Energy Efficiency Of Cognitive Radio Network Research Articles

Energy efficiency in cognitive radio network using cooperative spectrum sensing based on hybrid spectrum handoff

Rapid spectrum usage in wireless networks may reduce energy efficiency, requiring cognitive radio networks to be more efficient than conventional ones. Due to increased data transmission demand, cognitive radio networks arose from a lack of spectrum bandwidth. Spectrum sensing and handoff decision are two cognitive radio network strategies that help avoid interference, channel access, and cohabitation between primary and secondary users. Current research focuses on handoff decision and cooperative spectrum sensing to improve sensing efficiency and system throughput while neglecting energy efficiency and handoff latency. Spectrum mobility and sensing are essential for energy-efficient cognitive radio networks. This study provides a second priority user transmission system using cooperative spectrum sensing to sense available channels. An energy detection technique optimizes the sensing process's energy usage, leading to energy efficiency. A primary user traffic pattern-based threshold approach is presented for spectrum mobility management. A threshold approach is utilized to calculate probabilistic stay-and-wait and QoS handoff values. The transmission channel is selected using a hybrid handoff strategy based on dynamic spectrum aggregation. Moreover, a cooperative spectrum sensing algorithm is described and simulated to identify the optimum channel with the greatest throughput and minimum energy consumption. The proposed approach increases energy efficiency and throughput while maintaining handoff delay and avoids miss-detection and false alarm. This technique improves energy efficiency, sensing performance, throughput, and handoff time. miss-detection.

Energy Efficiency Metrics in Cognitive Radio Networks: A Hollistic Overview

Due to the explosive progression in the number of users for new generation wireless communication networks which includes cognitive radio networks, energy efficiency has been a fundamental factor affecting its development and performance. In order to adeptly access and analyze the energy efficiency of a cognitive radio network, a standardized metric for this purpose is required. As a starting point, in this article we provided an analysis for energy efficiency metrics of a cognitive radio network in respect to its design and operation. The performance metrics and metrics developed at the different levels of a cognitive radio network are also studied. Establishing a comprehensive metric for evaluating, measuring and reporting the energy efficiency of cognitive radio networks is a crucial step in achieving an energy-efficient cognitive radio network.

Carrier Frequency Offset (CFO) Synchronization and Peak Average Power Ratio (PAPR) Minimization for Energy Efficient Cognitive Radio Network (CRN) for 5G Wireless Communication

Carrier Frequency Offset (CFO) Synchronization and Peak Average Power Ratio (PAPR) Minimization for Energy Efficient Cognitive Radio Network (CRN) for 5G Wireless Communication

MIMO Based Cognitive Radio Network with Efficient Spectrum Utilization

This paper presents an energy-efficient power control scheme for Multi-Input–Multi-Output (MIMO) based multi-channel cognitive radio networks (CRNs) with adaptive mode selection protocol. Cognitive radio device operates in the temporal domain to utilize spectrum holes and MIMO system operates in the spatial domain. Thus CRN in collaboration with MIMO can produce a significant improvisation for spectrum utilization, and therefore, the integration of MIMO and CRN has received widespread research interest. To address the challenges of the collaboration of MIMO and CRN, we propose a MIMO based CRN network in this paper, in which basic CRN is modified with the QPSK transmission technique having four antenna elements per primary users (PU) and secondary users (SU). Similarly, MIMO significantly improves channel capacity particularly in SU transmission under low SNR condition, but issues like several antenna elements and specific antenna selection issues increase the computational complexity and energy consumption. The proposed network protocol strives to improve communication stability and rate of throughput via selecting the MIMO operating parameters such as multiplexing, diversity, or hybrid multiplexing-diversity gains. Specifically, we designed transmission power distribution along with an antenna selection scheme for enhancing the energy efficiency of CRN’s under the influence of the maximum interference caused by the SU to PU, utmost power transmission from SU, and the least rate of transmission through the SU link. Finally, the simulation results are compared with basic CRN alone using the BPSK scheme to evaluate and validate the proposed scheme.

Optimal Sensing and Transmission of Energy Efficient Cognitive Radio Networks

The issue of spectrum scarcity can be alleviated by the cognitive radio technology with efficient spectrum sensing and allocation of free spectrum bands. In Cognitive Radio Networks energy efficiency improvement is the state of art now days. This paper considers the case of primary user protection from cognitive user transmission to optimize the energy efficiency. The parameters of optimal design problem are sensing, transmission time and transmission power. A Sub Optimal Iterative Search Algorithm is proposed to maximize efficiency by optimizing sensing time and transmitting time. Simulation results exhibits substantial improvement in energy efficiency compared to the recent algorithms.

A Comprehensive Review on Spectrum Management, Security and Energy-Efficient Cognitive Radio Networks

The explosive growth of mobile devices and the rapid increase in wireless services necessitate an improvement in communication techniques. This technology can boost spectrum utilization as well as meet the massive connectivity requirement to draw attention from academia and industry. Cognitive Radio Network (CRN) is an evident optimal solution in alleviating the challenges of spectrum unused potentially in wireless networks. The Secondary Users are allowed intelligently to access the sections of spectrum unused (spectrum holes) by the authorized Primary Users (PUs). The objective of this paper is to analyze channel throughput, delay, carrier aggregations, energy efficiency, and security challenges and provide research ideas to enhance the overall performance of the CRN. This review paper completely investigated the potential management of spectrum techniques, essentially in spectrum sensing, spectrum allocation, spectrum sharing, and spectrum handoff in CRN. In addition, we study the energy efficient routing techniques like network optimization frameworks in cooperative networks, protocols and different types of attack issues, approaches for handling the secure communication in CRNs. The state of art review, which deeply examines the Cognitive Radio Network providing an idea of important technology and challenges are addressed. The open research challenges related to spectrum management, energy efficient routing and security are discussed.

Cognitive Radio Network With Energy-Harvesting Based on Primary and Secondary User Signals

Cognitive radio (CR) provides a radio-access method for unlicensed/secondary users to share the spectrum of licensed/primary users. To improve the energy efficiency of CR networks, we propose an energy-harvesting-aided spectrum sensing and data transmission scheme in this paper. In the proposed scheme, the individual sensing and the cooperative sensing of multi-users are combined corresponding to the strong and weak signal of primary users. Meanwhile, the transmission signal of local decisions in the cooperative sensing and the strong signal of primary users are harvested as RF energy by secondary users. In order to improve the performance of spectrum sensing and energy harvesting, we formulate a multi-objective optimization problem that jointly maximizes the detection probability of the presence of primary user and minimizes the false alarm probability while limiting the least harvested energy and the interference from secondary users on the primary receiver. The multi-objective optimization problem is solved by transferring it into a single objective problem. The single objective problem on the parameters without certain range is transferred into the problem on the parameters with certain range to obtain the final solution. Numerical results prove the effectiveness of our proposed scheme, that is, the interference from primary users on secondary users can improve the global detection probability and the harvested energy at secondary users.

Intelligent resource allocation scheme for the cognitive radio network in the presence of primary user emulation attack

An energy-efficient cognitive radio network (CRN) design is one of the primary requirements for the low-battery-driven wireless terminals in the presence of primary user emulation attack. Furthermore, sensing accuracy is also essential for allocating vacant bands to the secondary users (SUs) for data transmission. Hence, this study focuses on designing an energy-efficient double threshold-based CRN. The adversarial effects arising in the presence of the attacker are analysed and are treated as constraints while formulating the energy efficiency (EE) maximisation problem. To develop the formulation, a unique SU selection algorithm to identify most eligible SUs for data transmission is proposed. The EE is then maximised by minimising the total power consumption through novel adaptive resource allocation algorithm. Hence, the authors proposed approaches are based on the suitable selection of SUs and adaptive power allocation under the constraints of maximum throughput, controlled transmission power providing sufficient protection to the primary user, minimum power consumption and minimum false alarm probability. The extensive simulation results demonstrate that the proposed scheme substantially increases EE with lower complexity over the conventional scheme.

An intelligent resource management scheme for SDF-based cooperative spectrum sensing in the presence of primary user emulation attack

This paper focuses on designing an energy-efficient cognitive radio network (CRN) in the presence of a primary user emulation attacker. The approach requires suitable resource management schemes that help in maximizing energy efficiency (EE) of the secondary network while providing sufficient protection to the primary network. Hence, we propose a secondary users (SUs) selection algorithm for maximizing system throughput while ensuring spectrum sensing accuracy. Then the sensing time is evaluated for obtaining maximum throughput, minimum delay and minimum false alarm probability. Further, the non-linear EE maximization problem associated with the non-convex constraints is solved by our proposed novel iterative resource allocation (NIRA) algorithm to jointly optimize both sensing time and transmission power of the secondary network within which the fractional EE is transferred to convex programming by employing the Dinkelbach method in the outer layer and the constraints are addressed by dual decomposition and Difference of convex (DC) programming method. Simulation results validate the effectiveness of our proposed approaches, and the impact of different system parameters is also studied.

Energy-Aware Scheduling for Multi-Hop Cognitive Radio Networks

Cognitive radio (CR) technology, which enables unlicensed secondary users to opportunistically access the unused licensed spectrum, has attracted more and more attention from both academia and industry due to its potential to significantly improve the spectrum utilization. Considering both temporal and spatial variations of spectrum availability, this paper focuses on improving the energy efficiency in CR networks by opportunistically serving the delay-tolerant data only when enough spectrum is available. Based on this idea, a stochastic optimization problem is formulated to integrate the power control, link scheduling, and routing, which minimizes the expected power consumption while guaranteeing the system stability. To obtain the solution, we use the Lyapunov optimization technique and design an online algorithm, which solves a sub-problem without future knowledge of the related stochastic models (e.g., random data arrival and spectrum supply). Besides, in view of the NP-hardness of the sub-problem, we also develop a heuristic algorithm based on branch-and-bound framework to obtain the approximate solution with low computing complexity. Theoretical analysis shows that our algorithm offers an explicit tradeoff between energy consumption and delay performance. Numerical results also confirm the effectiveness of our solutions.

Identification and Punishment Policies for Spectrum Sensing Data Falsification Attackers Using Delivery-Based Assessment

Spectrum sensing data falsification (SSDF) attacks represent a major challenge for cooperative spectrum sensing (CSS) in cognitive radio (CR) networks. In an SSDF attack, a malicious user or many malicious users send false sensing results to the fusion center (FC) to mislead the global decision about spectrum occupancy. Thus, an SSDF attack degrades the achievable detection accuracy, throughput, and energy efficiency of CR networks (CRNs). In this paper, a novel attacker-identification algorithm is proposed that is able to skillfully detect attackers and reject their reported results. Moreover, we provide a novel attacker-punishment algorithm that aims at punishing attackers by lowering their individual energy efficiency, motivating them either to quit sending false results or leave the network. Both algorithms are based on a novel assessment strategy of the sensing performance of each user. The proposed strategy is called delivery-based assessment, which relies on the delivery of the transmitted data to evaluate the made global decision and the individual reports. Mathematical analysis and simulation results show promising performance of both algorithms compared with previous works, particularly when then the number of attackers is very large.

Sensing throughput trade‐off for an energy efficient cognitive radio network under faded sensing and reporting channel

SummaryThe trade‐off between sensing time and throughput is investigated in the context of an energy‐efficient cognitive radio network considering both the sensing and reporting channels are Rayleigh faded, while the existing literature considered the fading in sensing channel only. In this paper, such a trade‐off is re‐examined under Rayleigh faded sensing as well as reporting channel. Novel analytical expressions for overall detection probability and false alarm probability are developed under such scenario. The performance is investigated in terms of detection probability, false alarm probability, throughput and energy efficiency of the network for different sensing parameters such as sensing time, number of samples, sensing channel signal‐to‐noise ratio and reporting channel signal‐to‐noise ratio. Our analysis shows that the quality of the reporting channel significantly affects the trade‐off performance of the network. Copyright © 2015 John Wiley & Sons, Ltd.

Energy-efficient cognitive radio networks [Guest Editorial

Spectrum efficiency and energy efficiency are two critical issues for wireless communication networks. As a promising paradigm to improve spectrum usage efficiency, much attention has been paid to cognitive radio (CR) in both academia and industry since it was introduced more than 10 years ago. On the other hand, fast growing wireless applications are consuming more and more energy, and pose big challenges to operators in terms of energy footprint. However, few discussions were held on the energy efficiency issue in CR networks (CRNs) until recently. Actually, energy efficiency is of crucial importance for a CR scenario compared to non-CR ones because it not only involves the greenhouse problem and operational expenditure, but is a prerequisite to achieve high utilization of the limited transmission power consumed to support additional signal processing requirements for the CR system, such as spectrum sensing and signal overhead. Optimizing the energy efficiency of CRNs not only reduces the environmental impact, but also cuts deployment costs to enable economical green wireless networks.

Energy-efficient non-cooperative cognitive radio networks: micro, meso, and macro views

Cognitive radio technology can significantly improve spectrum utilization efficiency via enabling secondary users to access licensed spectrum dynamically without harmful interference to primary users. Most existing works on cognitive radio networks were focused on enhancing spectrum efficiency using spectrum sensing, spectrum sharing, and network deployment schemes. However, the energy efficiency issue was largely ignored. Thus, the dilemma between high energy consumption by fast-growing wireless communications and fewer global energy resources has motivated research on energy-efficient cognitive radio networks for enhancing spectrum efficiency and energy efficiency simultaneously. In this article, we overview energy-efficient non-cooperative cognitive radio networks from the micro, meso, and macro perspectives, where the micro view means how to design energy-efficient spectrum sensing algorithms for each individual secondary user, the meso view means how to coordinate non-cooperative secondary users to share spectrum efficiently, and the macro view means how to deploy cognitive radio networks in an energy-efficient approach.

Energy efficient cognitive radio network based on multiband sensing and spectrum sharing

The authors consider the problem of energy consumption in a multiband cognitive radio network, in which a secondary user can sense multiple frequency bands authorised to the primary user (PU) before it decides to access some frequency bands and keep the interference introduced to the PU under a certain threshold. The sensing time and power allocation need to be designed carefully to reduce the energy consumption. Therefore they identify the optimal sensing time and accessing power to minimise the energy consumption of the cognitive radio that simultaneously senses and accesses multiple bands under the multiband sensing-based spectrum sharing scheme. To obtain the optimal solution, they transform the original problem formulated as the ratio of two functions into another parametric formulation. The interference power is considered as one of constraints in the author's scheme in order to protect the PU. The algorithm of optimal sensing time and power allocation is proposed for the multiband cognitive radio system. Furthermore, they discuss the effect of channel number, interference power and gain estimation of channels on energy consumption and optimal sensing time and validate their schemes and analysis by simulation results.

Optimal Energy-Efficient Sensing and Power Allocation in Cognitive Radio Networks

We consider a joint optimization of sensing parameter and power allocation for an energy-efficient cognitive radio network (CRN) in which the primary user (PU) is protected. The optimization problem to maximize the energy efficiency of CRN is formulated as a function of two variables, which are sensing time and transmit power, subject to the average interference power to the PU and the target detection probability. During the optimizing process, the quality of service parameter (the minimum rate acceptable to secondary users (SUs)) has also been taken into consideration. The optimal solutions are analyzed and an algorithm combined with fractional programming that maximizes the energy efficiency for CRN is presented. Numerical results show that the performance improvement is achieved by the joint optimization of sensing time and power allocation.

Optimal power allocation for green cognitive radio: fractional programming approach

In this study, the problem of determining the power allocation that maximises the energy efficiency of cognitive radio network is investigated as a constrained fractional programming problem. The energy-efficient fractional objective is defined in terms of bits per Joule per Hertz. The proposed constrained fractional programming problem is a non-linear non-convex optimisation problem. The authors first transform the energy-efficient maximisation problem into a parametric optimisation problem and then propose an iterative power allocation algorithm that guarantees e-optimal solution. A proof of convergence is also given for the e-optimal algorithm. The proposed e-optimal algorithm provide a practical solution for power allocation in energy-efficient cognitive radio networks. In simulation results, the effect of different system parameters (interference threshold level, number of primary users and number of secondary users) on the performance of the proposed algorithms are investigated.

Reinforcement learning based sensing policy optimization for energy efficient cognitive radio networks

This paper introduces a machine learning based collaborative multi-band spectrum sensing policy for cognitive radios. The proposed sensing policy guides secondary users to focus the search of unused radio spectrum to those frequencies that persistently provide them high data rate. The proposed policy is based on machine learning, which makes it adaptive with the temporally and spatially varying radio spectrum. Furthermore, there is no need for dynamic modeling of the primary activity since it is implicitly learned over time. Energy efficiency is achieved by minimizing the number of assigned sensors per each subband under a constraint on miss detection probability. It is important to control the missed detections because they cause collisions with primary transmissions and lead to retransmissions at both the primary and secondary user. Simulations show that the proposed machine learning based sensing policy improves the overall throughput of the secondary network and improves the energy efficiency while controlling the miss detection probability.

Adaptive transmission scheduling over fading channels for energy-efficient cognitive radio networks by reinforcement learning

In this paper, we address a cross-layer issue of long-term average utility maximization in energy-efficient cognitive radio networks supporting packetized data traffic under the constraint of collision rate with licensed users. Utility is determined by the number of packets transmitted successfully per consumed power and buffer occupancy. We formulate the problem by dynamic programming method namely constrained Markov decision process (CMDP). Reinforcement learning (RL) approach is employed to finding a near-optimal policy under undiscovered environment. The policy learned by RL can guide transmitter to access available channels and select proper transmission rate at the beginning of each frame for its long-term optimal goals. Some implement problems of the RL approach are discussed. Firstly, state space compaction is utilized to cope with so-called curse of dimensionality due to large state space of formulated CMDP. Secondly, action set reduction is presented to reduce the number of actions for some system states. Finally, the CMDP is converted to a corresponding unconstrained Markov decision process (UMDP) by Lagrangian multiplier approach and a golden section search method is proposed to find the proper multiplier. In order to evaluate the performance of the policy learned by RL, we present two naive policies and compare them by simulations.