS/CDMA) multiuser communications scenario; therefore, its channel model is described and the three methods are explained and particularized for solving this. Important issues such as suboptimal convergence, cycling search or control of the population diversity have deserved special attention. Several numerical simulations analyze the performance of these three methods, showing, as well, comparative results with well-known classical algorithms such as the Minimum Mean Square Error estimator (MMSE), the Matched Filter (MF) or Radial Basis Function (RBF)-based detection schemes. As a consequence, the three proposed methods would allow transmission at higher data rates over channels under more severe fading and interference conditions. Simulations show that our proposals require less computational load in most cases. For instance, the proposed GA saves about 73% of time with respect to the standard GA. Besides, when the number of active users doubles from 10 to 20, the complexity of the proposed GA increases by a factor of 8.33, in contrast to 32 for the optimum maximum likelihood detector. The load of TS and SQ is around 15–25% higher than that of the proposed GA.
This paper shows a comparative description of three important optimization methods inspired in the Natural Computation paradigm. Specifically, we have studied three metaheuristic methods: genetic algorithms, tabu search and a particular case of simulated annealing known as simulated quenching. Our work summarizes their basic concepts, differences, drawbacks and advantages. These methods are developed in order to solve one of the most common and important problems in digital communications: the joint channel estimation and symbol detection in a multiuser environment. Specific aspects such as the coding of potential solutions or the description of strategies for avoiding local minima convergence or cycling, have been addressed. The numerical results section shows several simulations that compare the proposed GA, TS and SQ in our best knowledge fair terms, and also includes comparisons with well-known classical detectors such as the decorrelator, the matched filter or the RBF-based multiuser detectors. We have seen how these Natural Computation based methods are efficient tools for solving a complex optimization problem. The GA offers a notably better performance, specially when scenario conditions become harder (low SNR of the user of interest, a large number of interfering users, or the presence of near-far degradation). When these interferences are low or moderate, both TS and SQ exhibit a similar behaviour, which is close to the GA and to the optimum receiver. When interferences are harder or the signal energy of interest is poorer, the proposed GAshows a better performance due to its powerful search scheme that controls the population diversity by monitoring the entropy of the population fitness. Besides, genetic operators are on-line fine-tuned using this information. The three proposed methods allow to have a cost-effective operation of existing channels at higher data rates – even in cases with more severe fading, more active users and stronger interference conditions – than previously existing detectors with, in worst case, limited additional cost increments.