CRII: CIF: Towards Self-Powered Heterogeneous Cellular Networks

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Driven by unprecedented increase in mobile data traffic, cellular networks are undergoing a huge paradigm shift from a coverage-centric homogeneous deployment of high-power cell towers (base stations) to a more organic capacity-driven deployment that additionally includes various types of low-power base stations, collectively called small cells. To maintain deployment flexibility and limit operational costs, it is highly desirable that these base stations have a capability to power themselves through self-contained energy harvesting modules. The inherent unreliability associated with energy harvesting, however, combined withthe irregular locations of the small cells, makes it challenging to quantify the reliability of such "self-powered" heterogeneous cellular networks. This project aims to lay the foundations of these networks through new analytical tools and metrics. Self-powered base stations with additional capability to self-backhaul will result in a "truly wireless" cellular network thereby enabling a variety of "drop and play" deployments. Further broader impact of this project will be through research dissemination, education, broadening participation of students, and industry collaboration. With the overarching goal of establishing fundamental performance limits for self-powered hetergeneous networks, this project adopts a cross-disciplinary approach involving tools from communications, information theory, and stochastic geometry. The first synergistic component of this project develops a new comprehensive model capable of capturing key characteristics of self-powered heterogeneous networks, such as the differences in the base station capabilities, irregularities in their deployments, and uncertainties in their energy levels. Powerful mathematical tools with foundations in stochastic geometry and point process theory lend tractability to this model, thereby allowing the formal analysis of new performance metrics unique to self-powered heterogeneous networks. For instance, due to the coupling of loads across base stations, when one base station drains out its energy, it may initiate a "cascade effect" taking the whole network down with it. The first suite of results characterizes this behavior and determines the regimes in which the network remains stable. Once stability is guaranteed, the second set of results focuses on the end-user performance in this new paradigm where the surviving base stations are not always guaranteed to have sufficient energy to serve all their load, thereby resulting in energy outages. Finally, these metrics are jointly analyzed with classical quality-of-experience metrics, such as downlink rate.