Heterogeneous Cognitive Radio Networks Research Articles

Burst traffic: Congestion management and performance optimization strategies in heterogeneous cognitive radio networks

AbstractIn order to solve the network congestion problem caused by burst service flows in heterogeneous cognitive radio networks (Het‐CRN), a two‐stage dual‐threshold random early detection (TD‐RED) control mechanism is proposed here. Meanwhile, a three‐state Markov modulated Poisson process (MMPP‐3) is used to model the burst service to analyze its arrival in depth. Moreover, a performance evaluation system (PES) is proposed to quantify the interference of burst services to different classes of services and their quality of service, which can comprehensively evaluate the performance enhancement of the TD‐RED control mechanism for each service. The system examines performance metrics such as packet loss rate, average delay, average captain etc. PES simulation results show that TD‐RED significantly reduces the packet rejection rate and transmission delay compared to the no congestion control (NCC) and random early detection (RED) methods, thus providing superior quality of service to all services. thereby providing a superior quality of service to cognitive users. Its lower average captain fully proves that TD‐RED can effectively alleviate congestion.

A composite channel hopping algorithm for blind rendezvous in heterogeneous cognitive radio networks

A composite channel hopping algorithm for blind rendezvous in heterogeneous cognitive radio networks

Analysis of Bayesian game theoretic approach for the target channel selection of secondary user under heterogeneous cognitive radio network

Analysis of Bayesian game theoretic approach for the target channel selection of secondary user under heterogeneous cognitive radio network

Multi‐objective spectrum assignment in heterogeneous cognitive radio networks for internet of things

SummaryInternet of Things (IoT) applications have massively increased in the last few years. Future‐generation IoTs are going to be massive in number, heterogeneous in nature, and extremely demanding in terms of computation and communication requirements. Therefore, effective techniques are needed to optimize the use of scarce communication resources. This work proposes a novel multi‐objective framework to investigate the channel assignment problem in cognitive radio network (CRN)‐based IoT applications. The multi‐objective optimization framework comprises three objective functions. The first objective aims to maximize the overall system throughput fairness, the second objective aims to maximize the overall system residual energy, and the third objective aims to increase the user satisfaction level by maximizing the overall priority index. The problem of channel assignment is modeled and solved using Mixed Integer Linear Programming (MILP) and ‐constraint technique. This technique generates a set of Pareto efficient solutions, and an optimal solution among these solutions is selected using a fuzzy logic decision mechanism. Moreover, power level selection is incorporated by solving the problem multiple times for different power levels and obtaining a preferred solution for each power level. The simulations are conducted to unveil an interesting trade‐off between the conflicting system design objectives, that is, the overall throughput and the overall residual energy while incorporating the significance of primary user (PU) activity when preforming channel assignment in CRN.

Optimal Time Allocation for Backscatter-Aided Relay Cooperative Transmission in Wireless-Powered Heterogeneous CRNs

Nowadays, backscatter, radio-frequency (RF) energy harvesting (EH), and cognitive radio technologies have been widely applied in Internet of Things (IoT) to address the issues of energy supply and spectrum scarcity. This paper focuses on the throughput maximization problem of backscatter-aided wireless-powered heterogeneous cognitive radio networks (WPHetCRNs), where two types of secondary transmitters (STs), i.e., the STs with backscatter units (STBs) and the STs with RF-EH units (STEs), coexist. The STBs operate in the ambient backscatter (AB) mode, and the STEs operate in the harvest-then-transmit (HTT) mode. Inspired by the potential benefits of cooperations between different users, we propose a backscatter-aided cooperative relay transmission (BaCRT) strategy to improve the sum-throughput of the secondary users (SUs). The main idea is that when the licensed spectrum of the primary users (PUs) is busy, the STBs first help to relay the primary data via the passive relay mode, and then transmit the secondary data via the AB mode, while the STEs harvest energy in the HTT mode. With the help of relaying, the target throughput of the PUs could be met in shorter duration and the licensed spectrum could become idle more quickly. When the licensed spectrum becomes idle, the STEs transmit data in the HTT mode. The goal of this paper is to identify the optimal time allocation among the passive relay mode, AB mode, and data transmission of HTT mode that maximizes the sum-throughput of the SUs. To reach this goal, we first investigate the single-ST case for each type and derive the closed-form solution of the optimal time allocation. We then extend to the multiple-ST case for each type, where three scenarios are classified with respect to the fairness issue of the STBs. We prove that the sum-throughput maximization problem is convex in each scenario, and employ the block coordinate descent and gradient descent iterative algorithms to solve the problem. Numerical results show that the proposed BaCRT strategy significantly improves the sum-throughput of the SUs compared with other strategies.

A Channel Allocation Framework Under Responsive Pricing in Heterogeneous Cognitive Radio Network

For a virtual network operator (VNO) of a heterogeneous cognitive radio network (CRN), finding a channel allocation method that can maximize income while guaranteeing the quality of service (QoS) of secondary users (SU) is a complex but crucial problem. Aim to this problem, based on the probability distribution vector (PDV) designed to describe all possible channel allocation results, a queueing analysis framework capable of obtaining the performance metrics of the QoS, such as throughput, queue length, packets rejection rate and the VNO’s income, is proposed. The proposed framework, which is able to analyze each non-homogeneous SU with configurable parameters independently, can be applied to the real-world scenarios of CRN flexibly. To calculate income of the VNO accurately, the nonlinear function between the throughput and the pricing for each SU is set as a configurable parameter in the proposed framework. To maximize income of the VNO, a maximum price for throughput allocation (MPTA) protocol is designed. Numerical results of experiments show that the MPTA protocol can increase VNO’s income while maintaining the Qos of SUs compared with the existing protocols. And it is proved that the proposed framework is capable of helping design channel allocation protocols to increase VNO’s income.

Ambient Backscatter Communication Based Cooperative Relaying for Heterogeneous Cognitive Radio Networks

Ambient Backscatter Communication Based Cooperative Relaying for Heterogeneous Cognitive Radio Networks

System throughput maximization in IRS-assisted phase cooperative NOMA networks

This work investigates performance of system throughput in intelligent reflecting surfaces (IRSs)-enabled phase cooperative non-orthogonal multiple access (NOMA) framework. By exploiting heterogeneous cognitive radio networks concept the aim is to maximize the sum rate of secondary users in the proposed phase cooperative downlink network configuration via optimization solutions. However, the optimization problem comes out to be NP-hard and precludes direct solution. Hence, an alternating optimization is applied at the primary network to solve the maximization problem by exploiting the transmit beamforming (BF) at the power station (PS) and phase shift optimization at the IRS. Later, sum rate maximization for secondary network is performed by utilizing phase shifts of primary network via phase cooperation. In order to find global optimal solutions for active beamformers at both PSs, a branch-reduce-and-bound (BRnB) method is used whereas, passive phase shift optimization at the primary PS is performed via a simple iterative solution, i.e., the element-wise block coordinate descent method. For the proposed framework, Monte-Carlo simulations are performed where the optimality of the global solution is compared with heuristic BF methods including minimum-mean-square-error/regularized zero-forcing-beamforming (ZFBF) and ZFBF. The BRnB algorithm sets an upper performance bound by improving the sum rate of users in comparison with the conventional heuristic BF schemes. This work signifies the utilization of phase cooperation in IRS-assisted NOMA networks for a multi-user environment.

Optimization-based spectrum sensing in crn for effective utilization of available channel

Cognitive radio networks (CRN) are subjected to the challenge of demand in available resources, limited fair resource allocation between users in the heterogeneous CRN. The availability of minimal resources affects the channel allocation between Primary Users (PUs) and Secondary Users (SUs). In heterogeneous CRNs, efficient resources are available to SUs with fair resource allocation in a multi-hop environment. This paper proposed an efficient resource allocation in multi-hop CRN through Monte-Carlo Normal Form (MCNF). The proposed MCNF is involved in the optimization of the available resources through the estimation of the objective function. With Monte-Carlo estimation, MCNF computes the optimal resource availability within the CRN. Based on the Normal Form game theory resource availability of the network is computed and allocated between the users. The performance of the proposed MCNF is computed in terms of throughput, PDR, and running time. The proposed MCNF is comparatively examined with conventional game theory and the Monte-Carlo technique. The simulation analysis of the proposed MCNF expressed that running time is reduced with increased Packet delivery rate (PDR) and throughput.

Energy-efficient distributed heterogeneous clustered spectrum-aware cognitive radio sensor network for guaranteed quality of service in smart grid

The development of a modern electric power grid has triggered the need for large-scale monitoring and communication in smart grids for efficient grid automation. This has led to the development of smart grids, which utilize cognitive radio sensor networks, which are combinations of cognitive radios and wireless sensor networks. Cognitive radio sensor networks can overcome spectrum limitations and interference challenges. The implementation of dense cognitive radio sensor networks, based on the specific topology of smart grids, is one of the critical issues for guaranteed quality of service through a communication network. In this article, various topologies of ZigBee cognitive radio sensor networks are investigated. Suitable topologies with energy-efficient spectrum-aware algorithms of ZigBee cognitive radio sensor networks in smart grids are proposed. The performance of the proposed ZigBee cognitive radio sensor network model with its control algorithms is analyzed and compared with existing ZigBee sensor network topologies within the smart grid environment. The quality of service metrics used for evaluating the performance are the end-to-end delay, bit error rate, and energy consumption. The simulation results confirm that the proposed topology model is preferable for sensor network deployment in smart grids based on reduced bit error rate, end-to-end delay (latency), and energy consumption. Smart grid applications require prompt, reliable, and efficient communication with low latency. Hence, the proposed topology model supports heterogeneous cognitive radio sensor networks and guarantees network connectivity with spectrum-awareness. Hence, it is suitable for efficient grid automation in cognitive radio sensor network–based smart grids. The traditional model lacks these capability features.

Efficient Approximations for Optimization of N-Out-of-K Rule for Heterogeneous Cognitive Radio Networks

Spectrum sensing (SS) has attracted much attention due to its important role in the improvement of spectrum efficiency. However, the limited sensing time leads to an insufficient sampling point due to the tradeoff between sensing time and communication time. Although the sensing performance of cooperative spectrum sensing (CSS) is greatly improved by mutual cooperation between cognitive nodes, it is at the expense of computational complexity. In this paper, efficient approximations of the N-out-of-K rule-based CSS scheme under heterogeneous cognitive radio networks are provided to obtain the closed-form expression of the sensing threshold at the fusion center (FC), where the false alarm probability and its corresponding detection probability are approximated by the Poisson distribution. The computational complexity required to obtain the optimal sensing threshold at the FC has greatly decreased and theoretical derivations state that the approximation error is negligible. The simulations validate the effectiveness of the proposed scheme.

Quality of service provisioning through resource optimisation in heterogeneous cognitive radio sensor networks

Recently, cognitive radio sensor networks (CRSN) have evolved as a result of the introduction of cognitive capabilities to conventional wireless sensor networks. In most CRSN designs, secondary users and/or sensor nodes are permitted, under certain constraints, to use the limited resources of a primary network. One major challenge with CRSN is how to optimally appropriate and use the limited resources available in driving their communication demands. To overcome this challenge, in this paper, we develop a resource allocation (RA) model that is capable of achieving a target quality of service (QoS) demand for the heterogeneous CRSN, despite the huge resource constraints imposed on the network. The RA problem developed is a complex optimisation problem. We analyse and solve the complex RA problem using the optimisation approaches of integer linear programming, Lagrangian duality and by a heuristic. We then study the performance of the RA model for the different solution approaches investigated. The results obtained are used to establish the optimality-complexity trade-off, which is a critical criterion for QoS decision-making in practical CRSN applications.

Two‐tier trading strategy design for spectrum allocation in heterogeneous cognitive radio networks

The heterogeneous network structure is a promising paradigm to improve the quality of service across the entire network. Nevertheless, such a structure is challenging due to the presence of multiple-tier secondary users (SUs). In this study, the authors investigated the effect of spectrum allocation in heterogeneous cognitive radio networks with a primary network and two-tier secondary networks, and proposed a two-tier spectrum trading strategy which includes two trading processes. In Process One, they model the spectrum trading as a monopoly market, where the primary spectrum owner (PO) acts as the monopolist and the first-tier secondary users (FSUs) act as the buyers. They design an optimal quality-price contract to maximise the utility of PO, and the FSUs will choose the spectrum with appropriate quality and price to enhance their satisfaction. In Process Two, spectrum trading is modelled as a multi-seller, multi-buyer market. The dynamic behaviour of second-tier SUs is studied using the theory of evolution game, while the competition among FSUs is analysed via a non-cooperative game where the Nash equilibrium is considered as the solution. The existences of the optimal contract, evolutionary equilibrium and Nash equilibrium are demonstrated in the performance evaluation.

A novel method for the optimization of Spectral -Energy efficiency tradeoff in 5 G heterogeneous Cognitive Radio Network

Cognitve Radio (CR) technology has recently become popular for future communication networks since it allows co-existence of Secondary User (SU) and Primary User (PU) networks. Compared to interweave and overlay, underlay mode of operation is best suited for practical scenarios since it facilitates PU-SU co-existence with minimum overhead. Aiming to enhance Spectral Efficiency (SE) alone using CR may lead to huge power consumption and thus increase the overall cost of network operation. Hence, effort must be taken to maintain a tradeoff between SE and Energy Efficiency (EE). In this work, a novel technique, called Superior Population Generation Algorithm (SPGA), is developed to solve the crucial problem of SE-EE tradeoff for a Fifth generation (5 G) heterogeneous network which uses key future technologies such as CR and Carrier Aggregation (CA). The proposed technique performs optimum resource allocation, by modifying regular Genetic Algorithm (GA) to incorporate the capabilities of conventional techniques. SPGA follows a multi objective optimization approach towards obtaining SE-EE tradeoff, rather than focusing on SE alone. Simulation results show that SPGA outperforms conventional methods and regular GA in terms of SE-EE tradeoff with about 5% and 10% better EE for the same SE respectively. It is also observed that SPGA converges to the Pareto optimal solutions, much faster using only 25% of iterations required for regular GA. All simulations in this work are performed according to 3GPP specifications for 5 G communication networks. Also, power consumption of various base station components is also considered in addition to the transmit power to make the EE calculations further close to reality.

Withdrawn: Hybrid optimisation algorithms for resource allocation in heterogeneous cognitive radio networks

Hybrid optimisation algorithms for resource allocation in heterogeneous cognitive radio networks - Withdrawn on 18th May 2020.

Hybrid Optimization Algorithms for Resource Allocation in Heterogeneous Cognitive Radio Networks

In recent days, accessing the spectrum of Cognitive Radio spread has gained significance in the applications of sensor based wireless devices. In this promising environment the bottle neck existed in accessing the spectrum which makes complications in the setup on wireless networks. In each cognitive space the spread spectrum optimization to the entire user is much more needed for enjoying the fruits of cognitive Radio. To achieve this, an effective mechanism is needed for the effective utilization of spectrum in cognitive radio system. Even though there are many substantial innovations in the area of cognitive radio none of the prevailing articles have focused on providing solutions to the problems which are related to efficient allocation of spectrum for each subordinate user with less power utilization and interference. In this article, the framework has been analyzed in both presence and absence of prime users with maximizing the capacity and data rate of the network. Further, the proposed model is solved by integrating a hybrid optimization algorithm with effective decision-making mechanism. Moreover, the simulation results validate that best solution is achieved considering capacity, spectrum sharing, data rate and interference for each subordinate users in entire network.

On Platform to Enable the Cognitive Radio Over 5G Networks

With the increase in the number of communication devices, the requirement for higher bandwidth is essential. To achieve this goal, research and industrial communities have both suggested that future wireless systems will take advantage of the numerous emerging technologies. Utilization of Cognitive Radio (CR) for the next-generation Fifth Generation (5G) communication technology is the major advancement for getting a higher bandwidth in a cellular communication network. In this paper, we present a comprehensive study of CR from the perspectives of spectrum allocation schemes, impact and role of MAC layer in spectrum sensing and sharing, CR application in multi-hop wireless networks, and challenges associated with channel selection and packet routing in multi-hop heterogeneous CR networks. This paper also presents the analysis, in literature, of a range of intelligent routing protocols that are considered viable for packets routing in CR networks. The need to address the issue of spectrum depletion and the apparent underutilization of available scarce spectrum resources in existing wireless networks is the primary motivation behind this study. Considering the fact that CR technology can potentially maximize the utilization of bulk of the unused communication spectrum bands for the future 5G of wireless network and beyond.

Interference Mitigation of Heterogeneous Cognitive Radio Network using Spatial Diversity

The heterogeneous cognitive radio networks are playing the most important role in the future generation wireless networks in order to address the problem of spectrum scarcity and to satisfy the demand of multiple coexistence networks. . In a country like India the deployment of CRN is possible on television network with the help of TV White Spaces (TVWS) as its capacity is quite high due to digital transmission of TV channel. The use of multiple wireless standards such as IEEE 802.11a, IEEE 802.22, IEEE 802.19.1 and many more wireless networks operating in the same frequency band of TVWS creates the coexistence scenario which involves the heterogeneous networks. The interference mitigation is the most important issue in such heterogeneous networks. In literature, the issue of interference mitigation is addressed mainly at the medium access layer; however, very limited work is presented at physical layer. In this paper, an interference mitigation problem of heterogeneous cognitive radio network at physical layer is addressed. The spatial diversity based techniques are proposed to mitigate the interference in heterogeneous CRN. The coexistence of different wireless networks in secondary CRN is considered for analysis. The characterization of aggregate interference is carried out for different interference scenarios. The proposed system outperform for heterogeneous CRN network over TVWS network.

A Fast Blind Scheme With Full Rendezvous Diversity for Heterogeneous Cognitive Radio Networks

In cognitive radio networks (CRNs), unlicensed secondary users (SUs) need first meet with each other on a commonly available channel before communication, which is referred to as rendezvous. It is preferable for the distributed environments without an infrastructure that using blind rendezvous scheme to achieve rendezvous. In this paper, we propose a fast blind rendezvous scheme named modified enhanced heterogeneous radio rendezvous (MEHRR) scheme for the SUs in distributed heterogeneous CRNs. Compared with our published paper, we consider rendezvous diversity when designing the MEHRR scheme. SUs can achieve full rendezvous diversity (i.e., rendezvous on all commonly available channels) by the MEHRR scheme, which can increase the success probability of rendezvous while reducing the impact of blocking primary users (PUs) for a long time. The upper bounds on the time to rendezvous (TTR) with full rendezvous diversity are derived by theoretical analysis for the MEHRR scheme. Besides, we also theoretically analyze the channel loading, and the optimal allocation of multiple radios that can achieve the shortest maximum TTR (MTTR) with full rendezvous diversity for the SUs using the MEHRR scheme. Simulation results verify the theorems derived by the theoretical analysis. Moreover, the superiority of the MEHRR scheme compared with other state-of-the-art blind rendezvous schemes for the multiple radios-based SUs in terms of MTTR with full rendezvous diversity is also shown by the simulation results.

Energy-Efficient Power Allocation with Guaranteed QoS Under Imperfect Sensing for OFDM-Based Heterogeneous Cognitive Radio Networks

This paper investigates the energy efficient resource allocation scheme for orthogonal frequency division multiplexing based heterogeneous cognitive radio network (HCRN) under imperfect spectrum sensing scenario with guaranteed quality of service (QoS). The objective of this paper is to maximize the energy efficiency (EE) of the HCRN subject to total transmission power, interference and QoS Constraints. To solve the mixed integer nonlinear programming problem efficiently, the primal problem has been transformed into a linear programming problem by separating the resource allocation scheme into two steps, i.e., subcarrier assignment and power allocation. Consequently an energy efficient power allocation (EEPA) algorithm has been anticipated based on fractional programming and sub-gradient method. Numerical results confirm that the proposed EEPA algorithm can achieve higher EE than conventional equal power allocation method.