Energy efficiency in wireless communications for mobile user devices

Resumen

Mobile user devices' market has experienced an exponential growth worldwide over the last decade, and wireless communications are the main driver for the next generation of 5G networks. The ubiquity of battery-powered connected devices makes energy efficiency a major research issue. While most studies assumed that network interfaces dominate the energy consumption of wireless communications, a recent work unveils that the frame processing carried out by the device could drain as much energy as the interface itself for many devices. This discovery poses doubts on prior energy models for wireless communications and forces us to reconsider existing energy-saving schemes. From this standpoint, this thesis is devoted to the study of the energy efficiency of mobile user devices at multiple layers. To that end, we assemble a comprehensive energy measurement framework, and a robust methodology, to be able to characterise a wide range of mobile devices, as well as individual parts of such devices. Building on this, we first delve into the energy consumption of frame processing within the devices' protocol stack. Our results identify the CPU as the leading cause of this energy consumption. Moreover, we discover that the characterisation of the energy toll ascribed to the device is much more complex than the previous work showed. Devices with complex CPUs (several frequencies and sleep states) require novel methodologies and models to successfully characterise their consumption. We then turn our attention to lower levels of the communication stack by investigating the behaviour of idle WiFi interfaces. Due to the design of the 802.11 protocol, together with the growing trend of network densification, WiFi devices spend a long time receiving frames addressed to other devices when they might be dormant. In order to mitigate this issue, we study the timing constraints of a commercial WiFi card, which is developed into a standard-compliant algorithm that saves energy during such transmissions. At a higher level, rate adaptation and power control techniques adapt data rate and output power to the channel conditions. However, these have beentypically studied with other metrics rather than energy efficiency in mind (i.e., performance figures such as throughput and capacity). In fact, our analyses and simulations unveil an inherent trade-off between throughput and energy efficiency maximisation in 802.11. We show that rate adaptation and power control techniques may incur inefficiencies at mode transitions, and we provide energy-aware heuristics to make such decisions following a conservative approach. Finally, our research experience on simulation methods pointed us towards the need for new simulation tools commited to the middle-way approach: less specificity than complex network simulators in exchange for easier and faster prototyping. As a result, we developed a process-oriented and trajectory-based discrete-event simulation package for the R language, which is designed as a easy-to-use yet powerful framework with automatic monitoring capabilities. The use of this simulator in networking is demonstrated through the energy modelling of an Internet-of-Things scenario with thousands of metering devices in just a few lines of code.