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

Optimal spectrum utilisation in cognitive network using combined spectrum sharing approach: overlay, underlay and trading

Cognitive radio technology enables unlicensed users (secondary users, SUs) to access the unused spectrum. In the literature, there are three spectrum sharing paradigms that enable SUs to access the licensed spectrum. These access techniques include underlay, overlay and spectrum trading, and have their own drawbacks. To combat these drawbacks, we propose a new approach for each of them and merge them into one combined system. Our overlay scheme provides quick access to the unused spectrum. We propose a new cooperative sensing protocol to reduce the likelihood of interfering with PUs. In order to enable SUs for transmitting simultaneously with PUs, we suggest using our underlay scheme. Our trading scheme allows PUs to trade the unused spectrum for the SUs that require better quality of service. The new combined scheme increases the size of spectrum in the cognitive network. Simulation results show the ability of the new scheme to serve extra traffic

Power Control and Cooperative Sensing in Cognitive Radio

The traditional ways of spectrum management is inefficient as large portions of useable spectrum is left idle most periods of the day hence the call for more dynamic spectrum management techniques. Cognitive Radio (CR) is considered a viable means to vastly improve the efficiency of spectrum since it allows unlicensed users access to licenced spectrum as long as the quality of service is not downgraded. This research investigates the major problems associated with designing CRs. An in-depth analysis shows that the two major problems that hinders the successful design of CR systems are that of spectrum sensing (How the device detects the Primary User (PU)) and Power Control (which focuses on the level of transmit power of CR devices so as not to induce interference to PUs). To solve the problem of power control in this research, we consider a single cell scenario where N CR terminals are operating in a network with a Cognitive base station (CBS) together with one PU along with its Primary Base station (PBS). In the scenario, CR devices will generally seek to improve quality of service by increasing it’s transmit power. This increase introduces interference to the PU. To mitigate this, the CR devices are modelled as players of a non-cooperative game where offending devices are penalised till a Nash equilibrium level is achieved. At this point, the players can no longer influence the state of the game no matter the strategy they chose to play. The work is extended to cover CR internet of things devices by exploiting the adequate path loss exponent for the operational environment. The power control algorithm is compared with two other known power control algorithms and it outperforms them in average power, average SNR and rate of convergence. Spectrum sensing in CRs has been shown in literature to improve when done cooperatively rather than individually. To this end, this research focuses on cooperative sensing which allows the radios to make decision on their channel state based on the combine results of individual radios. The channel is modelled as a frame- by frame structure of equal length using the slotted aloha access contention technique. Each frame has a fixed length and is made up of sensing, prediction and transmission periods. It is seen observed that longer sensing periods results in better sensing results but considerable lower throughput. The scenario researched involves a CR network with K CRs and M sub-channels. It is assumed that the conditions of all sub-channels are equal, and each CR randomly chooses any one to sense and the throughput is measured. The interference caused to the PU are measured by collisions in the system. This are of two types: (1) Collisions with PUs due to missed detections and (2) collisions with other CRs due to access contention. Whenever there is a collision, the packet is withheld by the system and transmission is stopped. The throughput is a measure of successful packet transmissions. The derived algorithm improved the throughput by detecting the optimal sensing period. Using the K-of-M fusion decision rule, the sensing algorithm guarantees that optimal throughput can be achieved when 50% of the cognitive radio correctly detects the state of the spectrum. Cognitive radio throughput will be of very grave importance. Especially in spectrums like TVWSs and radar systems. A throughput model with power control is presented. The aim is to improve the throughput in interweave scenarios

Spectrum Allocation Algorithms for Cognitive Radio Mesh Networks

Empowered by the cognitive radio technology, and motivated by the sporadic channel utilization, both spatially and temporally, dynamic spectrum access networks (also referred to as cognitive radio networks and next generation wireless networks) have emerged as a solution to improve spectrum utilization and provide more flexibility to wireless communication. A cognitive radio network is composed of wireless users, referred to as secondary users, which are allowed to use licensed spectrum bands as long as their are no primary, licensed, users occupying the channel in their vicinity. This restricted spectrum access strategy leads to heterogeneity in channel availability among secondary users. This heterogeneity forms a significant source of performance degradation for cognitive radio networks, and poses a great challenge on protocol design. In this dissertation, we propose spectrum allocation algorithms that take into consideration the heterogeneity property and its effect on the network performance. The spectrum allocation solutions proposed in this dissertation address three major objectives in cognitive radio mesh networks. The first objective is maximizing the network coverage, in terms of the total number of served clients, and at the same time simplifying the communication coordination function. To address this objective, we proposed a received based channel allocation strategy that alleviates the need for a common control channel, thus simplifying the coordination function, and at the same time maximizes the number of clients served with link reliability guarantees. We show the superiority of the proposed allocation strategy over other existing strategies. The second objective is improving the multicast throughput to compensate for the performance degradation caused by channel heterogeneity. We proposed a scheduling algorithm that schedules multicast transmissions over both time and frequency and integrates that with the use of network coding. This algorithm achieves a significant gain, measured as the reduction in the total multicast time, as the simulation results prove. We also proposed a failure recovery algorithm that can adaptively adjust the schedule in response to temporary changes in channel availability. The last objective is minimizing the effect of channel switching on the end-to-end delay and network throughput. Channel switching can be a significant source of delay and bandwidth wastage, especially if the secondary users are utilizing a wide spectrum band. To address this issue, we proposed an on-demand multicast routing algorithm for cognitive radio mesh networks based on dynamic programming. The algorithm finds the best available route in terms of end-to-end delay, taking into consideration the switching latency at individual nodes and the transmission time on different channels. We also presented the extensibility of the proposed algorithm to different routing metric. Furthermore, a route recovery algorithm that takes into consideration the overhead of rerouting and the route cost was also proposed. The gain of these algorithms was proved by simulation

Novel Interference And Spectrum Aware Routing Techniques}{for Cognitive Radio Ad Hoc Networks

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2011Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2011Yüksek hızlı kablosuz ağlara artan rağbet nedeniyle, radyo spektrumu dünya üzerinde en çok kullanılan ve pahalı doğal kaynaklardan biri haline gelmiştir. Lisanslı spektrumu etkin şekilde kullanma ve paylaşmaya olanak sağlaması nedeniyle radyo spektrumundan yararlanma potansiyelini arttıran bilişsel radyo teknolojisi büyük ilgi toplamaktadır. Söz konusu potansiyelden faydalanmak üzere bilişsel radyo ağları tasarlanırken üzerinde önemle durulması gereken en önemli konulardan bir tanesi de yönlendirmedir. Çalışmamızda bilişsel radyo ağlarında kullanılmak üzere önerilen yönlendirme teknikleri hakkında bir bakış açısı sunulmakla beraber asıl olarak girişim ve spektruma dayalı özgün yönlendirme teknikleri önerilmektedir. Öncelikle, spektrum kullanım karakteristikleri ve ağdaki akışların yarattığı girişim göz önüne alınarak yönlendirme ölçütleri tasarlanmıştır. Ayrıca, bilişsel radyo ağları için otonom dağıtık uyarlanır menzil kontrol stratejisi önerilmiştir. Bu önerilere ek olarak dağıtık ve etkin bir kümeleme tabanlı yönlendirme tekniği geliştirilmiştir. Son olarak, bilişsel radyo ağları için otonom dağıtık uyarlanır menzil kontrol stratejisi ve spektrum erişebilirliği ve girişim maliyeti ölçütlerini bir arada kullanan özgün bir yönlendirme tekniği önerilmiştir. Önerilen yeni yönlendirme ölçütlerinin kullanımı nedeniyle önerilen teknik trafiği kullanılabilir spektrumun daha çok ve girişimin daha az olduğu rotalara yönlendirmektedir. NS2 benzetim ortamı kullanılarak gerçekleştirilen testler, önerilen yöntemlerin bilişsel radyo ağlarına uygunluğunu ve ağ başarımını arttırdığını göstermiştir. Ayrıca güncel bilişsel radyo teknolojisini kullanan diğer yöntemlerle karşılaştırıldığında önerilen tekniklerin hem uçtan uca veri aktarımını arttırdığı hem de uçtan uca gecikmeyi azalttığı ve başarımlarının daha yüksek olduğu gözlemlenmiştir.Radio spectrum has become one of the most heavily used and expensive natural resource around the world because of the growing demand for high-speed wireless networks. Cognitive radio has received great attention due to tremendous potential to improve the utilization of the radio spectrum by efficiently reusing and sharing the licensed spectrum. To design such mobile cognitive radio networks, routing is one of the key challenging issues to be addressed and requires deep investigation. This study gives some insights about the potential routing approaches that can be employed, and suggests novel interference and spectrum aware routing techniques for cognitive radio networks. First, the spectrum usage characteristics, and the interference created by existing flows in the network both from the primary and secondary users are taken into account to define routing metrics. Next, an autonomous distributed adaptive transmission range control scheme for cognitive radio networks is proposed. A distributed and efficient cluster based routing technique, which benefits from new metrics, is also introduced. The last proposed routing algorithm incorporates novel metrics and autonomous distributed adaptive transmission range control mechanism to provide self adaptivity. As a consequence, the proposed protocol routes traffic across paths with better spectrum availability and reduced interference via these new routing metrics. Extensive experimental evaluations are performed in the ns2 simulator to show that proposed protocols provide better adaptability to the environment and maximize throughput, minimize end-to-end delay in a number of realistic scenarios and outperforms recently proposed routing protocols developed for cognitive radio networks.DoktoraPh

Machine Learning Approach for Spectrum Sharing in the Next Generation Cognitive Mesh Network

Nowadays, there is an unexpected explosion in the demand for wireless network resources. This is due to the dramatic increase in the number of the emerging web-based services. For wireless computer network, limited bandwidth along with the transmission quality requirements for users, make quality of service (QoS) provisioning a very challenging problem. To overcome spectrum scarcity problem, Federal Communications Commission (FCC) has already started working on the concept of spectrum sharing where unlicensed users (secondary users, SUs) can share the spectrum with licensed users (primary users, PUs), provided they respect PUs rights to use spectrum exclusively. Cognitive technology presents a revolutionary wireless communication where users can exploit the spectrum dynamically. The integration of cognitive technology capability into the conventional wireless networks is perhaps the significant promising architectural upgrade in the next generation of wireless network that helps to solve spectrum scarcity problem. In this work, we propose integrating cognitive technology with wireless mesh network to serve the maximum number of SUs by utilizing the limited bandwidth efficiently. The architecture for this network is selected first. In particular, we introduce the cluster-based architecture, signaling protocols, spectrum management scheme and detailed algorithms for the cognitive cycle. This new architecture is shown to be promising for the cognitive network. In order to manage the transmission power for the SUs in the cognitive wireless mesh network, a dynamic power management framework is developed based on machine learning to improve spectrum utilization while satisfying users requirements. Reinforcement learning (RL) is used to extract the optimal control policy that allocates spectrum and transmission powers for the SUs dynamically. RL is used to help users to adapt their resources to the changing network conditions. RL model considers the spectrum request arrival rate of the SUs, the interference constraint for the PUs, the physical properties of the channel that is selected for the SUs, PUs activities, and the QoS for SUs. In our work, PUs trade the unused spectrum to the SUs. For this sharing paradigm, maximizing the revenue is the key objective of the PUs, while that of the SUs is to meet their requirements and obtain service from the rented spectrum. However, PUs have to maintain their QoS when trading their spectrum. These complex conflicting objectives are embedded in our machine learning model. The objective function is defined as the net revenue gained by PUs from renting some of their spectrum. We use a machine learning to help the PUs to make a decision about the optimal size and price of the offered spectrum for trading. The trading model considers the QoS for PUs and SUs, traffic load at the PUs, the changes in the network conditions, and the revenues of the PUs. Finally, we integrate all the mechanisms described above to build a new cognitive network where users can interact intelligently with network conditions

Resource Management in Cognitive Radio Networks

In the last decade, the world has witnessed rapid increasing applications of wireless networks. However, with the fixed spectrum allocation policy that has been used since the beginning of the spectrum regulation to assign different spectrum bands to different wireless applications, it has been observed that most of the allocated spectrum bands are underutilized. Therefore, if these bands can be opportunistically used by new emerging wireless networks, the spectrum scarcity can be resolved. Cognitive Radio (CR) is a revolutionary and promising technology that can identify and then exploit the spectrum opportunities. In Cognitive Radio Networks (CRNs), the spectrum can be utilized by two kinds of users: Primary Users (PUs) having exclusive licenses to use certain spectrum bands for specific wireless applications, and Secondary Users (SUs) having no spectrum licenses but seeking for any spectrum opportunities. The SUs can make use of the licensed unused spectrum if they do not make any harmful interference to the PUs. However, the variation of the spectrum availability over the time and locations, due to the coexistence with the PUs, and the spread of the spectrum opportunities over wide spectrum bands create a unique trait of the CRNs. This key trait poses great challenges in different aspects of the radio resource management in CRNs such as the spectrum sensing, spectrum access, admission control, channel allocation, Quality-of-Service (QoS) provisioning, etc. In this thesis, we study the resource management of both single-hop and multi-hop CRNs. Since most of the new challenges in CRNs can be tackled by designing an efficient Medium Access Control (MAC) framework, where the solutions of these challenges can be integrated for efficient resource management, we firstly propose a novel MAC framework that integrates a kind of cooperative spectrum sensing method at the physical layer into a cooperative MAC protocol considering the requirements of both the SUs and PUs. For spectrum identification, a computationally simple but efficient sensing algorithm is developed, based on an innovative deterministic sensing policy, to assist each sensing user for identifying the optimum number of channels to sense and the optimum sensing duration. We then develop an admission control scheme and channel allocation policy that can be integrated in the proposed MAC framework to regulate the number of sensing users and number of access users; therefore, the spectrum identification and exploitation can be efficiently balanced. Moreover, we propose a QoS-based spectrum allocation framework that jointly considers the QoS provisioning for heterogeneous secondary Real-Time (RT) and Non-Real Time (NRT) users with the spectrum sensing, spectrum access decision, and call admission control. We analyze the proposed QoS-based spectrum allocation framework and find the optimum numbers of the RT and NRT users that the network can support. Finally, we introduce an innovative user clustering scheme to efficiently manage the spectrum identification and exploitation in multi-hop ad hoc CRNs. We group the SUs into clusters based on their geographical locations and occurring times and use spread spectrum techniques to facilitate using one frequency for the Common Control Channels (CCCs) of the whole secondary network and to reduce the co-channel interference between adjacent clusters by assigning different spreading codes for different clusters. The research results presented in this thesis contribute to realize the concept of the CRNs by developing a practical MAC framework, spectrum sensing, spectrum allocation, user admission control, and QoS provisioning for efficient resource management in these promising networks