Comprehensive survey on quality of service provisioning approaches in cognitive radio networks : part one

Much interest in Cognitive Radio Networks (CRNs) has been raised recently by enabling unlicensed (secondary) users to utilize the unused portions of the licensed spectrum. CRN utilization of residual spectrum bands of Primary (licensed) Networks (PNs) must avoid harmful interference to the users of PNs and other overlapping CRNs. The coexisting of CRNs depends on four components: Spectrum Sensing, Spectrum Decision, Spectrum Sharing, and Spectrum Mobility. Various approaches have been proposed to improve Quality of Service (QoS) provisioning in CRNs within fluctuating spectrum availability. However, CRN implementation poses many technical challenges due to a sporadic usage of licensed spectrum bands, which will be increased after deploying CRNs. Unlike traditional surveys of CRNs, this paper addresses QoS provisioning approaches of CRN components and provides an up-to-date comprehensive survey of the recent improvement in these approaches. Major features of the open research challenges of each approach are investigated. Due to the extensive nature of the topic, this paper is the first part of the survey which investigates QoS approaches on spectrum sensing and decision components respectively. The remaining approaches of spectrum sharing and mobility components will be investigated in the next part

Green cooperative spectrum sensing and scheduling in heterogeneous cognitive radio networks

The motivation behind the cognitive radio networks (CRNs) is rooted in scarcity of the radio spectrum and inefficiency of its management to meet the ever increasing high quality of service demands. Furthermore, information and communication technologies have limited and/or expensive energy resources and contribute significantly to the global carbon footprint. To alleviate these issues, energy efficient and energy harvesting (EEH) CRNs can harvest the required energy from ambient renewable sources while collecting the necessary bandwidth by discovering free spectrum for a minimized energy cost. Therefore, EEH-CRNs have potential to achieve green communications by enabling spectrum and energy self-sustaining networks. In this thesis, green cooperative spectrum sensing (CSS) policies are considered for large scale heterogeneous CRNs which consist of multiple primary channels (PCs) and a large number of secondary users (SUs) with heterogeneous sensing and reporting channel qualities. Firstly, a multi-objective clustering optimization (MOCO) problem is formulated from macro and micro perspectives; Macro perspective partitions SUs into clusters with the objectives: 1) Intra-cluster energy minimization of each cluster, 2) Intra-cluster throughput maximization of each cluster, and 3) Inter-cluster energy and throughput fairness. A multi-objective genetic algorithm, Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is adopted and demonstrated how to solve the MOCO. The micro perspective, on the other hand, works as a sub-procedure on cluster formations given by macro perspective. For the micro perspective, a multihop reporting based CH selection procedure is proposed to find: 1) The best CH which gives the minimum total multi-hop error rate, and 2) the optimal routing paths from SUs to the CHs using Dijkstra\u27s algorithm. Using Poisson-Binomial distribution, a novel and generalized K-out-of-N voting rule is developed for heterogeneous CRNs to allow SUs to have different levels of local detection performance. Then, a convex optimization framework is established to minimize the intra-cluster energy cost subject to collision and spectrum utilization constraints.Likewise, instead of a common fixed sample size test, a weighted sample size test is considered for quantized soft decision fusion to obtain a more EE regime under heterogeneity. Secondly, an energy and spectrum efficient CSS scheduling (CSSS) problem is investigated to minimize the energy cost per achieved data rate subject to collision and spectrum utilization constraints. The total energy cost is calculated as the sum of energy expenditures resulting from sensing, reporting and channel switching operations. Then, a mixed integer non-linear programming problem is formulated to determine: 1) The optimal scheduling subset of a large number of PCs which cannot be sensed at the same time, 2) The SU assignment set for each scheduled PC, and 3) Optimal sensing parameters of SUs on each PC. Thereafter, an equivalent convex framework is developed for specific instances of above combinatorial problem. For the comparison, optimal detection and sensing thresholds are also derived analytically under the homogeneity assumption. Based on these, a prioritized ordering heuristic is developed to order channels under the spectrum, energy and spectrum-energy limited regimes. After that, a scheduling and assignment heuristic is proposed and shown to have a very close performance to the exhaustive optimal solution. Finally, the behavior of the CRN is numerically analyzed under these regimes with respect to different numbers of SUs, PCs and sensing qualities. Lastly, a single channel energy harvesting CSS scheme is considered with SUs experiencing different energy arrival rates, sensing, and reporting qualities. In order to alleviate the half- duplex EH constraint, which precludes from charging and discharging at the same time, and to harvest energy from both renewable sources and ambient radio signals, a full-duplex hybrid energy harvesting (EH) model is developed. After formulating the energy state evolution of half and full duplex systems under stochastic energy arrivals, a convex optimization framework is established to jointly obtain the optimal harvesting ratio, sensing duration and detection threshold of each SU to find an optimal myopic EH policy subject to collision and energy- causality constraints

Spectrum Awareness in Cognitive Radio Systems

The paper addresses the issue of the Electromagnetic Environment Situational Awareness techniques. The main focus is put on sensing and the Radio Environment Map. These two dynamic techniques are described in detail. The Radio Environment Map is considered the essential part of the spectrum management system. It is described how the density and deployment of sensors affect the quality of maps and it is analysed which methods are the most suitable for map construction. Additionally, the paper characterizes several sensing methods

Spectrum Awareness in Cognitive Radio Systems

The paper addresses the issue of the Electromagnetic Environment Situational Awareness techniques. The main focus is put on sensing and the Radio Environment Map. These two dynamic techniques are described in detail. The Radio Environment Map is considered the essential part of the spectrum management system. It is described how the density and deployment of sensors affect the quality of maps and it is analysed which methods are the most suitable for map construction. Additionally, the paper characterizes several sensing methods

Synoptic analysis techniques for intrusion detection in wireless networks

Current system administrators are missing intrusion alerts hidden by large numbers of false positives. Rather than accumulation more data to identify true alerts, we propose an intrusion detection tool that e?ectively uses select data to provide a picture of ?network health?. Our hypothesis is that by utilizing the data available at both the node and cooperative network levels we can create a synoptic picture of the network providing indications of many intrusions or other network issues. Our major contribution is to provide a revolutionary way to analyze node and network data for patterns, dependence, and e?ects that indicate network issues. We collect node and network data, combine and manipulate it, and tease out information about the state of the network. We present a method based on utilizing the number of packets sent, number of packets received, node reliability, route reliability, and entropy to develop a synoptic picture of the network health in the presence of a sinkhole and a HELLO Flood attacker. This method conserves network throughput and node energy by requiring no additional control messages to be sent between the nodes unless an attacker is suspected. We intend to show that, although the concept of an intrusion detection system is not revolutionary, the method in which we analyze the data for clues about network intrusion and performance is highly innovative

Spectrum prediction in dynamic spectrum access systems

Despite the remarkable foreseen advancements in maximizing network capacities, the in-expansible nature of radio spectrum exposed outdated spectrum management techniques as a core limitation. Fixed spectrum allocation inefficiency has generated a proliferation of dynamic spectrum access solutions to accommodate the growing demand for wireless, and mobile applications. This research primarily focuses on spectrum occupancy prediction which equip dynamic users with the cognitive ability to identify and exploit instantaneous availability of spectrum opportunities. The first part of this research is devoted to identifying candidate occupancy prediction techniques suitable for SOP scenarios are extensively analysed, and a theoretical based model selection framework is consolidated. The performance of single user Bayesian/Markov based techniques both analytically and numerically. Understanding performance bounds of Bayesian/Markov prediction allows the development of efficient occupancy prediction models. The third and fourth parts of this research investigates cooperative decision and data-based occupancy prediction. The expected cooperative prediction accuracy gain is addressed based on the single user prediction model. Specifically, the third contributions provide analytical approximations of single user, as well as cooperative hard fusion based spectrum prediction. Finally, the forth contribution shows soft fusion is superior and more robust compared to hard fusion cooperative prediction in terms of prediction accuracy. Throughout this research, case study analysis is provided to evaluate the performance of the proposed approaches. Analytical approaches and Monte-Carlo simulation are compared for the performance metric of interest. Remarkably, the case study analysis confirmed that the statistical approximation can predict the performance of local and hard fusion cooperative prediction accurately, capturing all the essential aspects of signal detection performance, temporal dependency of spectrum occupancy as well as the finite nature of the network

Performance Evaluation of Cognitive Radio Spectrum Sensing Techniques through a Rayleigh Fading Channel

In recent years, there has been a steep rise in the demand for bandwidth due to a sharp increase in the number of devices connected to the wireless network. Coupled with the expected commercialization of 5G services and massive adoption of IoT, the upsurge in the number of devices connected to the wireless network will continue to grow exponentially into billions of devices. To accommodate the associated demand for wireless spectrum as we step into this new era of wireless connectivity, traditional methods of spectrum utilization based on fixed and static allocation are no longer adequate. New innovative forms that support dynamic assignment of spectrum space on as-per-need basis are now paramount. Cognitive radio has emerged as one of the most promising techniques that allow flexible usage of the scarce spectrum resource. Cognitive radio allows unlicensed users to opportunistically access spectrum bands assigned to primary users when these spectrum bands are idle. As such, cognitive radio reduces the gap between spectrum scarcity and spectrum underutilization. The most critical function of cognitive radio is spectrum sensing, which establishes the occupation status of a spectrum band, paving the way for a cognitive radio to initiate transmission if the band is idle. The most common and widely used methods for spectrum sensing are energy detection, matched filter detection, cyclostationary feature detection and cooperative based spectrum sensing. This dissertation investigates the performance of these spectrum-sensing techniques through a Rayleigh fading channel. In a wireless environment, a Rayleigh fading channel models the propagation of a wireless signal where there is no dominant line of sight between the transmitter and receiver. Understanding the performance of spectrum sensing techniques in a real world simulation environment is important for both industry and academia, as this allows for the optimal design of cognitive radio systems capable of efficiently executing their function. MATLAB software provides an experimental platform for the fusion of various Rayleigh fading channel parameters that mimic real world wireless channel characteristics. In this project, a MATLAB environment test bed is used to simulate the performance for each spectrum sensing technique across a range of signal-to-noise values, through a Rayleigh fading channel with a given set of parameters for channel delay, channel gain and Doppler shift. Simulation results are presented as plots for probability of detection versus signal-tonoise ratio, receiver operating characteristics (ROC) curves and complementary ROC curves. A detailed performance analysis for each spectrum sensing technique then follows, with comparisons done to determine the technique that offers the best relative performance

Spectrum sensing for cognitive radio and radar systems

The use of the radio frequency spectrum is increasing at a rapid rate. Reliable and efficient operation in a crowded radio spectrum requires innovative solutions and techniques. Future wireless communication and radar systems should be aware of their surrounding radio environment in order to have the ability to adapt their operation to the effective situation. Spectrum sensing techniques such as detection, waveform recognition, and specific emitter identification are key sources of information for characterizing the surrounding radio environment and extracting valuable information, and consequently adjusting transceiver parameters for facilitating flexible, efficient, and reliable operation. In this thesis, spectrum sensing algorithms for cognitive radios and radar intercept receivers are proposed. Single-user and collaborative cyclostationarity-based detection algorithms are proposed: Multicycle detectors and robust nonparametric spatial sign cyclic correlation based fixed sample size and sequential detectors are proposed. Asymptotic distributions of the test statistics under the null hypothesis are established. A censoring scheme in which only informative test statistics are transmitted to the fusion center is proposed for collaborative detection. The proposed detectors and methods have the following benefits: employing cyclostationarity enables distinction among different systems, collaboration mitigates the effects of shadowing and multipath fading, using multiple strong cyclic frequencies improves the performance, robust detection provides reliable performance in heavy-tailed non-Gaussian noise, sequential detection reduces the average detection time, and censoring improves energy efficiency. In addition, a radar waveform recognition system for classifying common pulse compression waveforms is developed. The proposed supervised classification system classifies an intercepted radar pulse to one of eight different classes based on the pulse compression waveform: linear frequency modulation, Costas frequency codes, binary codes, as well as Frank, P1, P2, P3, and P4 polyphase codes. A robust M-estimation based method for radar emitter identification is proposed as well. A common modulation profile from a group of intercepted pulses is estimated and used for identifying the radar emitter. The M-estimation based approach provides robustness against preprocessing errors and deviations from the assumed noise model