Correlated energy generation and imperfect State-of-Charge knowledge in energy harvesting devices

Nowadays, many devices in wireless sensor networks are provided with energy harvesting capability to allow for their continuous operation over long periods of time. In principle, the energy level within each sensor should be managed optimally to ensure the best performance. Network engineers, however, often consider optimality under the idealized assumption of perfect knowledge about the State-of-Charge (SOC) of the device. This information is not always realistic or accurate. In our previous work, we showed that optimal policies for sensing, transmission, and battery usage should rather consider uncertainty on the SOC of the device. In this paper, we extend that investigation, therein performed in the idealized scenario of i.i.d. energy arrivals, by considering a correlated energy generation process. We show that the knowledge of the SOC and that of the energy generation process are useful in a complementary manner, that is they can be traded for each other. Moreover, the knowledge on the state of the energy generation process can obviate the need for acquiring accurate SOC information. This investigation paves the road for a new line of research in wireless sensor networks, allowing a tighter interaction between the designers of energy harvesting and battery storage mechanisms on the one hand, and the engineers of network operation and control policies on the other

Coping with spectrum and energy scarcity in Wireless Networks: a Stochastic Optimization approach to Cognitive Radio and Energy Harvesting

In the last decades, we have witnessed an explosion of wireless communications and networking, spurring a great interest in the research community. The design of wireless networks is challenged by the scarcity of resources, especially spectrum and energy. In this thesis, we explore the potential offered by two novel technologies to cope with spectrum and energy scarcity: Cognitive Radio (CR) and Energy Harvesting (EH). CR is a novel paradigm for improving the spectral efficiency in wireless networks, by enabling the coexistence of an incumbent legacy system and an opportunistic system with CR capability. We investigate a technique where the CR system exploits the temporal redundancy introduced by the Hybrid Automatic Retransmission reQuest (HARQ) protocol implemented by the legacy system to perform interference cancellation, thus enhancing its own throughput. Recently, EH has been proposed to cope with energy scarcity in Wireless Sensor Networks (WSNs). Devices with EH capability harvest energy from the environment, e.g., solar, wind, heat or piezo-electric, to power their circuitry and to perform data sensing, processing and communication tasks. Due to the random energy supply, how to best manage the available energy is an open research issue. In the second part of this thesis, we design control policies for EH devices, and investigate the impact of factors such as the finite battery storage, time-correlation in the EH process and battery degradation phenomena on the performance of such systems. We cast both paradigms in a stochastic optimization framework, and investigate techniques to cope with spectrum and energy scarcity by opportunistically leveraging interference and ambient energy, respectively, whose benefits are demonstrated both by theoretical analysis and numerically. As an additional topic, we investigate the issue of channel estimation in UltraWide-Band (UWB) systems. Due to the large transmission bandwidth, the channel has been typically modeled as sparse. However, some propagation phenomena, e.g., scattering from rough surfaces and frequency distortion, are better modeled by a diffuse channel. We propose a novel Hybrid Sparse/Diffuse (HSD) channel model which captures both components, and design channel estimators based on it