Cognitive Radio Technology in 5G Wireless Communications

Empowered by the cognitive radio technology, and motivated by the sporadic channel utilization, both spatially and temporally, dynamic spectrum access networks (also referred to as cognitive radio networks and next generation wireless networks) have emerged as a solution to improve spectrum utilization and provide more flexibility to wireless communication. A cognitive radio network is composed of wireless users, referred to as secondary users, which are allowed to use a given licensed spectrum band as long as there are no primary, licensed, users occupying this band in their vicinity. This restricted spectrum access strategy leads to heterogeneity in channel availability among secondary users. Heterogeneity in channel availability forms a significant source of performance degradation for cognitive radio networks, and poses a great challenge on protocol design. In this dissertation, we propose spectrum allocation algorithms that take this heterogeneity property and its effect on the network performance into consideration. The spectrum allocation solutions proposed in this dissertation address three major objectives in cognitive radio mesh networks. The first objective is maximizing the network coverage, in terms of the total number of served clients, and at the same time simplifying the communication coordination function. To address this objective, we proposed a received based channel allocation strategy that alleviates the need for a common control channel, thus simplifying the coordination function, and at the same time maximizes the number of clients served with link reliability guarantees. We show the superiority of the proposed allocation strategy over other existing strategies. The second objective is improving the multicast throughput to compensate for the performance degradation caused by the heterogeneity in channel availability. We proposed a

In the recent years Cognitive Radio (CR) technology imposed itself like a very good solution to the increasing spectrum utilization. This technology proposes the development of a new radio type - a cognitive radio - endowed with intelligence that shall sense, share, and use the spectrum opportunities (SOP) of the preexisting wireless networks, that is the channels that are not used by the licensed users. A new routing algorithm for CR networks (CRN): IPSAG - IP Spectrum Aware Geographic based Routing Protocol is presented in the paper. As suggested by the name, the IP hop-by-hop, the geographic, and the spectrum aware routing concepts were used together in the protocol's design. The dynamism and flexibility, characteristic to both CR and IP networks, made us compare the CR concept with the IP concept (a wireless versus wired flexible environment comparison); thus we assumed the IP routing approach for CRN. The proposed strategy is based on hop-by-hop routing, local decisions being taken by nodes according to both local information (CR node's neighborhood) and global information (the entire topology of CRN), channels availability, and destination location.

Cognitive radio technologies enable users to opportunistically access unused licensed spectrum and are viewed as a promising way to deal with the current spectrum crisis. Over the last 15 years, cognitive radio technologies have been extensively studied from algorithmic design to practical implementation. One pressing and fundamental problem is how to integrate cognitive radios into current wireless networks to enhance network capacity and improve users’ experience. Unfortunately, existing solutions to cognitive radio networks (CRNs) suffer from many practical design issues. To foster further research activities in this direction, we attempt to provide a tutorial for CRN architecture design. Noticing that an effective architecture for CRNs is still lacking, in this tutorial, we systematically summarize the principles for CRN architecture design and present a novel flexible network architecture, termed cognitive capacity harvesting network (CCHN), to elaborate on how a CRN architecture can be designed. Unlike existing architectures, we introduce a new network entity, called secondary service provider, and deploy cognitive radio capability enabled routers, called cognitive radio routers, in order to effectively and efficiently manage resource harvesting and mobile traffic while enabling users without cognitive radios to access and enjoy CCHN services. Our analysis shows that our CCHN aligns well to industrial standardization activities and hence provides a viable approach to implementing future CRNs. We hope that our proposed design approach opens a new venue to future CRN research.

Recent advancements in wireless communication technologies have lead to challenges in effective utilization of Radio frequency (RF) spectrum. These challenges can be overcome by using cognitive radio (CR) technology, which implements spectrum access in opportunistic manner. In this article we study the classification of various spectrum sensing techniques used in field of CR technology with a comparative study of their performances under different conditions.

Currently, the wireless communication technology has noticed explosive expansion in its demand and it is increasing rapidly whereasdue to their wide range of applications and uses, Wireless Sensor Networks (WSNs) have attracted the attentions of the academic community in various offline and online applications. These networks are deployed in the unattended and harsh environment due to which maintaining the network lifetime becomes a challenging task. The energy consumption in these networks is a challenging task which needs to be addressed for increased network lifetime. Moreover, the conventional wireless communication standards are utilizing the radio spectrum. Currently, the spectrum is very limited and excessive exploitation creating overburdening on the network. Thus efficient spectrum allocation is also becoming a tedious task. Nowadays, Cognitive Radio (CR) has appeared to be a viable way out to efficiently utilize the available spectrum. According to this CR concept, cognitive radio technology helps to opportunistically and dynamically access the spectrum bands which improve the utilization of radio spectrum resources.Several techniques have been presented to deal with the issue but these techniques suffer from various issues such as uncertain energy harvesting and resource (channel) allocation which becomes a critical issue for these networks. To overcome these problems, the workproposes a novel approachcalled ?Fuzzy?_Q-EACWSNR (an energy aware cognitive wireless sensor network routing) scheme which minimizes the energy consumption by using reinforcement based Q-learning scheme for packet forwarding in cognitive enabled wireless sensor nodes.This novel scheme considers the optimal cluster formulation, cluster head selection, spectrum sensing and allocation.The experimental study reported thenoteworthy improvement in the communication performance by evaluating and comparing the performance with EACRP, ESAC andOptimum distance based clustering in terms of network throughput, average packet delay, and energy consumption.

We focus on a specific class of denial-of-service (DoS) attacks that is executed through Channel Eviction Triggering (CET), whereby adversary nodes unduly invoke mechanisms inherent in a cognitive radio (CR) network (CRN) operation to protect the licensed users and thus disrupt secondary access to the otherwise idle licensed bands. Skewing the spectrum sensing decision of CRN through sensing misreports is a manifestation of CET attacks. Whereas most studies in the literature focus on making the cooperative sensing more robust against such sensing misreports, we tackle the problem from the novel perspective of incentive alleviation. We distinguish two classes of such DoS attacks, which we refer to as CET and CET-jamming attacks. In the former case, the incentive of adversary CRs is to remove the competition of truthful CRs in accessing the licensed spectral ranges. The latter class of DoS attack deals with scenarios in which the adversary nodes are mainly interested in denying the chances of communication of CRN over primary bands and as such their incentive cannot be modelled by the same utility maximisation model as truthful CRs. We propose a solution for each class of attacks, and our numerical results verify the effectiveness of the proposed CET defence scheme in both cases. Copyright © 2012 John Wiley & Sons, Ltd.

Cognitive radio (CR) technology is designed to improve reliability in communication between users through efficient and dynamic spectrum exploitation. CRs address the problems in spectrum allocation and channel access and improve the rate of radio resource utilization. The flexibility of the CR networks (CRN) and communication medium exposes it to a variety of threats; primary user emulation attack (PUEA) is a malicious and denial-of-service kind of adversary that defaces CRN performance. This manuscript proposes an adaptive learning-based attack detection in CRN for detecting and mitigating PUEA by analyzing the received power of the transmitter. The learning process endorses some beneficial features by distinguishing low spectrum legitimate PU from an adversary. The learning process adopts cyclostationary feature analysis for distinguishing adversaries and low power PU in CR communications. The process of learning is further enhanced by estimating distance variance and communication time-based analysis for improving the rate of signal classification and SU communication rate. The experimental analysis proves the stability of the proposed detection method by improving the SU throughput, with lesser signal classification time and misdetection probability.

In recent years, channel-hopping-based medium access control protocols have been proposed to improve the capacity in a decentralized multichannel cognitive radio (CR) network without using extra control channels. Each CR user has to stochastically follow a default channel-hopping sequence in order to locate a channel and conduct its frame transmission. In this paper, theoretical analysis is conducted on the probability of channel availability and the average frame delay for primary users (PUs) by considering the impact caused by imperfect sensing of CR users and imperfect synchronization between the primary and CR networks. According to the proposed analytical model with realistic considerations, an optimal channel-hopping sequence (OCS) approach is designed for the CR users based on a dynamic programming technique. It is designed by exploiting the optimal load balance between channel availability and channel utilization within the delay constraints of PUs. By adopting the OCS approach, maximum aggregate throughput of CR users can be achieved while considering PU's quality-of-service (QoS) requirements. Moreover, in addition to the paired CR networks, the logical partition problem that occurs in generalized CR networks will also be addressed. This problem can severely degrade the aggregate throughput due to the decreased probability of connectivity between CR users, especially in a CR network with heavy traffic. Therefore, both wake-up successive contention (WSC) and wake-up counter-reset successive contention (WCSC) algorithms are proposed to increase the number of negotiations by both exploring the blind spot of imperfect sensing and amending the contention mechanisms between CR users. Compared to conventional channel-hopping sequences, numerical results illustrate that the proposed approaches can effectively maximize aggregate throughput for CR users under the QoS requirements of PUs.

EGHR: Efficient group-based handover authentication protocols for mMTC in 5G wireless networks

During the last decade, Cognitive Radio (CR) has become a popular research topic. The availability of radio spectrum is in shortage, and CR technology can solve the problem by enabling dynamic spectrum access. This innovative technology has been used to manage the radio spectrum since it was introduced. These developments have led to rapid advances in this research field. A review of recent advances in Spectrum Sensing (SS) is presented in this paper, from its origins to its present state. A CR network has been found to be a highly effective and intelligent technology. Frequency spectrum is a bounded natural resource and an essential component of wireless communication networks. We explored CR in this paper and its various phases. Different definitions of CR are then presented from various institutions. Many different types of communications systems use CR techniques. It is anticipated that they will improve commercial and military data services, as well as increase the use of underutilized radio frequencies. In this paper, CR standards have been discussed, as well as its applications in various areas.

ABSTRACTThe rapid expansion of the Internet of Things (IoT) and the increasing demand for wireless communication have intensified the need for efficient spectrum management in cognitive radio networks (CRNs). Traditional approaches to spectrum sensing and allocation often operate in isolation or rely on static methods, which fail to address the dynamic and evolving nature of modern wireless environments. As IoT devices proliferate and spectrum resources become increasingly congested, there is a pressing need for more adaptive and efficient spectrum management solutions. Our approach addresses this need by offering an adaptive framework that responds to the changing spectrum landscape, thereby optimizing spectrum usage and reducing interference. This research suggests improved NN joint spectrum sensing for CR‐IoTNet (INJSS‐CR). This approach leverages cognitive radio (CR) technology to enhance spectrum utilization and mitigate the impact of spectrum shortages. CR technology enables secondary users (SUs) to detect and access unused spectrum through spectrum sensing. Within the CR‐IoTNet framework, joint spectrum sensing and allocation are performed to serve SU‐IoT devices via an interference‐free channel (IFC). The system comprises multiple primary user base stations (PU‐BSs) and SU devices functioning as IoT smart objects. Additionally, we integrate an improved neural network (INN) to adapt to dynamic network conditions and monitor primary user (PU) spectrum utilization using a comprehensive multiclass (J × 8) − D feature set. This combination of advanced techniques and CR technology aims to optimize spectrum management and support the growing IoT ecosystem. In particular, the INJSS‐CR obtained the greatest accuracy of 0.9492 at a training rate of 80%.

The advent of the wireless communications systems augurs new cutting-edge technologies, including self-driving vehicles, unmanned aerial systems, autonomous robots, the Internet-of-Things, and virtual reality. These technologies require high data rates, ultra-low latency, and high reliability, all of which are promised by the fifth generation of wireless communication systems (5G). Many research groups state that 5G cannot meet its demands without artificial intelligence (A.I.) integration as 5G wireless networks are expected to generate unprecedented traffic giving wireless research designers access to big data that can help in predicting the demands and adjust cell designs to meet the users' requirements. Subsequently, many researchers applied A.I. in many aspects of 5G wireless communication design including radio resource allocation, network management, cyber-security. In this paper, we provide an in-depth review of A.I. for 5G wireless communication systems. in this respect, the aim of this paper is to survey A.I. in 5G wireless communication and networking by discussing many case studies, discuss the challenges, and shed new light on future research directions for leveraging A.I. in 5G wireless communications.

Cognitive radio technology has become a promising approach to increase the efficiency of spectrum utilization. Since cognitive radio users are vulnerable to malicious attacks, security countermeasures are crucial to the successful deployment of cognitive radio networks in the future. In this paper, we focus on defending against the jamming attack, one of the major threats to cognitive radio networks, where several malicious attackers intend to jam the secondary user's communication link by injecting interference. We model this scenario into a jamming game, and derive the optimal strategy through the Markov decision process approach. Furthermore, a learning scheme is proposed for the secondary user to observe the wireless environment and estimate parameters such as primary users' access pattern and the number of attackers. Finally, simulation results are presented to verify the performance.

With growing concern about environmental issues and an emerging green communications paradigm, cognitive radio (CR) networks (CRNs) have to be considered from the energy efficiency perspective. In this paper, we focus on scheduling in CRNs, in which a cognitive base station (CBS) makes frequency allocations to the CRs at the beginning of each frame. A cognitive scheduler must consider the diversity among CRs' queues and channel capacities in terms of number of bits and the channel switching cost from one frequency to another. Taking all these into account, we formulate the scheduling problem as an energy efficiency maximization problem, which is a nonlinear integer programming (NLP) problem and is thereby hard to solve. We seek alternate computationally easier solutions. To this aim, we propose a polynomial-time heuristic algorithm, i.e., the energy-efficient heuristic scheduler (EEHS), which allocates each idle frequency to the CR that attains the highest energy efficiency at this frequency. Next, we reformulate the original problem first as a throughput maximization problem subject to energy consumption restrictions and then as an energy consumption minimization problem subject to minimum throughput guarantees. These two schedulers also have the power to provide fairness in resource allocation. We analyze the energy efficiency and successful transmission probability of the proposed schedulers under both contiguous and fragmented spectrum scenarios. Performance studies show that, compared with a pure opportunistic scheduler with a throughput maximization objective, proposed schedulers can attain almost the same throughput performance with better energy efficiency.

As a cognitive radio (CR) modulation technique, transform domain communication system (TDCS) has been proposed by utilizing white space spectrum for the network access and achieving low probability of interception (LPI). In the previously reported TDCS- based cognitive radio networks (CRN), however, the non-zero periodic correlation function between any pair of users results in multiuser interference (MUI). In this paper, a novel framework under the CR constraints is presented by employing a two- dimension spreading scheme (i.e., time and frequency domains). We first obtain a class of spreading sequences with almost perfect periodic correlation function for arbitrary spectrum utilization pattern. Then, an universal criteria is presented for synchronous MUI-free CRNs when employing TDCS. Simulation results demonstrate that the proposed TDCS-based CRN architecture is a preferable candidate for distribution wireless networks, such as CR Ad-hoc wireless sensor networks.