Green Cognitive Radio Research Articles

To mitigate primary user emulation attack trajectory using cognitive single carrier frequency division multiple access approaches: Towards next generation green IoT

AbstractThe growing cognizance of spectrum scarcity has become a more significant concern in wireless radio communications. Due to the exponential growth of data transmission in intelligent wireless sensor networks, energy spectrum detection has become a promising solution for resolving spectrum shortages. Primary user emulation attack (PUEA) has been identified as a significant attack vector in the cognitive radio (CR) domain's physical layer. In comparison, the CR is a promising method to increase spectrum efficiency by allowing unlicensed secondary users (SUs) to access licensed frequency bands without interfering with approved primary users (PUs). The study's primary findings are the methodology for preventing PUEA using authentication tags, which are unique sequences. This research blends SC‐FDMA with CR to protect CR networks from PUEA attacks, a Latin square (LS) matrix tag generation system is proposed to mitigate the PUEA effect. The technology is meant to provide effective authentication and protection against malicious users. In a secured environment, the LS tag technique is utilized to track and estimate the PU. For example, the BER of both techniques is virtually identical between 0 and 4 dB, while the BER performance of the suggested LS tag generation improves with increasing signal‐to‐noise ratio (SNR). As a result, the suggested LS tag generation is less susceptible to PUEA. To diminish the influence of PUEA in CR networks, an efficient enlightening approach for making the future Green Cognitive Radio Wireless networks structure is proposed. The simulation results also demonstrate the resilience of the proposed CR spectrum sensing techniques for energy‐efficient knowledge at varying degrees to reduce the adverse effects of environmental technologies.

Deep Q-learning multiple networks based dynamic spectrum access with energy harvesting for green cognitive radio network

In cognitive radio (CR), dynamic spectrum access (DSA) can be used for secondary user (SU) to improve its spectrum efficiency under the limited spectrum resources. However, the SU has to consume more energy to sense the state of primary user (PU) before spectrum access. Therefore, high spectrum efficiency and lower energy consumption have become the requirements of CR. In this paper, we propose a sustainable green CR network combining DSA and energy harvesting, which can perform intelligent spectrum sensing, spectrum access and energy harvesting by a reward mechanism of deep Q-learning multiple networks (DQMN). The DQMN model consisting of multiple neural networks is designed to learn multiple learning objectives, including spectrum sensing, channel selection, communication power and access mode in DSA. A sleep mechanism is introduced to optimize the spectrum access mode and further reduce the energy consumption. The simulation results show that the DQMN-based DSA model can autonomously sense the channel state, harvest the energy, and then select the appropriate access strategy. Due to the energy harvesting and sleep mechanism, the proposed system model shows lower energy consumption and higher average effective throughput than the traditional models.

CEAR: A cooperation based energy aware reward scheme for next generation green cognitive radio networks

Cognitive Radio (CR) networks are envisioned as a key empowering technology of the fifth-generation (5G) wireless communication networks, which solves the major issues of 5G, like high-speed data transmission, seamless connectivity, and increased demand for mobile data. Another significant characteristic of the 5G network is green communications, as energy consumption from the communication field is predicted to rise remarkably by the year 2030. In this work, we are concerned about energy-related issues and propose a cooperation-based energy-aware reward scheme (CEAR) for next-generation green CR networks. The proposed CEAR scheme is based on the antenna and temporal diversity of the primary users (PUs). For providing the service to the PUs, the users of another network called cognitive users (CUs) work as a cooperative relay node, and, in return, they get more spectrum access opportunities as a reward from the primary network. The CUs with delay-tolerant data packets take a cooperative decision by recognizing the availability and traffic load of PUs, channel state information, and data transmission requirements. We utilize the optimal stopping protocol for solving the decision-making problem and use the backward induction method to obtain the optimal cooperative solution. The simulation results reveal notable enhancements in energy efficiency (EE) of CUs compared with other cooperative schemes. The proposed CEAR scheme is more energy-efficient for ultra-dense network deployment because results show that the CU’s EE, spectral efficiency (SE), and throughput improved with the increase of PUs.

An AHP-TOPSIS Integrated Model for QoS-Aware Energy Efficiency in Green Cognitive Radio Networks

The selection of the best spectrum band available to meet the quality of service requirements of secondary users without interfering with the transmission of the primary users is considered as a challenge in cognitive radio networks. On the other side, green network is a recent concept that refers to the processes used to optimize a network in order to make it more energy efficient. In this paper, we propose a new algorithm ensuring the selection of the best available spectrum satisfying the demands of the secondary users based on TOPSIS and AHP in OFDM-based cognitive radio networks. We will assess the need for the secondary users in terms of quality of service and energy efficiency, by analyzing the characteristics of the available channels and taking into account the interference generated with the presence of the primary user. It efficiently manages energy because it allows a significant reduction of the transmission power used by the secondary user and therefore presents an effective solution in green networking.

Machine Learning for Spectrum Information and Routing in Multihop Green Cognitive Radio Networks

Research works on cognitive radio networks (CRNs) together with energy harvesting (EH) promise to address the spectrum scarcity and limited battery power problems on the wireless communication nodes. While radio frequency (RF) signal of primary user (PU) can be used in EH, its absence (non-transmission state) offers an unused spectrum for the availability of the secondary user (SU) data transmission. Spectrum sensing (SS) process that detects the presence or the absence of PU signal, occupies a considerable slot in time-frame and energy consumption in wireless nodes, hence, both SU throughput and battery power get reduced. To address the problems, this work explores a support vector machine (SVM) based PU activity (transmit/non-transmit mode) prediction without accomplishing any SS task in the system operation. Thereafter, a Deep Q-networks (DQN) based energy and spectrum efficient routing strategy is suggested to maximize the CRNs throughput. The proposed joint EH and routing scheme improve the sum throughput, spectrum and energy efficiencies by <inline-formula> <tex-math notation="LaTeX">$\sim 33.48\%$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\sim 34.24\%$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\sim 30.76\%$ </tex-math></inline-formula>, respectively over the conventional dedicated SS based approach.

Jamming in Eavesdropping on Throughput Maximization in Green Cognitive Radio Networks

This work considers a cognitive radio (CR) network consisting of a set of CR transmit-receive node pairs, one fusion center (FC), multiple primary user emulation attack (PUEA) nodes, an eavesdropper ( <inline-formula><tex-math notation="LaTeX">$E_{av}$</tex-math></inline-formula> ) node and a set of friendly jammers used for protection of CR data transmission from eavesdropping. At the initial time slot of the frame, simultaneous energy harvesting (EH) and spectrum sensing are done through power splitting (PS) mode. CR transmit nodes then amplify and forward the sensed samples of both the primary user (PU) and the PUEA to the FC for cooperative spectrum sensing (CSS). Based on the CSS decision, CR transmit nodes either perform EH over the entire duration or make an opportunistic data transmission in time division mode. The closed form expressions of the optimal sensing duration, power allocation factor and transmit power for each secondary user (SU) are found. The sum secondary throughput of the network is maximized under the constraints of meeting the sensing reliability of the PU, individual energy causality for each SU and the best selected jammer, interference at the PU receiver, individual secondary and secrecy outage probability. Simulation results show a performance gain on the maximum value of the sum secondary throughput by <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 17.56 and <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 49.69 percent over the existing works.

FPGA Based Hardware Implementation For Green Cognitive Radio

Greening of communication systems leads to provide a solution for spectrum scarcity, high energy consumption, secure and fast transmission, and diversity for user access. OFDM with cognitive radio offer satisfactory solutions for all these heads. The cognitive radio system senses the surrounding environment possesses an electromagnetic spectrum. CR can adapt the radio parameter dynamically and autonomously. It improves the performance of the wireless communication system by endowing the opportunity to unlicensed secondary users temporarily. This arrangement maximizes network throughput with an interoperability facility. This paper presents a hardware implementation of a Green Cognitive Radio Communication (GCR) using an FPGA board. It defines the complete process of software and hardware implementation for the practical execution of this novel approach.

Optimization of Resource Allocation Model With Energy-Efficient Cooperative Sensing in Green Cognitive Radio Networks

Green cognitive radios show promise for high energy efficiency (EE) in the future of wireless communications. Spectrum sensing refers to an energy-consuming procedure that allows cognitive users to independently identify unused radio spectrum segments and prevent interference to primary users, and it should be minimized due to resource limitations. In this paper, we present a wireless multiple-access channel function to establish the primary users’ presence and the mean EE optimization problem of the cognitive radio systems with mathematical structure computation purposes. EE, as the average throughput to the average energy consumption ratio, is used to measure the network’s performance subject to detection constraint. More specifically, since secondary users are generally battery-powered devices, saving on energy is crucial. We aim to decrease the energy consumption of secondary users while maximizing the total EE and preserving the accurate sensing by maximizing the sensing time detection subject to secondary user power constraints and minimum data rate. To address the non-convexity optimization problem, one can consider energy-efficient power allocation based on an iterative method. Simulation results show the optimum of our framework when combined by the multiple-access channel computation-based scheme. Green cognitive radio should consider the tradeoff against its complexity and maximum available EE metric. However, one can observe from the simulation results that the improved EE presented in this work yields much higher when compared with others with the same detection performance.

Resource Allocation for Green Cognitive Radios: Energy Efficiency Maximization

Green cognitive radios are promising in future wireless communications due to high energy efficiency. Energy efficiency maximization problems are formulated in delay‐insensitive green cognitive radio and delay‐sensitive green cognitive radio. The optimal resource allocation strategies for delay‐insensitive green cognitive radio and delay‐sensitive green cognitive radio are designed to maximize the energy efficiency of the secondary user. The peak interference power and the average/peak transmit power constraints are considered. Two algorithms based on the proposed resource allocation strategies are proposed to solve the formulated problems. Simulation results show that the maximum energy efficiency of the secondary user achieved under the average transmit power constraint is higher than that achieved under the peak transmit power constraint. It is shown that the design of green cognitive radio should take the tradeoff between its complexity and its achievable maximum energy efficiency into consideration.

Energy-Efficiency Joint Cooperative Spectrum Sensing and Power Allocation Scheme for Green Cognitive Radio Network: A Soft Decision Fusion Approach

This paper focus on a jointly spectrum sensing parameter and energy efficiency (EE) optimization problem in OFDMA CRN system for enabling Green Communication. In this perspective, we firstly propose an algorithm to choose less spatially-correlated cognitive users to reduce the shadowing effect in wireless network. Furthermore, based on Lagrangian duality theorem with the aid of parametric transformation, the algorithm called an Iterative Dinkelbach Scheme (IDS) is proposed to optimize both transmission power allocation and sensing duration of the cognitive users (Cus) for maximizing Energy Efficiency under the constraints of overall outage of cognitive network, interference to the PU, maximum transmission power and minimum data rate requirement. Numerical result proves the effectiveness of our proposed algorithm. Compared with existing schemes, our proposed scheme outperforms in enhancing the EE with the same parameters.

Distributed Energy-Spectrum Trading in Green Cognitive Radio Cellular Networks

Reducing the power consumption of base stations is crucial to enhancing the energy efficiency of cellular networks. As the number of mobile users increases exponentially, enhancing the spectrum efficiency is also critical in order to accommodate more users. In this paper, by exploiting the cooperation between secondary base stations (SBSs) and primary base stations (PBSs), we propose a new energy spectrum trading model to enhance the energy as well as spectrum efficiency of cellular networks. In our scheme, by leveraging cognitive radio, PBSs share some portion of their licensed spectrum with SBSs, and SBSs, in exchange, provide data service to the primary users under their coverage. We first prove that the power consumption minimization problem is NP-hard. Then, to decrease the computational complexity, we design an efficient distributed auction model including green energy aware bidding (GEAB) and adaptive bid selection (ABS) algorithms, to achieve a good approximation of the optimal solution in less time. Our simulation results show that the cooperation between PBS and SBSs via ABS and GEAB algorithms can significantly improve the energy and spectral efficiency of cellular networks by nearly doubling the number of offloaded users and reducing the PBS power consumption by up to 40% as compared to existing approaches. Furthermore, green energy utilization among SBSs is increased by nearly 25%.

Transmission range-aware clustering for green cognitive radio ad hoc networks

The absence of network infrastructure and opportunistic spectrum access in cognitive radio ad hoc networks (CRAHNs) results in connectivity and stability problems. Clustering is known as an effective technique to overcome this problem. Clustering improves network performance by implementing a logical network backbone. Therefore, how to efficiently construct this backbone among CRAHNs is of interest. In this paper, we propose a new clustering algorithm for CRAHNs. Moreover, we model a novel cluster head selection function based on the channel heterogeneity in term of transmission ranges. To the best of our knowledge, this is the first attempt to model the channel heterogeneity into the clustering formation in cognitive radio networks. Simulation results show that the performance of clustering is significantly improved by the channel heterogeneity considerations.

Harvesting‐throughput tradeoff for RF‐powered underlay cognitive radio networks

A green cognitive radio that is solely powered by an energy harvester which extracts energy from the RF signal of a primary user transmitter is considered. The cognitive radio operates in a harvesting–transmitting fashion with the period of one time slot. In each period, it first harvests energy for a fixed duration and then transmits data using the harvested energy for the rest of the period. Considering the intrinsic harvesting-throughput tradeoff, we optimise the harvesting ratio of the cognitive radio to maximise its achievable throughput, subject to the interference constraint by the primary user receiver. It is shown that the formulated optimisation problem is convex and we derive a closed-form expression for the optimal harvesting ratio. Simulations validate the correctness and analyse the achievable throughput and optimal harvesting ratio under different parameters.

RF-Powered Green Cognitive Radio Networks: Architecture and Performance Analysis

In this letter, we consider an RF-powered green cognitive radio network (RF-GCRN), where a central node, called a power beacon (PB), harvests green energy from ambient sources and wirelessly delivers random harvested energy to cognitive users. Random in-band energy transmission by PB is the only energy source of cognitive users. Performance of this network, with a single pair of secondary users, is analyzed under two spectrum access schemes, i.e., random access and spectrum sensing-based access schemes. Results show feasibility of the RF-GCRN model, if the energy transmission rate is below a certain threshold. This threshold is determined according to maximum tolerable delay of primary user and parameters of spectrum access scheme. Finding a closed form expression for this threshold results in a quite complicated formula, which cannot be helpful in determining feasibility region, due to excessive complexity. Instead, we numerically calculated feasibility regions of both access schemes to facilitate parameter selection process.

Combined Optimization of Clustering Using Artificial Bee Colony Algorithm and Cooperative Beamforming in Green Cognitive Radio Networks with Relays

In this paper, our objective is to reduce the interference in the direction of Primary Users (PUs) and to decrease the power cost of Secondary Users (SUs) in the Cognitive underlay system. Artificial Bee Colony (ABC) algorithm, one of the most recently introduced meta-heuristic optimization algorithms is used here as the clustering algorithm. Here relay stations are introduced in cooperative beamforming technique, which are equipped with directional antennas that can efficiently improve the spectral efficiency and can reduce the interference to primary users in a cognitive underlay systems. In the proposed research, flexible cooperation is considered as a general case while full cooperation and inter-cell cooperation are considered as special cases. We jointly optimize the beamforming and the clustering to reduce the overall power consumption of the secondary users and to satisfy the primary users interference limits. Inorder to find optimal solution, an iterative algorithm called generalized Benders decomposition method is applied. We have investigated the benefits of ABC algorithm and showed the advantages of the relays in cognitive wireless networks.

On Green Cognitive Radio Cellular Networks: Dynamic Spectrum and Operation Management

We study a profit maximization problem related to cognitive radio cellular networks in an environmentally friendly framework. The objective of the primary network (PN) and the secondary network (SN) is to maximize their profits while respecting a certain carbon dioxide (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) emission threshold. In this paper, the PN can switch off some of its base stations (BSs) powered by microgrids, and hence leases the spectrum in the corresponding cells, to reduce its footprint. The corresponding users are roamed to the SN infrastructure. In return, the SN receives a certain roaming cost and its users can freely exploit the spectrum. We study two scenarios in which the profits are either separately or jointly maximized. In the disjoint maximization problem, two low-complexity algorithms for PN and SN BS on/off switching are proposed to maximize the profit per CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission utility and determine the amount of the shared bandwidth. In the joint maximization approach, the low-complexity algorithm is based on maximizing the sum of weighted profits per CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . Selected numerical results illustrate the collaboration performance versus various system parameters. We show that the proposed algorithms achieve performances close to those obtained with the exhaustive search method, and that the roaming price and the renewable energy availability are crucial parameters that control the collaboration of both networks.

Parameter Optimization Based on Evolutionary Algorithms for Green Cognitive Radio

In this paper, we study parameter adjustments that maximize the energy efficiency of green cognitive radio. Because the problem of parameter adjustments for green cognitive radio can be looked at as a complex discrete optimization problem, evolutionary algorithms can be applied to solve it. Parameter optimization methods based on quantum genetic algorithm (QGA), particle swarm optimization (PSO), and chaotic quantum particle swarm optimization (CQPSO) are designed. In particular, CQPSO integrates the characteristics of chaos with quantum particle swarm optimization (QPSO), which gives it strong global search abilities. Chaotic mutation is introduced into the proposed CQPSO to avoid premature convergence and keep diversity in populations. Quantum computing has excellent features used to increase optimization speed and enhance the search abilities of the algorithm. The proposed CQPSO method provides good performance in terms of convergence rate and convergence accuracy, and can search for an optimal solution to parameter adjustments in green cognitive radio networks. Through simulations comparing it to QGA and PSO, we conclude that the proposed CQPSO can improve energy efficiency and meet users’ quality of service needs.

Quantum-inspired bacterial foraging algorithm for parameter adjustment in green cognitive radio

Parameter adjustment that maximizes the energy efficiency of cognitive radio networks is studied in this paper where it can be investigated as a complex discrete optimization problem. Then a quantum-inspired bacterial foraging algorithm (QBFA) is proposed. Quantum computing has perfect characteristics so as to avoid local convergence and speed up the optimization of QBFA. A proof of convergence is also given for this algorithm. The superiority of QBFA is verified by simulations on three test functions. A novel parameter adjustment method based on QBFA is proposed for resource allocation of green cognitive radio. The proposed method can provide a globally optimal solution for parameter adjustment in green cognitive radio networks. Simulation results show the proposed method can reduce energy consumption effectively while satisfying different quality of service (QoS) requirements.

Clustering and Cooperative Beamforming in Green Cognitive Radio Networks with Relays

A relay assisted cooperative beamforming technique can efficiently improve the spectral efficiency and can reduce the interference to primary users in a cognitive underlay systems. Our objective is to reduce the total power cost of Secondary Users (SU) while limiting the interference in the direction of Primary Users (PU). Kmean algorithm which is a partition based clustering algorithm is used here as the clustering algorithm. While considering flexible cooperation as general scheme, we collectively optimize the clustering and the beamforming to lower the overall power consumption while fulfilling the secondary users quality of service and satisfying the primary users interference temperature limits. An iterative algorithm called generalized Benders decomposition method is used to find an optimal solution. The indirect transmission mode of relays is considered in this work. The relays are equipped with directional antennas which will reduce the interference to the PU and meet the power cost requirement of SU under the assumptions that the clusters are non-overlapping and fixed. Thus spectral efficiency can be increased. Through simulations, we analyzed the advantages of algorithms and showed the benefits of the relays in cognitive wireless networks.

Resource Allocation in Hospital Networks Based on Green Cognitive Radios

Pervasive wireless communication and computing can facilitate a variety of e-health applications to communicate patient medical data and information to the doctors/caregivers in an e-Health environment. With the advancement of medical devices and practicing methods the concept of in-cooperating latest communication techniques have taken a new direction by enabling cognitive radio networks in e-Health applications. This paper presents an approach to solve the joint call admission control and power allocation problem in a hospital environment based on green cognitive radio. Specifically, a multi-objective non-convex mixed integer non-linear programming (MINLP) problem of wireless access in an indoor hospital environment is formulated to maximize the number of admitted secondary users and minimize transmit power and carbon dioxide emission. Along with that the throughput requirement of all secondary users and interference constraints for the protected and primary users are also satisfied. In this paper, we invoke outer approximation approach based linearization technique to solve MINLP problem. The proposed method gives guaranteed $$\varepsilon $$? convergence to the optimal solution results with reasonable computational complexity. Simulation results verify the effectiveness of the proposed approach method.