Millimetre wave communications in 5g networks under latency constraintsmachine intelligence, application scenarios and perspectives

Resumen

Nowadays there is little doubt that wireless communications have been a pivotal player in the irruption and maturity of digital technologies in almost all sectors of activity. Over the years, the society has witnessed how wireless networking has become an essential element of its evolution and prosperity. However, the ever-growing requirements of applications and services in terms of rate, reliability and latency have steered the interest of regulatory bodies towards emergent radio access interfaces capable of efficiently coping with such requisites. In this context, millimeter-wave (mmWave) communications have been widely acknowledged as a technology enabler for ultra-reliable, low-latency applications in forthcoming standards, such as 5G. Unfortunately, the unprecedented data rates delivered by mmWave communications come along with new paradigms in regards to radio resource allocation, user scheduling, and other issues all across the protocol stack, mainly due to the directivity of antennas andsensitiveness to blockage of communications held in this spectrum band. Consequently, the provision of machine intelligence to systems and processes relying on mmWave radio interfaces is a must for efficiently handling the aforementioned challenges.This Thesis contributes to the above research niche by exploring the use of elements and tools from Computational Intelligence, Matching Theory and Stochastic Optimization for the management of radio and network resources in mmWave communications. To this end, two different application scenarios are targeted: 1) Vehicular communications, where the high degree of mobility and the recurrent inter-vehicular blockage give rise to complex channel conditions for channel allocation and beam alignment; and 2) mobile virtual reality (VR), where the motion-to-photon latency limit raises the hurdle for scheduling the delivery of multimedia content over mmWave. A diversity of intelligent methods for clustering, predictive modeling, matching and optimization for dynamical systems are studied, adapted and applied to the aforementioned scenarios, giving evidences of the profitable advantages and performance gains yielded by these methods. The Thesis complements its technical contribution with a thorough overview of the recent literature of mmWave communications, leading to the main conclusion stemming from the findings of the Thesis: machine intelligence, provided by any technological means, is a driver to realize the enormous potential of mmWave for applications with unprecedented latency constraints.