Optimization of Spectrum Allocation in Cognitive Radio and Dynamic Spectrum Access Networks

Spectrum has become a treasured commodity. However, many licensed frequency bands exclusively assigned to the primary license holders (also called primary users) remain relatively unused or under-utilized for most of the time. Allowing other users (also called secondary users) without a license to operate in these bands with no interference becomes a promising way to satisfy the fast growing needs for frequency spectrum resources. A cognitive radio adapts to the environment it operates in by sensing the spectrum and quickly decides on appropriate frequency bands and transmission parameters to use in order to achieve certain performance goals. One of the most important issues in cognitive radio networks (CRNs) is intelligent channel allocation which will improve the performance of the network and spectrum utilization. The objective of this dissertation is to address the channel allocation optimization problem in cognitive radio and DSA networks under both centralized architecture and distributed architecture. By centralized architecture we mean the cognitive radio and DSA networks are infrastructure based. That is, there is a centralized device which collects all information from other cognitive radios and produces a channel allocation scheme. Then each secondary user follows the spectrum allocation and accesses the corresponding piece of spectrum. By distributed architecture we mean that each secondary user inside the cognitive radio and DSA networks makes its own decision based on local information on the spectrum usage. Each secondary user only considers the spectrum usage around itself. We studied three common objectives of the channel allocation optimization problem, including maximum network throughput (MNT), max-min fairness (MMF), and proportional fairness (PF). Given different optimization objectives, we developed mathematical models in terms of linear programing and non-linear programing formulations, under the centralized architecture. We also designed a unified framework with different heuristic algorithms for different optimization objectives and the best results from different algorithms can be automatically chosen without manual intervention. We also conducted additional work on spectrum allocation under distributed architecture. First, we studied the channel availability prediction problem. Since there is a lot of usable statistic information on spectrum usage from national and regional agencies, we presented a Bayesian inference based prediction method, which utilizes prior information to make better prediction on channel availability. Finally a distributed channel allocation algorithm is designed based on the channel prediction results. We illustrated that the interaction behavior between different secondary users can be modeled as a game, in which the secondary users are denoted as players and the channels are denoted as resources. We proved that our distributed spectrum allocation algorithm can achieve to Nash Equilibrium, and is Pareto optimal

Un nuevo esquema de agrupación para redes sensoras inalámbricas de radio cognitivas heterogéneas

Introduction: This article is the product of the research “Learning-based Spectrum Analysis and Prediction in Cognitive Radio Sensor Networks”, developed at Sejong University in the year 2019. Problem: Most of the clustering schemes for distributed cognitive radio-enabled wireless sensor networks consider homogeneous cognitive radio-enabled wireless sensors. Many clustering schemes for such homogeneouscognitive radio-enabled wireless sensor networks waste resources and suffer from energy inefficiency because of the unnecessary overheads. Objective: The objective of the research is to propose a node clustering scheme that conserves energy and prolongs network lifetime. Methodology: A heterogeneous cognitive radio-enabled wireless sensor network in which only a few nodes have a cognitive radio module and the other nodes are normal sensor nodes. Along with the hardware cost, theproposed scheme is efficient in energy consumption. Results: We simulated the proposed scheme and compared it with the homogeneous cognitive radio-enabled wireless sensor networks. The results show that the proposed scheme is efficient in terms of energyconsumption. Conclusion: The proposed node clustering scheme performs better in terms of network energy conservation and network partition. Originality: There are heterogeneous node clustering schemes in the literature for cooperative spectrum sensing and energy efficiency, but to the best of our knowledge, there is no study that proposes a non-cognitiveradio-enabled sensor clustering for energy conservation along with cognitive radio-enabled wireless sensors. Limitations: The deployment of the proposed special device for cognitive radio-enabled wireless sensors is complicated and requires special hardware with better battery powered cognitive sensor nodes

Recurrent Neural Networks and Matrix Methods for Cognitive Radio Spectrum Prediction and Security

In this work, machine learning tools, including recurrent neural networks (RNNs), matrix completion, and non-negative matrix factorization (NMF), are used for cognitive radio problems. Specifically addressed are a missing data problem and a blind signal separation problem. A specialized RNN called Cellular Simultaneous Recurrent Network (CSRN), typically used in image processing applications, has been modified. The CRSN performs well for spatial spectrum prediction of radio signals with missing data. An algorithm called soft-impute for matrix completion used together with an RNN performs well for missing data problems in the radio spectrum time-frequency domain. Estimating missing spectrum data can improve cognitive radio efficiency. An NMF method called tuning pruning is used for blind source separation of radio signals in simulation. An NMF optimization technique using a geometric constraint is proposed to limit the solution space of blind signal separation. Both NMF methods are promising in addressing a security problem known as spectrum sensing data falsification attack

Spectrum Map and its Application in Cognitive Radio Networks

Recent measurements on radio spectrum usage have revealed the abundance of underutilized bands of spectrum that belong to licensed users. This necessitated the paradigm shift from static to dynamic spectrum access. Cognitive radio based secondary networks that utilize such unused spectrum holes in the licensed band, have been proposed as a possible solution to the spectrum crisis. The idea is to detect times when a particular licensed band is unused and use it for transmission without causing interference to the licensed user. We argue that prior knowledge about occupancy of such bands and the corresponding achievable performance metrics can potentially help secondary networks to devise effective strategies to improve utilization. In this work, we use Shepard\u27s method of interpolation to create a spectrum map that provides a spatial distribution of spectrum usage over a region of interest. It is achieved by intelligently fusing the spectrum usage reports shared by the secondary nodes at various locations. The obtained spectrum map is a continuous and differentiable 2-dimension distribution function in space. With the spectrum usage distribution known, we show how different radio spectrum and network performance metrics like channel capacity, secondary network throughput, spectral efficiency, and bit error rate can be estimated. We show the applicability of the spectrum map in solving the intra-cell channel allocation problem in centralized cognitive radio networks, such as IEEE 802.22. We propose a channel allocation scheme where the base station allocates interference free channels to the consumer premise equipments (CPE) using the spectrum map that it creates by fusing the spectrum usage information shared by some CPEs. The most suitable CPEs for information sharing are chosen on a dynamic basis using an iterative clustering algorithm. Next, we present a contention based media access control (MAC) protocol for distributed cognitive radio network. The unlicensed secondary users contend among themselves over a common control channel. Winners of the contention get to access the available channels ensuring high utilization and minimum collision with primary incumbent. Last, we propose a multi-channel, multi-hop routing protocol with secondary transmission power control. The spectrum map, created and maintained by a set of sensors, acts as the basis of finding the best route for every source destination pair. The proposed routing protocol ensures primary receiver protection and maximizes achievable link capacity. Through simulation experiments we show the correctness of the prediction model and how it can be used by secondary networks for strategic positioning of secondary transmitter-receiver pairs and selecting the best candidate channels. The simulation model mimics realistic distribution of TV stations for urban and non-urban areas. Results validate the nature and accuracy of estimation, prediction of performance metrics, and efficiency of the allocation process in an IEEE 802.22 network. Results for the proposed MAC protocol show high channel utilization with primary quality of service degradation within a tolerable limit. Performance evaluation of the proposed routing scheme reveals that it ensures primary receiver protection through secondary power control and maximizes route capacity

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

Fuzzy based clustering in CWPSN using machine learning model

90-94Cognitive wireless power sensor network (CWPSN) technology, widely used in almost all fields, has addressed various issues. The researchers have addressed the problems in the lack of radio spectrum availability and enabled the allocation of dynamic spectrum access in specific fields. The main challenge has been to support the radio spectrum allocation using intelligent adaptive learning and decision-making techniques so that various requirements of 5G wireless networks can be encountered. Machine learning (ML) is one of the most promising artificial intelligence tools conceived to support cognitive wireless networks. This paper aims to provide energy optimization and enhance security to cognitive wireless power sensor networks using a novel protocol during resource allocation. In addition to the existing methods, a novel protocol, fuzzy cluster-based greedy algorithms for attack prediction and energy harvesting using a machine-language model based on neural network techniques have been introduced. The simulation has been done using MATLAB software tools which gives efficient results

SVM-Based Spectrum Mobility Prediction Scheme in Mobile Cognitive Radio Networks

Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. The mobility of cognitive users (CUs) and the working activities of primary users (PUs) are analyzed in theory. And a joint feature vector extraction (JFVE) method is proposed based on the theoretical analysis. Then spectrum mobility prediction is executed through the classification of SVM with a fast convergence speed. Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. Additionally, a rational parameter design can remedy the prediction performance degradation caused by high speed SUs with strong randomness movements