Market Mechanisms Towards Secondary Spectrum Usage

Widespread adoption of smartphones, tablets and other smart devices has resulted in mobile operators (MOs) making a transition from voice to data centric business model. As a consequence there has been an increase in demand for radio spectrum. Spectrum availability in the future can be a cause of concern, the main reason of which is being attributed to the traditional and inflexible approach towards spectrum management. Hence it is required to overhaul the existing spectrum management techniques and adopt those models which aim at higher spectrum utilization. As part of our research methodology we first perform a state-of-the-art review on secondary usage of radio spectrum. We observe that most research assumes a clean slate approach towards the emergence of secondary spectrum markets which are typically designed with an underlying assumption of participating actors being of homogeneous type. In contrast with above we take an evolutionary approach while designing market mechanisms towards heterogeneous secondary usage of spectrum. The evolution of trading markets is reflected in the incremental steps used in our research, i.e. starting from Wireless Fidelity (Wi-Fi IEEE 802.11) capacity markets, followed by super Wi-Fi (IEEE 802.11af) capacity markets and finally TV White Spaces (TVWS) spectrum leasing markets. We make use of Value Network Configuration (VNC) methodology for illustrating the design of market mechanism and further evaluate the designed mechanism using Agent Based Modeling (ABM). Based on our simulation results we observe that a generic trade-off exist between the length of lease time, trade facilitation cost and the extent of trading activity within the markets. We also observe that there exists an optimal range of lease time for which all the market players find themselves in economically favourable situation. We compare super Wi-Fi capacity markets and TVWS spectrum leasing markets over performance of MOs and TV broadcasters and according to our evaluation local area strategy seems to offer more benefits for TVWS spectrum usage

Compressive Sensing Over TV White Space in Wideband Cognitive Radio

PhDSpectrum scarcity is an important challenge faced by high-speed wireless communications. Meanwhile, caused by current spectrum assignment policy, a large portion of spectrum is underutilized. Motivated by this, cognitive radio (CR) has emerged as one of the most promising candidate solutions to improve spectrum utilization, by allowing secondary users (SUs) to opportunistically access the temporarily unused spectrum, without introducing harmful interference to primary users. Moreover, opening of TV white space (TVWS) gives us the con dence to enable CR for TVWS spectrum. A crucial requirement in CR networks (CRNs) is wideband spectrum sensing, in which SUs should detect spectral opportunities across a wide frequency range. However, wideband spectrum sensing could lead to una ordably high sampling rates at energy-constrained SUs. Compressive sensing (CS) was developed to overcome this issue, which enables sub-Nyquist sampling by exploiting sparse property. As the spectrum utilization is low, spectral signals exhibit a natural sparsity in frequency domain, which motivates the promising application of CS in wideband CRNs. This thesis proposes several e ective algorithms for invoking CS in wideband CRNs. Speci cally, a robust compressive spectrum sensing algorithm is proposed for reducing computational complexity of signal recovery. Additionally, a low-complexity algorithm is designed, in which original signals are recovered with fewer measurements, as geolocation database is invoked to provide prior information. Moreover, security enhancement issue of CRNs is addressed by proposing a malicious user detection algorithm, in which data corrupted by malicious users are removed during the process of matrix completion (MC). One key spotlight feature of this thesis is that both real-world signals and simulated signals over TVWS are invoked for evaluating network performance. Besides invoking CS and MC to reduce energy consumption, each SU is supposed to harvest energy from radio frequency. The proposed algorithm is capable of o ering higher throughput by performing signal recovery at a remote fusion center