Introductory Editorial

Excerpt

Communication devices have become a vital part of our lives, especially wireless personal communication. The increasing demand for higher service quality has ushered in a new era of technological innovation. Due to the nonlinear nature of the environment, major changes to the current system are needed. Over the past decade, soft computing has emerged as a potential candidate for solving intricate global optimization problems, which are difficult to solve by traditional methods. In the current scenario, image processing, signal processing, industrial optimization, and control system and power system applications present myriad complexities that must be unraveled by researchers, who are using soft computing tools to solve these problems. Popular soft computing techniques for global optimization include genetic algorithms (GA), differential evolution (DE), ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC), and the firefly algorithm (FFA). These methods have been successfully applied to a wide range of benchmark and real-world application problems. In wireless communication, soft computing will play a major role in uncertainty management, optimization, and prediction, and may also be used in classification. Machine learning algorithms such as artificial neural networks learn by experience and predict the output for the betterment of the system. Uncertainties arising due to incomplete modeling and measurements are handled using fuzzy logic, either in a standalone manner or in conjunction with optimization and prediction algorithms. The synergistic integration of different soft computing tools is best demonstrated in solving problems related to communication and mobility in wireless networks. This special issue explores many strategies and solutions for real-world practical issues in wireless personal communication. …