New Approaches in Cognitive Radios using Evolutionary Algorithms

Cognitive radio has claimed a promising technology to exploit the spectrum in an ad hoc network. Due many techniques have become a topic of discussion on cognitive radios, the aim of this paper was developed a contemporary survey of evolutionary algorithms in Cognitive Radio. According to the art state, this work had been collected the essential contributions of cognitive radios with the particularity of base they research in evolutionary algorithms. The main idea was classified the evolutionary algorithms and showed their fundamental approaches. Moreover, this research will be exposed some of the current issues in cognitive radios and how the evolutionary algorithms will have been contributed. Therefore, current technologies have matters presented in optimization, learning, and classification over cognitive radios where evolutionary algorithms can be presented big approaches. With a more comprehensive and systematic understanding of evolutionary algorithms in cognitive radios, more research in this direction may be motivated and refined

Adaptive and autonomous protocol for spectrum identification and coordination in ad hoc cognitive radio network

The decentralised structure of wireless Ad hoc networks makes them most appropriate for quick and easy deployment in military and emergency situations. Consequently, in this thesis, special interest is given to this form of network. Cognitive Radio (CR) is defined as a radio, capable of identifying its spectral environment and able to optimally adjust its transmission parameters to achieve interference free communication channel. In a CR system, Dynamic Spectrum Access (DSA) is made feasible. CR has been proposed as a candidate solution to the challenge of spectrum scarcity. CR works to solve this challenge by providing DSA to unlicensed (secondary) users. The introduction of this new and efficient spectrum management technique, the DSA, has however, opened up some challenges in this wireless Ad hoc Network of interest; the Cognitive Radio Ad Hoc Network (CRAHN). These challenges, which form the specific focus of this thesis are as follows: First, the poor performance of the existing spectrum sensing techniques in low Signal to Noise Ratio (SNR) conditions. Secondly the lack of a central coordination entity for spectrum allocation and information exchange in the CRAHN. Lastly, the existing Medium Access Control (MAC) Protocol such as the 802.11 was designed for both homogeneous spectrum usage and static spectrum allocation technique. Consequently, this thesis addresses these challenges by first developing an algorithm comprising of the Wavelet-based Scale Space Filtering (WSSF) algorithm and the Otsu's multi-threshold algorithm to form an Adaptive and Autonomous WaveletBased Scale Space Filter (AWSSF) for Primary User (PU) sensing in CR. These combined algorithms produced an enhanced algorithm that improves detection in low SNR conditions when compared to the performance of EDs and other spectrum sensing techniques in the literature. Therefore, the AWSSF met the performance requirement of the IEEE 802.22 standard as compared to other approaches and thus considered viable for application in CR. Next, a new approach for the selection of control channel in CRAHN environment using the Ant Colony System (ACS) was proposed. The algorithm reduces the complex objective of selecting control channel from an overtly large spectrum space,to a path finding problem in a graph. We use pheromone trails, proportional to channel reward, which are computed based on received signal strength and channel availability, to guide the construction of selection scheme. Simulation results revealed ACS as a feasible solution for optimal dynamic control channel selection. Finally, a new channel hopping algorithm for the selection of a control channel in CRAHN was presented. This adopted the use of the bio-mimicry concept to develop a swarm intelligence based mechanism. This mechanism guides nodes to select a common control channel within a bounded time for the purpose of establishing communication. Closed form expressions for the upper bound of the time to rendezvous (TTR) and Expected TTR (ETTR) on a common control channel were derived for various network scenarios. The algorithm further provides improved performance in comparison to the Jump-Stay and Enhanced Jump-Stay Rendezvous Algorithms. We also provided simulation results to validate our claim of improved TTR. Based on the results obtained, it was concluded that the proposed system contributes positively to the ongoing research in CRAHN

Efficient spectrum-handoff schemes for cognitive radio networks

Radio spectrum access is important for terrestrial wireless networks, commercial earth observations and terrestrial radio astronomy observations. The services offered by terrestrial wireless networks, commercial earth observations and terrestrial radio astronomy observations have evolved due to technological advances. They are expected to meet increasing users' demands which will require more spectrum. The increasing demand for high throughput by users necessitates allocating additional spectrum to terrestrial wireless networks. Terrestrial radio astronomy observations s require additional bandwidth to observe more spectral windows. Commercial earth observation requires more spectrum for enhanced transmission of earth observation data. The evolution of terrestrial wireless networks, commercial earth observations and terrestrial radio astronomy observations leads to the emergence of new interference scenarios. For instance, terrestrial wireless networks pose interference risks to mobile ground stations; while inter-satellite links can interfere with terrestrial radio astronomy observations. Terrestrial wireless networks, commercial earth observations and terrestrial radio astronomy observations also require mechanisms that will enhance the performance of their users. This thesis proposes a framework that prevents interference between terrestrial wireless networks, commercial earth observations and terrestrial radio astronomy observations when they co-exist; and enhance the performance of their users. The framework uses the cognitive radio; because it is capable of multi-context operation. In the thesis, two interference avoidance mechanisms are presented. The first mechanism prevents interference between terrestrial radio astronomy observations and inter-satellite links. The second mechanism prevent interference between terrestrial wireless networks and the commercial earth observation ground segment. The first interference reductionmechanism determines the inter-satellite link transmission duration. Analysis shows that interference-free inter-satellite links transmission is achievable during terrestrial radio astronomy observation switching for up to 50.7 seconds. The second mechanism enables the mobile ground station, with a trained neural network, to predict the terrestrial wireless network channel idle state. The prediction of the TWN channel idle state prevents interference between the terrestrial wireless network and the mobile ground station. Simulation shows that incorporating prediction in the mobile ground station enhances uplink throughput by 40.6% and reduces latency by 18.6%. In addition, the thesis also presents mechanisms to enhance the performance of the users in terrestrial wireless network, commercial earth observations and terrestrial radio astronomy observations. The thesis presents mechanisms that enhance user performance in homogeneous and heterogeneous terrestrial wireless networks. Mechanisms that enhance the performance of LTE-Advanced users with learning diversity are also presented. Furthermore, a future commercial earth observation network model that increases the accessible earth climatic data is presented. The performance of terrestrial radio astronomy observation users is enhanced by presenting mechanisms that improve angular resolution, power efficiency and reduce infrastructure costs

Improved Channel Allocation Scheme for Cognitive Radio Networks

In recent years, wireless channel optimization technologies witnessed tremendous improvements. In this regard, research for developing wireless spectrum for accommodating a wider range of wireless devices increased. This also helped in resolving spectrum scarcity issues. Cognitive Radio (CR) is a type of wireless communication in which a transceiver can intelligently detect which communication channels are being used. To avoid interference, it instantly moves traffic into vacant channels by avoiding the occupied ones. Cognitive Radio (CR) technology showed the potential to deal with the spectrum shortage problem. The spectrum assignment is often considered as a key research challenge in Cognitive Radio Networks (CRNs). In this paper, an evolutionary optimization algorithm is proposed for channel assignment in CRNs. Evolutionary algorithms are inspired by some type of biological evolution technique. In the proposed technology we used Particle Swarm Optimization (PSO). The resulting algorithm is called differential evolution-based particle swarm optimization with the repair process (DEPSO-RP). Moreover, a repair process is introduced to remove conflicts among secondary users (SUs) to increase the spectrum in CRNs. The performance of DEPSO-RP spectrum assignment algorithm has been evaluated by extensive simulations. The proposed spectrum assignment algorithm showed better performance regarding channel assignment in comparison with other existing algorithms in the literature

A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime

Ant-colony and nature-inspired heuristic models for NOMA systems: a review

The increasing computational complexity in scheduling the large number of users for non-orthogonal multiple access (NOMA) system and future cellular networks lead to the need for scheduling models with relatively lower computational complexity such as heuristic models. The main objective of this paper is to conduct a concise study on ant-colony optimization (ACO) methods and potential nature-inspired heuristic models for NOMA implementation in future high-speed networks. The issues, challenges and future work of ACO and other related heuristic models in NOMA are concisely reviewed. The throughput result of the proposed ACO method is observed to be close to the maximum theoretical value and stands 44% higher than that of the existing method. This result demonstrates the effectiveness of ACO implementation for NOMA user scheduling and grouping