The last decade has witnessed a ever-growing demand for radio spectrum resources. Both the increasing number of users and the diverse variety of demanded services have motivated the research community (both academic and industrial) to focus their research efforts on different techniques allowing fo…
The last decade has witnessed a ever-growing demand for radio spectrum resources. Both the increasing number of users and the diverse variety of demanded services have motivated the research community (both academic and industrial) to focus their research efforts on different techniques allowing for a more efficient use of the available resources in each particular scenario (e.g. from mobile phone communications to densely-deployed sensor networks). In the past, many of the studies in this trend have concentrated on simplicity-based solutions. Following this research area, the recently coined “green technology” concept aims at optimizing the energy consumption of communication devices by attempting to provide enhanced battery autonomy at a minimum cost, not only to the operators themselves, but also to the end users. Unfortunately, most of the proposed advances to date are far from practicality.
To overcome this lack of implementable communication schemes, huge research efforts are currently devoted to balance the trade-off between performance and energy efficiency, from dynamic allocation of available resources as a function of the network conditions/status to the design of efficient network topologies, encoding, modulation, detection techniques or, in general, any other complexity-aware data processing approaches attaining near-optimal performance. Particularly, soft computing approaches, such as bio-inspired algorithms have emerged in the communications realm as a means to efficiently solve theoretically-intractable optimization issues in communications. Such algorithmic methods become even more interesting by virtue of their adaptability to highly-dynamic constraints as those shown by mobile wireless networks.
