Improved Resource Allocation for TV White Space Network Based on Modified Firefly Algorithm

There is continued increased demand for dynamic spectrum access of TV White Spaces (TVWS) due to growing need for wireless broadband. Some of the use cases such as cellular (2G/3G/4G/5G) access to TVWS may have a high density of users that want to make use of TVWS. When there is a high density of secondary users (SUs) in a TVWS network, there is possibility of high interference among SUs that exceeds the desired threshold and also harmful interference to primary users (PUs). Optimization of resource allocation (power and spectrum allocation) is therefore necessary so as to protect PUs against harmful interference and to reduce the level of interference among SUs. Existing resource allocation optimization algorithms for a TVWS network ignore adjacent channel interference, interference among SUs or apply greedy algorithms which result in sub-optimal resource allocation. In this paper we propose an improved resource allocation algorithm based on continuous-binary firefly algorithm. Simulation is done using Matlab. Simulation results show that the proposed algorithm improves the SU sum throughput and SU signal to interference noise(SINR) ratio in the secondary network

Survey on Optimization Methods For Spectrum Sensing in Cognitive Radio Network

A cognitive radio is a capable Technology, which has provided a great innovation in wireless communication system as to improve the efficiency of the electromagnetic spectrum utilization in wireless network. The technology allows unlicensed user or secondary user to use the vacant spectrum of licensed user through dynamic channel assignment strategies to improve the spectral utilization and hence cognitive radio avoids spectrum shortage. Cooperative sensing is one of the fastest growing areas of research and it is likely to be a key enabling technology, for efficiently spectrum sensing in future. For this several spectrum sensing are available, which can detect the white spaces or spectrum holes and share them to the secondary user without interfering with the movement of licensed user. In order to reliably and swiftly detect spectrum holes in cognitive radios, optimization must be used. In this paper we study different optimization for spectrum searching and sharing and also compare this optimization on the basis of probability of total error on fading channel

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

Novel Resource Allocation Algorithm for TV White Space Networks Using Hybrid Firefly Algorithm

There is continued increased demand for dynamic spectrum access of TV White Spaces (TVWS) due to growing need for wireless broadband. Some of the use cases such as cellular (2G/3G/4G/5G) access to TVWS may have a high density of users that want to make use of TVWS. When there is a high of density secondary users (SUs) in a TVWS network, there is possibility of high interference among SUs that exceeds the desired threshold and also harmful interference to primary users (PUs). Optimization of resource allocation (power and spectrum allocation) is therefore necessary so as to protect the PUs against the harmful interference and to reduce the level of interference among SUs. In this paper, a novel and improved resource allocation algorithm based on hybrid firefly algorithm, genetic algorithm  and particle swarm optimization (FAGAPSO) has been designed and applied for joint power and spectrum allocation. Computer simulations have been done using Matlab to validate the performance of the proposed algorithm.   Simulation results show that compared to firefly algorithm (FA), particle swarm optimization (PSO) and genetic algorithm (GA), the algorithm improves the PU SINR, SU sum throughput and SU signal to interference noise (SINR) ratio in a TVWS network. Only one algorithm considered (SAP) has better PU SINR, SU sum throughput and SU signal to interference noise (SINR) ratio in a TVWS network but it has poor running time

Analysis of Spectrum Occupancy Using Machine Learning Algorithms

In this paper, we analyze the spectrum occupancy using different machine learning techniques. Both supervised techniques (naive Bayesian classifier (NBC), decision trees (DT), support vector machine (SVM), linear regression (LR)) and unsupervised algorithm (hidden markov model (HMM)) are studied to find the best technique with the highest classification accuracy (CA). A detailed comparison of the supervised and unsupervised algorithms in terms of the computational time and classification accuracy is performed. The classified occupancy status is further utilized to evaluate the probability of secondary user outage for the future time slots, which can be used by system designers to define spectrum allocation and spectrum sharing policies. Numerical results show that SVM is the best algorithm among all the supervised and unsupervised classifiers. Based on this, we proposed a new SVM algorithm by combining it with fire fly algorithm (FFA), which is shown to outperform all other algorithms.Comment: 21 pages, 6 figure

Improved Resource Allocation Model for Reducing Interference Among Secondary Users in TV White Space for Broadband Services

This research article was published by IEEE Access 2022In recent years, the Television White Space has attracted the interest of many researchers due to its propagation characteristics obtainable between 470MHz and 790MHz spectrum bands. However, aggre- gate interference increase when secondary users in wireless network increase. Aggregate interference on the side of Primary Users has been extensively scrutinized. Therefore, resource allocation (power and spectrum) is crucial when designing the Television White Space network to avoid interferences from Secondary Users to Primary Users and among Secondary Users themselves. This study proposes a resource allocation model that uses joint power and spectrum hybrid Particle Swarm Optimization, Firefly, and Genetic algorithm for reducing the aggregate interference among Secondary Users. The algorithm is integrated with the admission control algorithm so that; there is a possibility of removing some of the Secondary Users in the network whenever the Signal to Noise Ratio threshold for Secondary and Primary Users is not met. We considered an infeasible system whereby all Secondary and Primary Users may not be supported simultaneously. Metrics such as Primary User Signal-to-noise ratio, sum throughput, and secondary user signal-to-noise ratio less than the threshold used to compare the performance of the proposed algorithm and the results show that PSOFAGA with effective link gain ratio admission control has the best performance compared to particle swarm optimization, genetic algorithm, firefly algorithm, and PSOFAGA algorith

Securing radio resources allocation with deep reinforcement learning for IoE services in next-generation wireless networks

The next generation wireless network (NGWN) is undergoing an unprecedented revolution, in which trillions of machines, people, and objects are interconnected to realize the Internet of Everything (IoE). with the emergence of IoE services such as virtual reality, augmented reality, and industrial 5G, the scarcity of radio resources becomes more serious. Moreover, there are hidden dangers of untrusted terminals accessing the system and illegally manipulating interconnected devices. To tackle these challenges, this paper proposes a securing radio resources allocation scheme with Deep Reinforcement Learning for IoE services in NGWN. First, the solution uses a BP neural network based on multi-feature optimized Firefly Algorithm (FA) for spectrum prediction, thereby improving the prediction accuracy and avoiding interference between unauthorized and authorized users with efficient radio utilization. Then, a spectrum sensing method based on deep reinforcement learning is proposed to identify the untrusted users in system while fusing the sensing results, to enhance the security of the cooperative process and the detection accuracy of spectrum holes. Extensive simulation results show that the proposal is superior to the traditional solutions in terms of prediction accuracy, spectrum utilization and energy consumption, and is suitable for deployment in future wireless systems