State of the Art, Taxonomy, and Open Issues on Cognitive Radio Networks with NOMA

The explosive growth of mobile devices and the rapid increase of wideband wireless services call for advanced communication techniques that can achieve high spectral efficiency and meet the massive connectivity requirement. Cognitive radio (CR) and non-orthogonal multiple access (NOMA) are envisioned to be important solutions for the fifth generation wireless networks. Integrating NOMA techniques into CR networks (CRNs) has the tremendous potential to improve spectral efficiency and increase the system capacity. However, there are many technical challenges due to the severe interference caused by using NOMA. Many efforts have been made to facilitate the application of NOMA into CRNs and to investigate the performance of CRNs with NOMA. This article aims to survey the latest research results along this direction. A taxonomy is devised to categorize the literature based on operation paradigms, enabling techniques, design objectives and optimization characteristics. Moreover, the key challenges are outlined to provide guidelines for the domain researchers and designers to realize CRNs with NOMA. Finally, the open issues are discussed.Comment: This paper has been accepted by IEEE Wireless Communications Magazine. Pages 16, Figures

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

Applications of Repeated Games in Wireless Networks: A Survey

A repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model an interaction among players in only one period, in repeated games, interactions of players repeat for multiple periods; and thus the players become aware of other players' past behaviors and their future benefits, and will adapt their behavior accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey the applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games to encourage wireless nodes to cooperate, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference

Energy-efficient spectrum sensing approaches for cognitive radio systems

Designing an energy efficient cooperative spectrum sensing for cognitive radio network is our main research objective in this dissertation. Two different approaches are employed to achieve the goal, clustering and minimizing the number of participating cognitive radio users in the cooperative process. First, using clustering technique, a multilevel hierarchical cluster-based structure spectrum sensing algorithm has been proposed to tackle the balance between cooperation gain and cost by combining two different fusion rules and exploiting the tree structure of the cluster. The algorithm considerably minimizes the reporting overhead while satisfying the detection requirements. Second, based on reducing the number of participating cognitive radio users, primary user protection is considered to develop an energy efficient algorithm for cluster-based cooperative spectrum sensing system. An iterative algorithm with low complexity has been proposed to design energy efficient spectrum sensing for cluster-based cooperative systems. Simulation results show that the proposed algorithm can significantly minimize the number of contributing of cognitive radio users in the collaboration process and can compromise the performance gain and the incurred overhead. Moreover, a variable sensing window size is also considered to propose three novel strategies for energy efficient centralized cooperative spectrum sensing system using the three hard decision fusion rules. The results show that strategies remarkably increase the energy efficiency of the cooperative system; furthermore, it is shown optimality of k out of N rule over other two hard decision fusion rules. Finally, joint optimization of transmission power and sensing time for a single cognitive radio is considered. An iterative algorithm with low computational requirements has been proposed to jointly optimize power and sensing time to maximize the energy efficiency metric. Computer results have shown that the proposed algorithm outperforms those existing works in the literature

Low Complexity Energy-Efficient Collaborative Spectrum Sensing for Cognitive Radio Networks

Clustering approach is considered a management technology that arranged the distributed cognitive radio users into logical groups to improve the sensing performance of the network. A lot of works in this area showed that cluster-based spectrum sensing (CBSS) technique efficiently tackled the trade-off between performance and overhead issue. By employing the tree structure of the cluster, a multilevel hierarchical cluster-based spectrum sensing (MH-CBSS) algorithm was proposed to compromise between the gained performance and incurred overhead. However, the MH-CBSS iterative algorithm incurs high computational requirements. In this thesis, an energy-efficient low computational hierarchical cluster-based algorithm is proposed which reduces the incurred computational burden. This is achieved by predetermining the number of cognitive radios (CRs) in the cluster, which provides an advantage of reducing the number of iterations of the MH-CBSS algorithm. Furthermore, for a comprehensive study, the modified algorithm is investigated over both Rayleigh and Nakagami fading channels. Simulation results show that the detection performance of the modified algorithm outperforms the MH-CBSS algorithm over Rayleigh and Nakagami fading channels. In addition, a conventional energy detection algorithm is a fixed threshold based algorithm. Therefore, the threshold should be selected properly since it significantly affects the sensing performance of energy detector. For this reason, an energy-efficient hierarchical cluster-based cooperative spectrum sensing algorithm with an adaptive threshold is proposed which enables the CR dynamically adapts its threshold to achieve the minimum total cluster error. Besides, the optimal threshold level for minimizing the overall cluster detection error rate is numerically determined. The detection performance of the proposed algorithm is presented and evaluated through simulation results

Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). In the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we analyze the spear and shield relation between Byzantine attack and defense from three aspects: the vulnerability of CSS to attack, the obstacles in CSS to defense, and the games between attack and defense. Then, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Next, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we highlight the unsolved research challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral

Cooperative-hybrid detection of primary user emulators in cognitive radio networks

Primary user emulator (PUE) attack occurs in Cognitive Radio Networks (CRNs) when a malicious secondary user (SU) poses as a primary user (PU) in order to deprive other legitimate SUs the right to free spectral access for opportunistic communication. In most cases, these legitimate SUs are unable to effectively detect PUEs because the quality of the signals received from a PUE may be severely attenuated by channel fading and/or shadowing. Consequently, in this paper, we have investigated the use of cooperative spectrum sensing (CSS) to improve PUE detection based on a hybrid localization scheme. We considered different pairs of secondary users (SUs) over different received signal strength (RSS) values to evaluate the energy efficiency, accuracy, and speed of the new cooperative scheme. Based on computer simulations, our findings suggest that a PUE can be effectively detected by a pair of SUs with a low Root Mean Square Error rate of 0.0047 even though these SUs may have close RSS values within the same cluster. Furthermore, our scheme performs better in terms of speed, accuracy and low energy consumption rates when compared with other PUE detection schemes. Thus, it is a viable proposition to better detect PUEs in CRNs

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

Optimization and Learning in Energy Efficient Cognitive Radio System

Energy efficiency and spectrum efficiency are two biggest concerns for wireless communication. The constrained power supply is always a bottleneck to the modern mobility communication system. Meanwhile, spectrum resource is extremely limited but seriously underutilized. Cognitive radio (CR) as a promising approach could alleviate the spectrum underutilization and increase the quality of service. In contrast to traditional wireless communication systems, a distinguishing feature of cognitive radio systems is that the cognitive radios, which are typically equipped with powerful computation machinery, are capable of sensing the spectrum environment and making intelligent decisions. Moreover, the cognitive radio systems differ from traditional wireless systems that they can adapt their operating parameters, i.e. transmission power, channel, modulation according to the surrounding radio environment to explore the opportunity. In this dissertation, the study is focused on the optimization and learning of energy efficiency in the cognitive radio system, which can be considered to better utilize both the energy and spectrum resources. Firstly, drowsy transmission, which produces optimized idle period patterns and selects the best sleep mode for each idle period between two packet transmissions through joint power management and transmission power control/rate selection, is introduced to cognitive radio transmitter. Both the optimal solution by dynamic programming and flexible solution by reinforcement learning are provided. Secondly, when cognitive radio system is benefited from the theoretically infinite but unsteady harvested energy, an innovative and flexible control framework mainly based on model predictive control is designed. The solution to combat the problems, such as the inaccurate model and myopic control policy introduced by MPC, is given. Last, after study the optimization problem for point-to-point communication, multi-objective reinforcement learning is applied to the cognitive radio network, an adaptable routing algorithm is proposed and implemented. Epidemic propagation is studied to further understand the learning process in the cognitive radio network