Multicast Resource Allocation In OFDM-Based Cognitive Radio Networks

The massive introduction of smart wireless terminals and the rapid emergence of a variety of applications have brought the wireless network unprecedented opportunities and challenges as well. The conflict of spectrum underutilization and scarcity becomes increasingly intensified, in dire need of new communication technology to utilize the spectrum in a more intelligent way. Cognitive Radio (CR), well according with the demand, attracts extensive attention from both academia and industry. The spectrum resources obtained from CR is sporadic and fragmented, Orthogonal Frequency Division Multiplexing (OFDM) has great flexibility in resource utilization which just meets the technical requirements of CR networks (CRNs). Meanwhile, more and more applications focus on send specific information to specific user groups. It is well suited to apply multicast by grouping the users interested in the same content and providing multicast service for them, which greatly reduces the linear dependence between the connected users and the required resources. By doing so, operators and users can reach a win-win situation. Therefore, how to efficiently develop multicast delivery in OFDM-based CRNs is a trend in future wireless networks and is of vital significance.Although CR and multicast have many advantages, yet the actual performance boost mainly depends on the design and selection of resource allocation schemes. Therefore, it is of the essence to design efficient and practical resource allocation schemes in OFDM-based CRNs to enhance the resource utilization and guarantee the users’QoS. Most of the previous work focused on unicast resource allocation. Several researchers investigated conventional multicast (CM) resource allocation, in which the performance is limited by the worst-case user in the multicast group. In multiple description coding multicast (MDCM), the multicast data is first coded into’descriptions’so that the coded data has quality extensibility. That is, the more descriptions a user decodes correctly, the higher quality it will perceive. Hence, the base station is able to delivery multicast service flexibly based on users’channel condition. Currently, research on MDCM in OFDM-based CRNs is almost blank. Considering the new challenges CRNs face and the difficulty of MDCM, existing work does not meet the requirements which makes designing new schemes urgent and challenging.This paper investigates MDCM resource allocation in OFDM-based CRNs and obtains some results with a certain theoretical value and practical significance.First, the third chapter analyzes the asymptotic performance of MDCM in CRNs for the first time. Since in most cases exact performance analysis would incur complex expressions that are too complex to get insights, the paper investigates asymptotic performance comparisons among unicast, CM and MDCM in CRNs.by applying extreme value theory. Simulation results show that the asymptotic analysis is quite accurate even for moderate number of users. Meanwhile, it is also shown that MDCM possesses great superiorities over unicast and CM, which implies that it is appealing to use MDCM for common data delivery in CRNs. The results further verify the importance of the research work of the paper and provide theoretical guidance for specific resource allocation design.Second, the fourth chapter proposes a statistical-CSI-based MDCM resource allocation scheme. In the previous ideal model, it is assumed that the base station knows perfect CSI of all users. In multicast CRNs, since the number of SUs is typically large, it would be a big challenge for requiring perfect CSI feedback by further considering that feedback may be delayed and capacity-limited. With this in mind, this paper investigates the throughput optimization of MDCM in CRNs by taking into account both statistical channel state information (CSI) and the interference from the primary network. A statistical-CSI-based MDCM scheme with low complexity is proposed, which is shown to be able to approach the perfect-CSI performance with large multicast group size. Numerical results are also presented to validate the proposed scheme.Third, the fifth and sixth chapter propose two multi-cell MDCM resource allocation for OFDM-based CRNs. Multi-cell is a more common and practical scenario, but due to the existence of out-of-cell interference, the resource allocation problem becomes strongly coupled. Further incorporating MDCM and CR would make the problem even more complex. This paper first analyzes the theoretic performance for multi-cell multicast, then based on game theory, geometric programming, and decomposition theory, proposes two distributed schemes which achieve a good trade-off between performance and complexity.