Efficiency Of Cognitive Radio Networks Research Articles

Spectrum and Energy Efficiency in CRNs Using Dynamic Channel Reservation and Komodo Mlipir Optimization

Cognitive Radio Networks (CRNs) block new arriving services and remove existing services to increase the spectrum efficiency. Particularly, Secondary Users (SUs) experience a decline in service deprivation in the arrival of Primary Users (PUs). To overcome this, a Spectrum and energy Efficiency in Cognitive Radio Networks using Dynamic Channel Reservation and Komodo Mlipir Optimization (SE-KMOA-CRN) is proposed. The proposed method splits reserved and non-reserved bands from the accessible spectrum. Numerous channels from the non-reserved band are dynamically assigned to the reserved band to support services that experience disruptions when functioning on non-reserved band. From the non-reserved band, a count of channels dynamically assigned to the reserved band to support services that experience disruptions when functioning on the non-reserved band. It enables priority-based channel assignment and termination while providing dynamic access to the obtainable spectrum. Here, Allocation of channels to Reserved Band CRN using Dynamic Channel Reservation Algorithm (DCRA). Komodo Mlipir Optimization Algorithm (KMOA) is employed for Service selection and Channel restoration. The proposed SE-KMOA-CRN method is modelled utilizing Continuous Time Markov Chain (CTMC) with mathematical equations for some quality of service (QoS) factors is obtained. The SE-KMOA-CRN approach attains higher Throughput, lower End-to-End Delay, higher Energy Efficiency, greater spectral efficiency and higher Packet Delivery Ratio compared with existing methods, like Multiple objective optimization for spectrum along energy efficacy trade-off in IRS-assisted CRNs using NOMA (SE-MOO-CRN), Energy efficacy in CRN utilizing cooperative spectrum sensing under hybrid spectrum handoff (SE-TOA-CRN) and Energy-effectual cross-layer spectrum sharing on CR green IoT (SE-CRG-CRN) respectively.

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks.

The aim of this systematic review was to identify the correlations between spectrum sensing, clustering algorithms, and energy-harvesting technology for cognitive-radio-based internet of things (IoT) networks in terms of deep-learning-based, nonorthogonal, multiple-access techniques. The search results and screening procedures were configured with the use of a web-based Shiny app in the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow design. AMSTAR, DistillerSR, Eppi-Reviewer, PICO Portal, Rayyan, and ROBIS were the review software systems harnessed for screening and quality assessment, while bibliometric mapping (dimensions) and layout algorithms (VOSviewer) configured data visualization and analysis. Cognitive radio is pivotal in the utilization of an adequate radio spectrum source, with spectrum sensing optimizing cognitive radio network operations, opportunistic spectrum access and sensing able to boost the efficiency of cognitive radio networks, and cooperative spectrum sharing together with simultaneous wireless information and power transfer able increase spectrum and energy efficiency in 6G wireless communication networks and across IoT devices for efficient data exchange.

An Efficient Hybrid Analysis to Improve Data Rate Signal Transmission in Cognitive Radio Networks Using Multi- Hop

Spectrum scarcity problems can be resolved with the emerging communiqué technologies known as cognitive radio (CR). Cognitive radio networks (CRNs) will give mobile users greater bandwidth via wirelessly heterogeneity design and dynamic spectrum acquisition methods. The Cognitive Radio Mobile Ad-Hoc Network (CR-MANET) idea of Adaptive Routing a new network paradigm may be realized by using the functions of spectrum management to overcome such difficulties. Secondary users (SUs) have the freedom to opportunistically explore and make use of the open spaces on licensed channels. When a primary user (PU) interferes with a licensed channel, this forces the SU to leave it and switch to an open channel. Because of the constant channel switching those results, SUs degrades as a result. In this result recommends a number of channels, number of hop CRN that uses a fuzzy decision-making system that is genetically optimized for channel selection, channel switching, and spectrum allocation. According to study, the suggested architecture achieves higher PDR, throughput, latency, and transmission time than fuzzy and genetic algorithms. Through simulations the result demonstrates significant improvements in data rate performance, making it a promising solution for enhancing communication efficiency in cognitive radio networks.

Optimization of Spectrum Utilization Efficiency in Cognitive Radio Networks

Cognitive radio (CR) is considered as a key technology to overcome the problem of spectrum scarcity for wireless applications by the researchers. Efficient spectrum utilization is the most important and core purpose of CR systems after successful spectrum sensing. In this letter, different cases are investigated in which secondary users (SUs) can transmit their data after declaring the primary user (PU) is absent as a result of spectrum sensing. SU’s transmission in some of these cases may lead to interference in PU’s communication, which may cause inefficient spectrum utilization. In this letter, an optimization problem is formulated to optimize the sensing time that maximizes the spectrum utilization efficiency (SUE) under the constraints of high target detection probability and low probability of interference to PU. A trade-off between sensing time and spectrum utilization efficiency is shown in a given time frame for all possible cases. Theoretical and simulation results of proposed system show good match to each others. Finally proposed system is compared with conventional systems and 45% better performance in terms of optimal sensing time is achieved with maximum SUE.

Spectral Efficiency Enhancement using Hybrid Pre-Coder Based Spectrum Handover Mechanism

The use of Millimeter-wave (mm-Wave) has recently immensely enhanced in various communication applications due to massive technological developments in wireless communications. Furthermore, mm-Wave consists of a high bandwidth spectrum that can handle large demands of data transmission and internet services. However, high interference is observed in previous research at the time of spectrum handover from secondary (unlicensed) users to primary (licensed) users. Thus, interference reduction by achieving high spectral efficiency and an easy spectrum handoff process with minimum delay is an important research area. Therefore, a Hybrid Pre-coder Design based Spectrum Handoff (HPDSH) Algorithm is proposed in this article to increase spectrum efficiency in Cognitive Radio Networks(CRNs) and to access the large bandwidth spectrum of mm-Wave systems to meet the high data rate demands of current cellular networks. Moreover, a HPDSH Algorithm is presented to enhance spectral efficiency and this algorithm is utilized to take handover decisions and select backup channels. Here, different scenarios and parameters are considered to evaluate the performance efficiency of the proposed HPDSH Algorithm in terms of spectral efficiency and Signal to Noise (SNR) ratio. The proposed HPDSH Algorithm is compared against precoding methods like the OMP algorithm and SIC based methods.

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.

Improved hard fusion methods for enhancing detection and energy efficiency in cognitive radio networks

AbstractCognitive radio (CR) is a smart innovation in wireless industry, to resolve the issues of spectrum scarcity and underutilization. In this article, detection performance of majority rule is improved further by modifying the minimum number of justifiable local decisions required. A weight based improved hard fusion technique is proposed to enhance the detection performance. A weight factor is provided to the sensed results of the secondary users (SUs) based on their position from primary user (PU) and signal‐to‐noise ratio (SNR) value. A pipelined frame structure is proposed for combining sensing and reporting durations of users, nearer to PU. This increases duration of data transmission. Finally, a relay assistance method is proposed. Users with low residual energy are provided relay assistance with relay users (RUs), to prevent from running out of energy. With improved Majority fusion, 86.93% increase in detection is obtained at SNR of (−10 to −1) dB for false alarm of 0.2. There is about 24.30%, 38.10%, and 79.56% increase in detection for weight based AND, OR, and majority fusion respectively at false alarm of 0.2. In addition to detection performance, energy consumption, and throughput analysis of the proposed methods are performed to determine the achieved energy efficiency (EE).

Evaluation of Primary User Power Impact for Joint Optimization of Energy Efficiency in Cognitive Radio Networks

Energy efficiency (EE) is of great concern in cognitive radio networks since the throughput and energy consumption of secondary users (SUs) vary with the sensing time. However, the conditions of the detection probability and false alarm probability should be respected to better protect primary users (PUs) and to improve the sensing performance of SUs. Additionally, the PUs’ minimum averaged power provision should also be regarded as a key problem of interactive linking to SUs. Therefore, an integrated design between the PU and SUs is desired for the coordination of the whole cognitive radio system, especially regarding the satisfaction of EE and performance metrics. This study formulates sensing constraints in a unified way and calculates the minimum SNR of SUs, based on which the essential PU power provision is computed. Furthermore, EE is proved as a decreasing function with the PU’s active ratio, where the maximum EE is obtained corresponding to the minimum QoS requirements of the sensing process. Hence, a bisection-based method is proposed to maximize EE, which is considered as a concave function of SUs’ sensing time and has only one unique optimum. EE’s optimization was analyzed under different fusion rules for diverse SNR conditions. The optimum was also studied with sensing performance targets for various cases of PU power provision.

Optimal selection based K‐mean clustering technique to improve the energy efficiency in cognitive radio networks for 6G applications

SummaryThe main objective of the cognitive radio sensor network (CRSN) is to minimize the energy consumption in channel sensing and switching when sensor nodes sense and switch to a licensed channel for improving the energy efficiency for 6G applications. The drawback in CRSN is that the sensor network uses a higher amount of energy by intrabunch and interbunch of data emission energetic medium ingression assignment problem when the sensor nodes sense the channel. In this brief, a K‐mean clustering technique (KMCT) is proposed for CRSN to minimize the energy used by the sensor nodes in CRSN for 6G applications. The KMCT‐based joint power medium ingression technique (JPMIT) is introduced for enhancing the energy efficiency in a bunch of interdata emissions, and the KMCT‐based energetic medium ingression technique (EMIT) is introduced for minimizing the energy utilization in a bunch of intradata emissions. Simulation results show that the proposed KMCT for CRSN provides a better result compared with existing techniques. The performance parameters such as medium available duration, data loss rate, quantity of data, number of authorized mediums, and probability of false alarm have been analyzed using MATLAB R2012a.

Spectrum utilization efficiency in CRNs with hybrid spectrum access and channel reservation: A comprehensive analysis under prioritized traffic

The enhancement of spectrum utilization efficiency (SUE) is a critical objective in fifth generation networks-enabled Internet of Things (5G-IoT) for providing communication opportunities to plethora of 5G-IoT devices within the already scarce spectrum. Cognitive radio networks (CRNs) with interweave and underlay modes are foreseen to achieve this objective. The interweave mode is adequate for legacy CRNs, while the underlay mode is fitting for resource-constrained CRNs. As 5G-IoT is rapidly evolving into heterogeneous networks, CRNs enabled by hybrid underlay-interweave mode (H-mode) emerge to be the best option for SUE enhancement in 5G-IoT. In this paper, we comprehensively analyze the performance of H-mode enabled CRNs containing heterogeneous users by proposing two H-mode schemes, namely, primary user full priority (FP) and primary user partial priority (PP). The schemes consider secondary users (SUs) with high and low priorities. In FP, primary users (PUs) have absolute priority in channel access while in PP, SUs with certain attributes are immune from interruptions. Additionally, to accommodate interrupted users, a simple yet efficient channel reservation algorithm is proposed, which divides the given channels into reserved and unreserved bands. Using Markov chain modeling, expressions are derived for core SUE related performance metrics including service completion rate (SCR), blocking probability (BP), network serveability (NS), successful service completion probability (SSCP), handoff probability, and throughput, independently for PUs and all types of SUs. Performance of the proposed schemes is analyzed under varying channel failure rates and network traffic loads. Efficacy of the proposed schemes is shown through analytical and simulation results against state-of-the-art. Improvements of up to 67% in BP and 83% in cost efficiency are recorded as compared to the state-of-the-art. Likewise, up to 10% and 84% difference in PUs’ and SUs’ SSCP, respectively, and up to 17% and 93% difference in PUs’ and SUs’ SCR, respectively, are noted. Similarly, up to 85% performance difference in NS is recorded. Moreover, up to 35% improvement in throughput in H-mode is witnessed. Lastly, handoff probability is found to be increased by 5%.

Secrecy Energy Efficiency in Cognitive Radio Networks With Untrusted Secondary Users

The information security and energy efficiency in cognitive radio (CR) networks have been extensively studied. However, the practical scenario involving multiple untrusted secondary users (SUs) in CR networks under the underlay spectrum sharing mechanism has not been studied so far. This article considers the downlink secrecy energy efficient coordinated beamforming design for multiple inputs single output CR networks under this scenario. Our goal is to maximize the global secrecy energy efficiency (GSEE), defined as the ratio of the sum of secrecy rates of all the primary users (PUs) to the total power consumption, under requirements on quality of service of PUs and SUs as well as constraints on power budget at the primary transmitter (PTx) and the secondary transmitter (STx). To tackle the non-convex GSEE maximization (GSEEM) problem, an algorithm is proposed based on Dinkelbach method and successive convex approximation to jointly optimize beamforming vectors of the PTx and the STx. The convergence behavior and the computational complexity of the proposed GSEEM algorithm are analyzed, followed by the connection with the secrecy rate maximization design and the power minimization (PM) design in terms of GSEE. In view of significantly higher computational complexity of the proposed GSEEM algorithm than that of the PM design, a 2-step searching scheme is further designed to efficiently search for an approximate solution to the considered GSEEM problem based on the PM design and the golden search method. Simulation results demonstrate the efficacy of the proposed GSEEM algorithm and the searching scheme, and show that the spatial degrees of freedom (primarily determined by the antenna numbers of PTx and STx) is the key factor to the performance of the proposed GSEEM algorithm.

Censoring-Based Relay Transmission for Achieving Energy Efficiency in Cognitive Radio Networks

Energy efficiency (EE) is critical to achieve cooperative sensing and transmission in relay-assisted cognitive radio networks (CRNs) with limited battery capacity. This paper proposes an energy-efficient cooperative transmission strategy with combined censoring report and spatial diversity in cooperative process, namely, censoring-based relay transmission (CRT). Specifically, secondary relays (SRs) take part in cooperative sensing with differential censoring to reduce energy consumption, and the best SR assists secondary transmission to enhance communication quality in transmission stage and thus to improve secondary transmission EE. First, we derived generalized-form expressions for detection probability, reporting probability, sensing energy, and expected throughput for CRT. Second, we investigate a mean EE-oriented maximization nonconvex problem by joint optimizing sensing duration and power allocation for secondary users under secondary outage probability and sensing performance constraints. With the aid of Jensen’s inequality, an efficient cross-iteration algorithm with low complexity is proposed to obtain the suboptimal solutions, which is developed by golden segmentation search method. Finally, extensive simulations are conducted to evaluate the performance of CRT. The results show significant improvements of SUs’ EE compared with traditional noncooperation single cognitive transmission schemes, which demonstrate the benefits of our proposed cooperative strategy in conserving energy for secondary transmission.

Spectrum efficiency in CRNs using hybrid dynamic channel reservation and enhanced dynamic spectrum access

Blocking of new arriving services and dropping of ongoing services are inherent problems in Cognitive Radio Networks (CRNs), which need to be addressed to enhance spectrum efficiency. In particular, Secondary Users (SUs) undergo service degradation in the face of Primary Users (PUs)’ arrivals. In this paper, we present a scheme called Efficient Spectrum Utilization (ESU) that reduces the dropping and blocking probabilities of existing and new services, respectively, to make efficient use of the available spectrum. The scheme divides the available spectrum into reserved and non-reserved bands. The reserved band is dynamically allocated a number of channels from the non-reserved band in order to accommodate those services which face interruptions while operating in the non-reserved band. The scheme renders dynamic access to the available spectrum and facilitates priority-based channel allocation and termination. SUs are divided into low and high priority levels depending on their Quality of Service (QoS) requirements. SUs with low priority level are granted direct access to both the bands to enhance channel utilization. SUs operating in the reserved band with high priority levels are granted uninterruptible status to ensure a certain level of service provisioning to SUs. The proposed ESU scheme is modeled using Continuous Time Markov Chain (CTMC) and mathematical expressions are derived for several QoS parameters. Performance of the proposed scheme is evaluated under various network conditions. Results demonstrate that ESU reasonably improves spectrum efficiency under channel failure in CRNs.

Efficient energy harvesting scheme with power optimization strategies over cognitive radio networks

A wide growth of wireless communication and it's associated technologies innovates many innovative and robust systems. These kinds of innovations over wireless technologies highly depend on energy efficiency and power saving procedures over communication scenarios. These wirless technologies update its nature day-by-day based on the user needs, so a dynamic wireless communication medium is essential to tolerate and resolve this kind of robust needs. Cognitive Radio Networks, an intelligent dynamic programming scheme as well as which has powerful network configurations to choose the efficient wireless network channel for proceeding the communications without any interruptions. This kind of dynamicity and robustness requires more energy to manipulate the sensor node and routing details as well as their presence in every time to check the node availability for further communications. In this paper, we concentrate more on energy harvesting issues and power optimization procedures occurred over Cognitive Radio Networks. In this proposed system, the energy efficiency is preserved and power optimization over CR Networks is handled by means of three intelligent factors/strategies such as: (i) Efficient Route Reporter (ERR), (ii) Dynamic Channel Optimizer (DCO) and (iii) Power Efficiency Handler (PEH). These three strategies are collectively called as Dynamic Optimization Law over CR Networks (DOL-CRNs). By using these DOL-CRNs, we can easily manipulate the issues and provide best resolution to optimize the CR Networks to enhance the network lifetime, throughput, reduce the delay over CR Networks communication and maximize the frame success ratio. For all the proposed DOL-CRNs assures the efficiency of Cognitive Radio Networks as well as its communication robustness with the help of three associated strategies.

Energy Efficiency of Secure Cognitive Radio Networks with Cooperative Spectrum Sharing

Energy-efficient and secure wireless communications have recently earned tremendous interests due to economic, environmental, and military concerns. This paper investigates the tradeoff between the secrecy throughput and the energy efficiency in cognitive radio networks (CRNs), where primary and secondary users with different priorities of spectrum access can either interfere or cooperate with each other. To gain an understanding of the intricate effects that system parameters have on underlay network's performance, we exclusively focus on characterizing several key aspects that may have potential impacts on secure underlay CRNs, including the transmission power, the number of interfering users, and the designed interference resistance coefficient. Based on the obtained analytical results, we further propose a cooperative spectrum sharing paradigm to improve both the secrecy throughput and the energy efficiency of primary users. The main idea is that primary users allow secondary users to simultaneously access the licensed spectrum and in return, the secondary transmitter acts as both a relay for primary transmissions and a friendly jammer against eavesdropping, in case the primary transmission fails. Both theoretical and numerical results reveal that: (i) When the interference from secondary transmitters is small, there is an optimal transmission power that maximizes the secrecy throughput for primary users compared to CRNs without the security issue. (ii) When the interference from secondary transmitters is large, the secrecy throughput increases with the transmission power for primary users. (iii) The transmission power that maximizes the energy efficiency is smaller than that maximizes the secrecy throughput for primary users. (iv) The number of interfering users has a slight impact on the secrecy throughput and the energy efficiency of primary users due to the secondary power control. (v) The proposed cooperative paradigm is an efficient approach to boost both the secrecy throughput and the energy efficiency of primary users compared with the traditional non-cooperative spectrum sharing, and provides an alternative method to compensate for the interference caused by secondary users.

A Stackelberg game for relay selection and power allocation in an active cooperative model involving primary users and secondary users

SummaryIn recent years, cooperative communications have been widely used to improve the spectrum efficiency in cognitive radio networks. In this paper, we propose a new cooperative model involving primary and secondary users, where a primary transmitter may select a number of the secondary users to act as relays in order to maximize its data rate and to transmit at lower energy level, thereby saving energy and reducing interference at the secondary base station. The cooperative transmission is a multiple two‐hop relaying scheme, which guarantees an achievable data rate exceeding that in the direct transmission. In the proposed approach, the problem of joint relay selection and power allocation is formulated as a Stackelberg game, which converges to a unique optimal Nash equilibrium. Performance evaluation shows that this model offers benefit to both sides, where the primary users achieve higher data rate at lower energy consumption and the signal to interference plus noise ratio at the secondary base station is increased significantly. In addition, the results show that the proposed solution outperforms the investigated models in terms of achievable data rate.

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.

Using Massive MIMO and Small Cells to Deliver a Better Energy-Efficient Cognitive Radio Network

Energy efficiency in cognitive radio networks is gradually receiving a lot of significant attention lately due to the gradual increase in the energy consumption of the network. In order to enable a more efficient energy network and to facilitate higher spectral utilization, adequate measures need to be employed to foster and improve energy efficiency. In this work, a massive multi-input and multi-output (MIMO) antenna base station co-existing with small cells base stations are introduced to efficiently optimize the energy in cognitive radio networks. An energy efficiency optimization problem was formulated and Dinkelbach method is used to solve the problem iteratively. Our simulation results show that the inclusion of more antennas in the macro base station can significantly reduce the total power consumed in the network and also that the energy efficiency of the network can be greatly improved when an optimal base station is selected for transmission.

How to Increase Energy Efficiency in Cognitive Radio Networks

In this paper, we investigate the achievable energy efficiency of cognitive radio networks where two main modes are of interest, namely, spectrum sharing (known as underlay paradigm) and spectrum sensing (or interweave paradigm). In order to improve the energy efficiency, we formulate a new multiobjective optimization problem that jointly maximizes the ergodic capacity and minimizes the average transmission power of the secondary user network while limiting the average interference power imposed on the primary user receiver. The multiobjective optimization will be solved by first transferring it into a single objective problem (SOP), namely, a power minimization problem, by using the $\varepsilon$ -constraint method. The formulated SOP will be solved using two different methods. Specifically, the minimum power allocation at the secondary transmitter in a spectrum sharing fading environment are obtained using the iterative search-based solution and augmented Lagrangian approach for single and multiple secondary links, respectively. The significance of having extra side information and also imperfect side information of cross channels at the secondary transmitter are investigated. The minimum power allocations under perfect and imperfect sensing schemes in interweave cognitive radio networks are also found. Our numerical results provide guidelines for the design of future cognitive radio networks.

The Impact of Network Size and Mobility on Information Delivery in Cognitive Radio Networks

There have been extensive works on the design of opportunistic spectrum access and routing schemes to improve spectrum efficiency in cognitive radio networks (CRNs), which becomes an integral component in the future communication regime. Nonetheless, the potentials of CRNs in boosting network performance yet remain to be explored to reach the full benefits of such a phenomenal technique. In this paper, we study the end-to-end latency in CRNs in order to find the sufficient and necessary conditions for real-time applications in finite networks and large-scale deployments. We first provide a general mobility framework which captures most characteristics of the existing mobility models and takes spatial heterogeneity into account. Under this general mobility framework, secondary users are mobile with an mobility radius $\alpha$ , which indicates how far a mobile node can reach in spatial domain. We find that there exists a cutoff point on $\alpha$ , below which the latency has a heavy tail and above which the tail of the latency is bounded by some Gamma distributions. As the network grows large, the latency is asymptotically scalable (linear) with respect to the dissemination distance (e.g., the number of hops or euclidean distance). An interesting observation is that although the density of primary users adversely impacts the expected latency, it makes no influence on the dichotomy of the latency tail in finite networks and the linearity of latency in large networks. Our results encourage CRN deployment for real-time and large applications, when the mobility radius of secondary users is large enough.