Efficient Spectrum Sensing and Sharing Techniques for Dynamic Wideband Spectrum Access

Besides enabling an enhanced mobile broadband access, fifth-generation (5G) wireless mobile networks are envisioned to support the connectivity of massive, heterogeneous Internet of Things (IoT) devices. Connecting these devices through 5G systems and providing them with their needed data rates require huge amounts of spectrum and power resources, thus calling for the development and design of innovative, dynamic resource identification, access and sharing methods that make effective use of these limited resources. This thesis focuses specifically on wideband spectrum sensing, and presents innovative techniques that enable efficient identification and recovery of unused spectrum opportunities in wideband dynamic spectrum access. Recent research efforts have focused on leveraging compressive sampling (CS) theory to enable wideband spectrum sensing recovery at sub-Nyquist rates. However, these approaches suffer from the following shortcomings. First, they consider homogenous wideband spectrum, where all bands are assumed to have similar primary users (PU)s traffic characteristics whereas in practice, the wideband spectrum occupancy is heterogeneous. Second, the number of measurements that receiver hardware designs are able to perform is practically way smaller than the number of measurements required by the CS-based sensing approaches. Third, the number of measurements required by the CS-based sensing approaches depends on the number of occupied bands (i.e., sparsity level), which is often unknown in advance and changes over time. Forth, current wideband spectrum databases suffer from scalability issues in that they incur lots of sensing overhead. This thesis proposes a set of new, complementary techniques that overcome these aforementioned challenges. More specifically, in this thesis, 1. We design efficient spectrum occupancy information recovery techniques for heterogeneous wideband spectrum access. Our proposed techniques exploit the block-like structure of spectrum occupancy behavior observed in wideband spectrum access networks to enable the development of compressed spectrum sensing algorithms. Our proposed spectrum sensing algorithms achieve more stable spectrum information recovery than that achieved by existing approaches. 2. We develop distributed CS-based spectrum sensing techniques for cooperative wideband spectrum access that require lesser measurements while overcoming time-variability of spectrum occupancy and addressing hidden terminal challenges. Also, we propose non-uniform sensing matrices design that exploits the heterogeneity in the wideband spectrum access to further improve the spectrum sensing recovery accuracy. 3. We develop scalable spectrum occupancy information recovery techniques for database-driven wideband spectrum access networks. The novelty of our developed techniques lies in combining the merit of compressive sampling theory with that of low-rank matrix theory to enable scalable and accurate wideband spectrum occupancy recovery at low sensing overhead. 4. We propose joint data and energy transfer optimization frameworks for powering mobile cellular devices through RF energy harvesting. Our proposed framework accounts for both the consumed power at the base station and the battery power available at the end users to ensure that end users achieve their required data rates with as little battery power consumption as possible. We also analytically derive closed-form expressions of the optimal power allocations required for meeting the data rate requirements of the downlink and uplink communications between the base station and its mobile users

Scalable spectrum database construction mechanisms for efficient wideband spectrum access management

We propose a novel framework for enabling scalable database-driven dynamic spectrum access and sharing of heterogeneous wideband spectrum. The proposed framework consists of two complementary approaches that exploit the merits of compressive sensing theory, low-rank matrix theory, and user cooperation to build an accurate heterogeneous wideband spectrum map by overcoming the time-variability of the number of occupied bands, the need for a high number of measurements per sensing node (SN), the inherent wireless channels’ impairments, and the high reporting network overhead. First, exploiting the fact that close-by SNs have a highly correlated spectrum observation, we leverage distributed compressive sensing to enable cooperative heterogeneous wideband spectrum sensing only from a small number of measurements per each SN. Second, to reduce the network overhead due to the high width of the spectrum of interest, we propose a two-step approach that performs spectrum occupancy recovery using the local low-rank property of occupancy sub-matrices. Then, we combine the completed sub-matrices entries to produce the whole spectrum occupancy matrix. Through simulations, we show that the proposed framework efficiently achieves high detection in the sensing step and minimizes the spectrum occupancy matrix recovery error while reducing the overall network overhead.This work was supported in part by the US National Science Foundation, USA under NSF awards No. 1162296 and No. 1923884

Exploiting wideband spectrum occupancy heterogeneity for weighted compressive spectrum sensing

Compressive sampling has shown great potential for making wideband spectrum sensing possible at sub-Nyquist sampling rates. As a result, there have recently been research efforts that aimed to develop techniques that leverage compressive sampling to enable compressed wideband spectrum sensing. These techniques consider homogeneous wideband spectrum, where all bands are assumed to have similar PU traffic characteristics. In practice, however, wideband spectrum is not homogeneous, in that different spectrum bands could have different PU occupancy patterns. In fact, the nature of spectrum assignment, in which applications of similar types are often assigned bands within the same block, dictates that wideband spectrum is indeed heterogeneous, as different application types exhibit different behaviors. In this paper, we consider heterogeneous wideband spectrum, where we exploit this inherent, block-like structure of wideband spectrum to design efficient compressive spectrum sensing techniques that are well suited for heterogeneous wideband spectrum. We propose a weighted ? - minimization sensing information recovery algorithm that achieves more stable recovery than that achieved by existing approaches while accounting for the variations of spectrum occupancy across both the time and frequency dimensions. Through intensive numerical simulations, we show that our approach achieves better performance when compared to the state-of-the-art approaches. 1 2017 IEEE.Scopu