Spectrum Sharing for Massive Internet-of-Things Communications
- Author(s): Hattab, Ghaith
- Advisor(s): Cabric, Danijela
- et al.
The emergence of the massive Internet-of-things (IoT) market, i.e., applications with large-scale deployment of Internet-enabled devices, is transforming many sectors, including cities, logistics, retail, and agriculture. Nevertheless, a large-scale uptake of such applications is intertwined with connectivity challenges due to the unique traffic characteristics of massive IoT and the high density of IoT devices.
The main objective of this dissertation is to develop spectrum sharing solutions to enable massive IoT connectivity over the unlicensed and licensed bands. To this end, we unravel the interplay between the intra-network sharing and inter-network sharing, i.e., how a high density of low-cost IoT devices should share the spectrum among themselves while still harmoniously coexist with other wireless networks.
We first present a framework to model and analyze ultra-narrowband (UNB) communications, where IoT devices randomly access the unlicensed spectrum using extremely narrowband signals. The framework enables tractable analysis of large-scale networks, identifying fundamental limits and proposing enhancements that help UNB networks support a higher density of low-rate IoT devices. We then present an access scheme that supports applications with higher rates over unlicensed bands with sensing regulations. To this end, we revisit spectrum sensing for massive IoT and propose a sensing-based system, where base stations with narrowband receivers locally share and process sensing reports to scan a wideband spectrum, identifying resources at a fine spectral resolution and aggressively reusing them over space. Next, for applications with strict quality-of-service guarantees, we consider cellular connectivity, where we develop a shared spectrum access protocol, leveraging drones as IoT data aggregators. The proposed protocol maximizes the energy-efficiency of IoT devices while still protecting existing cellular users from interference. Last, we analyze and optimize the densification of small cells to maximize coverage and rate of users and IoT devices.
To deliver these contributions, we leverage tools from stochastic geometry, communication theory, and optimization, working across the theoretical analysis and practical algorithm development. We validate the effectiveness of the proposed solutions via comprehensive simulations of wide-area networks. We glean several design guidelines and recommendations throughout the dissertation, which can be of relevance to wireless communications engineers, network operators, and standard bodies.