Optimal resource allocation for multi-radio access in cognitive wireless networks

Abstract

In next generation wireless networks, the coexistence of multiple radio access technologies (RATs) would become one of the most prevalent features. The wireless networks will be characterized by the integration of different RATs and the use of multiple radio access standards for high data rate transmission. These networks where a mobile device (MD) can utilize multiple RATs simultaneously are named multi-radio access (MRA) system, which accommodates RATs such as 3GPP LTE, mobile WiMAX, and WLAN as subsystems. In MRA systems, optimal operation issues of multi-RAT have taken lots of attention recently to increase system efficiency and improve connectivity as well as energy performance due to the availability of multiple RATs. Lots of work have described that spectrum of each RAT can be dynamically assigned rather than fixed manner for improving the utilization of the spectrum and enhancing the performance of wireless networks. So, optimal resource allocation problem of these networks should be investigated for higher spectral efficiency. In this disseration, an optimal solution for how these resources should be allocated is considered. First, this dissertaion studies operational issues of the cognitive wireless networks, where multiple and simultaneous radio transmissions are possible by an SDR-based MD over several RATs. In these networks, an optimal solution is analyzed for parallel MRA and a distributed joint allocation algorithm is proposed to maximize total system capacity. This dissertaion also investigates the benefit of multiple transmissions over multiple RATs than a single transmission over a single RAT at a time, which can be interpreted as network diversity gain. Numerical results validate the performance enhancement of our proposed algorithm. Second, this dissertation focuses on a distributed multiple spectrum pricing scheme to maximize system capacity in the multiaccess system. The multi-price based scheme provides a distributed decision making...