On spectrum allocation strategies in Cognitive Radio Networks

Due to the temporal and spatial underutilization of licensed spectrum bands, as well as the crowdedness of unlicensed bands, a new spectrum access paradigm has been recently proposed namely, Cognitive Radio (CR). CR enables users to adjust their transceivers\u27 frequencies depending on the availability of licensed frequency bands which are otherwise unused by their licensees, called Primary Users (PUs). Thus, unlicensed wireless users, called Secondary Users (SUs) can dynamically and opportunistically access unused licensed bands in order to improve their throughput and service reliability. Whenever the licensed users, or the PUs, become active, SUs must vacate their bands. This dissertation is concerned with the operation of Cognitive Radio Networks (CRNs), and deals with four important problems. First, a performance model to study heterogeneous channel access in CRNs is presented. In this model, there are two types of licensed channels, where one type has a larger bandwidth, and hence a higher service rate for SUs. Therefore, SUs prefer to use such channels, if available, over channels in the second type which have a lower service rate. SUs may also switch from the second to the first type of channels when they become available, even if their current channels are still available. We also model the SUs\u27 sensing process, and derive several SUs\u27 performance metrics including average waiting time. Numerical results show that our proposed operational model outperforms a baseline model that does not support prioritized access. Second, we introduce a low overhead scheme for the uplink channel allocation within a single cell of CRNs operating as Wireless Mesh Networks (CR-WMNs). The scheme does not rely on using a Common Control Channel (CCC). The proposed mechanism is based on the use of Physical Layer Network Coding (PNC), in which two (or three) Secondary Users (SUs) who are requesting uplink channel allocation are allowed to transmit synchronously over a randomly selected channel from a set of available channels, and without coordination. A Mesh Router (MR) which is listening to these transmissions, and is in charge of channel allocation, can detect up to 2 (or 3) requests, on the same channel due to the use of PNC, and replies back with a control packet which contains information about channel assignment. Our proposed mechanisms significantly outperform traditional schemes that rely on using one CCC, or do not use PNC, in terms of channel allocation overhead time. Third, we also propose to enable SUs to recover their packets which collide with PUs\u27 transmissions when a PU becomes active for two scenarios, based on the received phase shifts. When a collision occurs between an SU and a PU transmitters, the SU\u27s receiver considers the PU\u27s transmission as an interference, and hence, cancels its effect in order to recover its corresponding received packet\u27s signals. Recovering collided packets, instead of retransmitting them saves transmitters\u27 energy. Numerical results show that a high percentage of energy can be saved over the traditional scheme, in which our packets recovery mechanisms are not employed. Finally, we propose a novel multicast resilient routing approach to select primary and backup paths from an SU source to SUs destinations. Our approach employs a multilayer hyper-graph, in order to model the network, e.g., channels. The primary paths to destination SUs are selected to minimize the end-to-end delay which takes into consideration channels switching latency and transmission delay. To protect the multicast session, we find a backup path for primary path, if feasible, such that these two paths are shared risk hyper-edge disjoint, in order to prevent a concurrent failure for these two paths, when the corresponding PU for this hyper-edge becomes active. Our simulation results show that increasing the number of available channels, increase the number of feasible primary and backup paths, and the maximum path delay decreases almost linearly

Optimizing performance and energy efficiency of group communication and internet of things in cognitive radio networks

Data traffic in the wireless networks has grown at an unprecedented rate. While traditional wireless networks follow fixed spectrum assignment, spectrum scarcity problem becomes a major challenge in the next generations of wireless networks. Cognitive radio is a promising candidate technology that can mitigate this critical challenge by allowing dynamic spectrum access and increasing the spectrum utilization. As users and data traffic demands increases, more efficient communication methods to support communication in general, and group communication in particular, are needed. On the other hand, limited battery for the wireless network device in general makes it a bottleneck for enhancing the performance of wireless networks. In this thesis, the problem of optimizing the performance of group communication in CRNs is studied. Moreover, energy efficient and wireless-powered group communication in CRNs are considered. Additionally, a cognitive mobile base station and a cognitive UAV are proposed for the purpose of optimizing energy transfer and data dissemination, respectively. First, a multi-objective optimization for many-to-many communication in CRNs is considered. Given a many-to-many communication request, the goal is to support message routing from each user in the many-to-many group to each other. The objectives are minimizing the delay and the number of used links and maximizing data rate. The network is modeled using a multi-layer hyper graph, and the secondary users\u27 transmission is scheduled after establishing the conflict graph. Due to the difficulty of solving the problem optimally, a modified version of an Ant Colony meta-heuristic algorithm is employed to solve the problem. Additionally, energy efficient multicast communication in CRNs is introduced while considering directional and omnidirectional antennas. The multicast service is supported such that the total energy consumption of data transmission and channel switching is minimized. The optimization problem is formulated as a Mixed Integer Linear Program (MILP), and a heuristic algorithm is proposed to solve the problem in polynomial time. Second, wireless-powered machine-to-machine multicast communication in cellular networks is studied. To incentivize Internet of Things (IoT) devices to participate in forwarding the multicast messages, each IoT device participates in messages forwarding receives Radio Frequency (RF) energy form Energy Transmitters (ET) not less than the amount of energy used for messages forwarding. The objective is to minimize total transferred energy by the ETs. The problem is formulated mathematically as a Mixed Integer Nonlinear Program (MINLP), and a Generalized Bender Decomposition with Successive Convex Programming (GBD-SCP) algorithm is introduced to get an approximate solution since there is no efficient way in general to solve the problem optimally. Moreover, another algorithm, Constraints Decomposition with SCP and Binary Variable Relaxation (CDR), is proposed to get an approximate solution in a more efficient way. On the other hand, a cognitive mobile station base is proposed to transfer data and energy to a group of IoT devices underlying a primary network. Total energy consumed by the cognitive base station in its mobility, data transmission and energy transfer is minimized. Moreover, the cognitive base station adjusts its location and transmission power and transmission schedule such that data and energy demands are supported within a certain tolerable time and the primary users are protected from harmful interference. Finally, we consider a cognitive Unmanned Aerial Vehicle (UAV) to disseminate data to IoT devices. The UAV senses the spectrum and finds an idle channel, then it predicts when the corresponding primary user of the selected channel becomes active based on the elapsed time of the off period. Accordingly, it starts its transmission at the beginning of the next frame right after finding the channel is idle. Moreover, it decides the number of the consecutive transmission slots that it will use such that the number of interfering slots to the corresponding primary user does not exceed a certain threshold. A mathematical problem is formulated to maximize the minimum number of bits received by the IoT devices. A successive convex programming-based algorithm is used to get a solution for the problem in an efficiency way. It is shown that the used algorithm converges to a Kuhn Tucker point

On the optimal operation of wireless networks

With the ever increasing mobile traffic in wireless networks, radio frequency spectrum is becoming limited and overcrowded. To address the radio frequency spectrum scarcity problem, researchers proposed advanced radio technology-Cognitive Radio to make use of the uncommonly used and under-utilized licensed bands to improve overall spectrum efficiency. Mobile service providers also deploy small base stations on the streets, into shopping center and users\u27 households in order to improve spectrum efficiency per area. In this thesis, we study cooperation schemes in cognitive radio networks as well as heterogeneous networks to reuse the existing radio frequency spectrum intelligently and improve network throughput and spectrum efficiency, reduce network power consumption and provide network failure protection capability. In the first work of the thesis, we study a multicast routing problem in Cognitive Ratio Networks (CRNs). In this work, all Secondary Users (SUs) are assumed not self interested and they are willing to provide relay service for source SUs. We propose a new network modeling method, where we model CRNs using a Multi-rate Multilayer Hyper-Graph (MMHG). Given a multicast session of the MMHG, our goal is to find the multicast routing trees that minimize the worst case end-to-end delay, maximize the multicast rate and minimize the number of transmission links used in the multicast tree. We apply two metaheuristic algorithms (Multi-Objective Ant Colony System optimization algorithm (MOACS) and Archived Multi-Objective Simulated Annealing Optimization Algorithm (AMOSA)) in solving the problem. We also study the scheduling problem of multicast routing trees obtained from the MMHG model. In the second work of the thesis, we study the cell outage compensation function of the self-healing mechanism using network cooperation scheme. In a heterogeneous network environment with densely deployed Femto Base Stations (FBSs), we propose a network cooperation scheme for FBSs using Coordinated Multi-Point (CoMP) transmission and reception with joint processing technique. Different clustering methods are studied to improve the performance of the network cooperation scheme. In the final work of the thesis, we study the user cooperative multi-path routing solution for wireless Users Equipment (UEs)\u27 streaming application using auction theory. We assume that UEs use multi-path transport layer service, and establish two paths for streaming events, one path goes through its cellular link, another path is established using a Wi-Fi connection with a neighbor UE. We study user coordinated multi-path routing solution with two different energy cost functions (LCF and EAC) and design user cooperative real-time optimization and failure protection operations for the streaming application. To stimulate UEs to participate into the user cooperation operation, we design a credit system enabled with auction mechanism. Simulation results in this thesis show that optimal cooperation operations among network devices to reuse the existing spectrum wisely are able to improve network performance considerably. Our proposed network modeling approach in CRN helps reduce the complicated multicast routing problem to a simple graph problem, and the proposed algorithms can find most of the optimal multicast routing trees in a short amount of time. In the second and third works, our proposed network cooperation and user cooperation approaches are shown to provide better UEs\u27 throughput compared to non-cooperation schemes. The network cooperation approach using CoMP provides failure compensation capability by preventing the system sum rate loss from having the same speed of radio resource loss, and this is done without using additional radio resources and will not have a significant adverse effect on the performance of other UEs. The user cooperation approach shows great advantage in improving service rate, improving streaming event success rate and reducing energy consumption compared to non-cooperation solution

Robust provisioning of multicast sessions in cognitive radio networks

Today\u27s wireless networks use fixed spectrum over long term and fixed geographical regions. However, spectrum utilization varies by time and location, which leads to temporal and special spectrum underutilization. Therefore, new ways to improve spectrum utilization are needed. Cognitive radio is an emerging technology that enables dynamic sharing of the spectrum in order to overcome spectrum underutilization problem. Users in cognitive radio networks are either primary or secondary users. A primary user is the user who is licensed to use a channel, and has priority to use it over any other user. The secondary user uses a licensed spectrum channel opportunistically when a primary user is idle. Hence, it has to vacate the channel within a certain tolerable interference time when the primary user appears. As a result of this, the secondary user needs to find backup channels to protect the links it is using from primary user\u27s interruption. In this thesis, we concentrate on supporting the multicast service mode using cognitive radio networks. Moreover, we are concerned with supporting this mode of service such that it is robust in the face of failures. The type of failures we are interested in is channel disappearance due to the resumption of activities by primary users. We develop three algorithms which provide robust multicasting in such networks. Our three proposed algorithms are: 1) multicast sessions protection without link-sharing, 2) multicast sessions protection with link-sharing and 3) multicast sessions protection using rings. These algorithms provision multiple multicast sessions, and protect them against single primary user interruption at a time. They also take into account that the activities of a primary user may disrupt communication in several groups, of secondary users, which are referred to as Shared Primary User Risk Group (SPURG). The objective of the proposed algorithms is to increase the number of sessions that can be accommodated in the network and minimize the cost of provisioning the sessions. Multicast sessions protection with/without link-sharing algorithms generate a primary tree for each multicast session, and protect each link of it using a backup tree. Multicast sessions protection with link-sharing allows backup trees to share some links of the primary tree within the same session, and share some links within backup trees for any session. In the third algorithm, a ring is generated where it starts and ends at the source node, and passes through all destination nodes. Also, we compare the performances of our three proposed algorithms. Simulation results show that the number of accommodated sessions in the network increases and the cost of multicast sessions decreases when the number of available channels increases or the session size decreases. Also, multicast sessions protection with link-sharing algorithm outperforms the other two algorithms in terms of the number of sessions in the network. On the other hand, multicast sessions protection using rings achieves the lowest cost for multicast sessions compared with the other two proposed algorithms

On spectrum allocation strategies in Cognitive Radio Networks

Due to the temporal and spatial underutilization of licensed spectrum bands, as well as the crowdedness of unlicensed bands, a new spectrum access paradigm has been recently proposed namely, Cognitive Radio (CR). CR enables users to adjust their transceivers' frequencies depending on the availability of licensed frequency bands which are otherwise unused by their licensees, called Primary Users (PUs). Thus, unlicensed wireless users, called Secondary Users (SUs) can dynamically and opportunistically access unused licensed bands in order to improve their throughput and service reliability. Whenever the licensed users, or the PUs, become active, SUs must vacate their bands. This dissertation is concerned with the operation of Cognitive Radio Networks (CRNs), and deals with four important problems. First, a performance model to study heterogeneous channel access in CRNs is presented. In this model, there are two types of licensed channels, where one type has a larger bandwidth, and hence a higher service rate for SUs. Therefore, SUs prefer to use such channels, if available, over channels in the second type which have a lower service rate. SUs may also switch from the second to the first type of channels when they become available, even if their current channels are still available. We also model the SUs' sensing process, and derive several SUs' performance metrics including average waiting time. Numerical results show that our proposed operational model outperforms a baseline model that does not support prioritized access. Second, we introduce a low overhead scheme for the uplink channel allocation within a single cell of CRNs operating as Wireless Mesh Networks (CR-WMNs). The scheme does not rely on using a Common Control Channel (CCC). The proposed mechanism is based on the use of Physical Layer Network Coding (PNC), in which two (or three) Secondary Users (SUs) who are requesting uplink channel allocation are allowed to transmit synchronously over a randomly selected channel from a set of available channels, and without coordination. A Mesh Router (MR) which is listening to these transmissions, and is in charge of channel allocation, can detect up to 2 (or 3) requests, on the same channel due to the use of PNC, and replies back with a control packet which contains information about channel assignment. Our proposed mechanisms significantly outperform traditional schemes that rely on using one CCC, or do not use PNC, in terms of channel allocation overhead time. Third, we also propose to enable SUs to recover their packets which collide with PUs' transmissions when a PU becomes active for two scenarios, based on the received phase shifts. When a collision occurs between an SU and a PU transmitters, the SU's receiver considers the PU's transmission as an interference, and hence, cancels its effect in order to recover its corresponding received packet's signals. Recovering collided packets, instead of retransmitting them saves transmitters' energy. Numerical results show that a high percentage of energy can be saved over the traditional scheme, in which our packets recovery mechanisms are not employed. Finally, we propose a novel multicast resilient routing approach to select primary and backup paths from an SU source to SUs destinations. Our approach employs a multilayer hyper-graph, in order to model the network, e.g., channels. The primary paths to destination SUs are selected to minimize the end-to-end delay which takes into consideration channels switching latency and transmission delay. To protect the multicast session, we find a backup path for primary path, if feasible, such that these two paths are shared risk hyper-edge disjoint, in order to prevent a concurrent failure for these two paths, when the corresponding PU for this hyper-edge becomes active. Our simulation results show that increasing the number of available channels, increase the number of feasible primary and backup paths, and the maximum path delay decreases almost linearly.</p