Machine learning explores the study and development of algorithms that can learn from and make predictions and decisions based on data. Applications of machine learning in wireless communications have been receiving a lot of attention, especially in the era of big data and IoT, where data mining and data analysis technologies are effective approaches to solving wireless system evaluation and design issues.

Several evolutionary algorithms (EAs) have emerged in recent decades that mimic the behaviour and evolution of biological entities. EAs are widely used to solve single and multi-objective optimization engineering problems. EAs have also been applied to a variety of microwave components, antenna design, radar design, and wireless communications problems. These techniques, among others, include genetic algorithms (GAs), evolution strategies (ES), particle swarm optimization (PSO), differential evolution (DE), and ant colony optimization (ACO). In addition, new innovative algorithms that are not only biology-based but also physics-based or music-based are also emerging, as are hybrid combinations of EAs.

Two of the fastest growing sectors of communications today are mobile and Internet, both of which have had a profound effect on people's lives. The convergence between these two sectors not only presents great opportunities for the future of 'unplugged' telecommunications but also great challenges in understanding the relative position of different technologies in this future.