Spectrum and transmission range aware clustering for cognitive radio ad hoc networks

Cognitive radio network (CRN) is a promising technology to overcome the problem of spectrum shortage by enabling the unlicensed users to access the underutilization spectrum bands in an opportunistic manner. On the other hand, the hardness of establishing a fixed infrastructure in specific situations such as disaster recovery, and battlefield communication imposes the network to have an ad hoc structure. Thus, the emerging of Cognitive Radio Ad Hoc Network (CRAHN) has accordingly become imperative. However, the practical implementation of CRAHN faced many challenges such as control channel establishment and the scalability problems. Clustering that divides the network into virtual groups is a reliable solution to handle these issues. However, previous clustering methods for CRAHNs seem to be impractical due to issues regarding the high number of constructed clusters and unfair load distribution among the clusters. Additionally, the homogeneous channel model was considered in the previous work despite channel heterogeneity is the CRN features. This thesis addressed these issues by proposing two clustering schemes, where the heterogeneous channel is considered in the clustering process. First, a distributed clustering algorithm called Spectrum and Transmission Range Aware Clustering (STRAC) which exploits the heterogeneous channel concept is proposed. Here, a novel cluster head selection function is formulated. An analytical model is derived to validate the STRAC outcomes. Second, in order to improve the bandwidth utilization, a Load Balanced Spectrum and Transmission Range Aware Clustering (LB-STRAC) is proposed. This algorithm jointly considers the channel heterogeneity and load balancing concepts. Simulation results show that on average, STRAC reduces the number of constructed clusters up to 51% compared to conventional clustering technique, Spectrum Opportunity based Clustering (SOC). In addition, STRAC significantly reduces the one-member cluster ratio and re-affiliation ratio in comparison to non-heterogeneity channel consideration schemes. LB-STRAC further improved the clustering performance by outperforming STRAC in terms of uniformity and equality of the traffic load distribution among all clusters with fair spectrum allocation. Moreover, LB-STRAC has been shown to be very effective in improving the bandwidth utilization. For equal traffic load scenario, LB-STRAC on average improves the bandwidth utilization by 24.3% compared to STRAC. Additionally, for varied traffic load scenario, LB-STRAC improves the bandwidth utilization by 31.9% and 25.4% on average compared with STRAC for non-uniform slot allocation and for uniform slot allocation respectively. Thus, LB-STRAC is highly recommended for multi-source scenarios such as continuous monitoring applications or situation awareness applications

Airborne Directional Networking: Topology Control Protocol Design

This research identifies and evaluates the impact of several architectural design choices in relation to airborne networking in contested environments related to autonomous topology control. Using simulation, we evaluate topology reconfiguration effectiveness using classical performance metrics for different point-to-point communication architectures. Our attention is focused on the design choices which have the greatest impact on reliability, scalability, and performance. In this work, we discuss the impact of several practical considerations of airborne networking in contested environments related to autonomous topology control modeling. Using simulation, we derive multiple classical performance metrics to evaluate topology reconfiguration effectiveness for different point-to-point communication architecture attributes for the purpose of qualifying protocol design elements

N-GAIR: Non-greedy asynchronous interference reduction algorithm in wireless networks

In this paper, we consider optimum channel/frequency allocation problem in wireless networks by reducing total network interference signal powers, which is an NP-complete problem. Its optimum solution for general wireless networks for even 2-channel case is not known. Turning the channel/frequency allocation problem into a maxCut graph partitioning problem, we i) propose a spectral clustering based channel allocation algorithm, called SpecPure, and ii) propose and analyze a novel Non-Greedy Asynchronous Interference Reduction Algorithm for Wireless Networks, called N-GAIR, and iii) extend the results in [1] to the case where the number of channels is arbitrary. By simulating various CDMA based ad-hoc networks, we examine various scenarios to compare the performances of the proposed algorithms with the reference algorithm. We draw various conclusions for different network scenarios. For example, the results show that the SpecPure algorithm performs well for "symmetric" base locations scenarios, while the N-GAIR performs best for random base locations scenarios. The results confirm the effectiveness of the proposed algorithms, which can be adopted by any cellular, cognitive, ad-hoc or mesh type radio networks

Recent Trends in Communication Networks

In recent years there has been many developments in communication technology. This has greatly enhanced the computing power of small handheld resource-constrained mobile devices. Different generations of communication technology have evolved. This had led to new research for communication of large volumes of data in different transmission media and the design of different communication protocols. Another direction of research concerns the secure and error-free communication between the sender and receiver despite the risk of the presence of an eavesdropper. For the communication requirement of a huge amount of multimedia streaming data, a lot of research has been carried out in the design of proper overlay networks. The book addresses new research techniques that have evolved to handle these challenges

The Spectrum Shortage Problem: Channel Assignment and Cognitive Networks

Recent studies have shown that the proliferation of wireless applications and services, experienced in the last decade, is leading to the challenging spectrum shortage problem. We provide a general overview regarding the spectrum shortage problem from the point of view of different technologies. First, we propose solutions based on multi-radio multi-channel wireless mesh networks in order to improve the usage of unlicensed wireless resources. Then, we move our focus on cognitive networks in order to analyze issues and solutions to opportunistically use licensed wireless resources. In wireless mesh networks, the spectrum shortage problem is addressed equipping each device with multiple radios which are turned on different orthogonal channels. We propose G-PaMeLA, which splits in local sub-problems the joint channel assignment and routing problem in multi-radio multi-channel wireless mesh networks. Results demonstrate that G-PaMeLA significantly improves network performance, in terms of packet loss and throughput fairness compared to algorithms in the literature. Unfortunately, even if orthogonal channels are used, wireless mesh networks result in what is called spectrum overcrowding. In order to address the spectrum overcrowding problem, careful analysis on spectrum frequencies has been conducted. These studies identified the possibility of transmitting on licensed channels, which are surprisingly underutilized. With the aim of addressing the resources problem using licensed channels, cognitive access and mesh networks have been developed. In cognitive access networks, we identify as the major problem the self-coexistence, which is the ability to access channels on a non-interfering basis with respect to licensed and unlicensed wireless devices. We propose two game theoretic frameworks which differentiate in having non-cooperative (NoRa) and cooperative (HeCtor) cognitive devices, respectively. Results show that HeCtor achieves higher throughput than NoRa but at the cost of higher computational complexity, which leads to a smaller throughput in cases where rapid changes occur in channels' occupancy. In contrast, NoRa attains the same throughput independent of the variability in channels' occupancy, hence cognitive devices adapt faster to such changes. In cognitive mesh networks, we analyze the coordination problem among cognitive devices because it is the major concern in implementing mesh networks in environments which change in time and space. We propose Connor, a clustering algorithm to address the coordination problem, which establishes common local control channels. Connor, in contrast with existing algorithms in the literature, does not require synchronization among cognitive mesh devices and allows a fast re-clustering when changes occur in channel's occupancy by licensed users. Results show that Connor performs better than existing algorithms in term of number of channels used for control purposes and time to reach and stay on stable configurations