Throughput Of Cognitive Radio Network Research Articles

Variable rate power-controlled batch-based channel assignment for enhanced throughput in cognitive radio networks

The number of users in wireless networks, such as mobile and Internet-of-Things networks, is witnessing a tremendous increase, turning the available frequency spectrum into a scarce resource that needs to be efficiently utilized. Cognitive radio (CR) is a key technology for achieving spectrum efficiency by continuously sensing and detecting frequency bands that are not used by licensed primary users (PU) and allowing unlicensed secondary users (SUs) to use them. One of the main challenges in CR is the design of a medium access control (MAC) protocol that ensures efficient spectrum sharing by SUs without disrupting the connectivity of PUs. To achieve that, many of the existing MAC protocols in the literature allow multiple SU transmissions to proceed simultaneously by performing batch-based power control decisions to limit mutual interference between them. Interestingly, the majority of such protocols are demand-rate unaware; i.e., all SUs are granted the same data rate, regardless of their data rate demand. In this paper, we highlight the severe drawbacks of demand-rate unawareness and propose the rate-aware power-controlled channel assignment (RPCCA) MAC protocol, which performs batch-based simultaneous channel assignment decisions to competing SUs along with power control to limit mutual interference, while taking into account the variable demand-rate across SUs. Simulation experiments have demonstrated that the RPCCA protocol offers substantial performance improvements over existing demand-rate unaware CR-based MAC protocols.

Throughput‐delay tradeoff for opportunistic spectrum access in cognitive radio networks

AbstractThe internet of things accelerates the wireless connections of massive devices to provide all kinds of new services, and thus intensifies the spectrum scarcity and access delay. Cognitive radio (CR) technology brings a solution for the issue. As a typical CR scheme, opportunistic spectrum access (OSA) has been addressed widely in the past decade. However, the tradeoff between two key indexes, the throughput of cognitive radio network (CRN) and the delay of secondary user (SU), is rarely mentioned so far, which ignites the authors’ work in this paper. Taking into account a channel handoff (CH) based multi‐channel OSA scenario, the authors first analyze the opportunistic transmission performance of SU, and model the throughput of CRN as well as the handoff delay of SU. Then, the authors build up a delay‐constraint throughput optimization problem, and thus formulate the throughput‐delay tradeoff for OSA. Finally, the optimal traffic rates of SU for a good throughput‐delay tradeoff are derived according to maximizing the throughput of CRN. Theoretical and simulated results show that to enhance the throughput and to reduce the delay do not conflict always. By well adjusting the traffic rates of SU according to the traffic rates of primary user, the throughput of CRN can be improved while the handoff delay can be kept under a given level.

Correction: A Design of Minimizing Interference and Maximizing Throughput in Cognitive Radio Network by Joint Optimization of the Channel Allocation and Power Control

Correction: A Design of Minimizing Interference and Maximizing Throughput in Cognitive Radio Network by Joint Optimization of the Channel Allocation and Power Control

A Design of Minimizing Interference and Maximizing Throughput in Cognitive Radio Network by Joint Optimization of the Channel Allocation and Power Control

A Design of Minimizing Interference and Maximizing Throughput in Cognitive Radio Network by Joint Optimization of the Channel Allocation and Power Control

A novel spectrum handoff switching decision scheme for improved performance of secondary users in cognitive radio network

SummarySpectrum handoff (SH) in the cognitive radio network (CRN) is considered as a key challenging area to enhance the performance of secondary users (SUs) in CRN. If the primary user is detected, the SU may pause and stay on the same channel or may perform SH to another idle channel. An accurate and precise handoff decision improves the overall throughput and quality of experience of end‐users. In this paper, we introduce a new SH algorithm and continuous short‐sensing strategy to improve the overall throughput of SUs. In addition, we have derived the minimum length of the target channel sequence based on network‐specific parameters like desired call dropping probability. Further, an optimum channel search time is obtained to minimize the handoff delay. The simulation result shows that the proposed scheme improves the overall throughput of CRN, and the mean opinion score of different video applications increases by 10%, 4.6%, and 1% for rapid motion, gentle walk, and slight motion types of video applications. In the case of VoIP applications, the maximum simultaneous call is improved by 2 times in the case of G.711, 1.72 times in the case of G.729, and 1.66 times in the case of iLBC.

The maximal secondary throughput in cognitive radio networks with energy harvesting

In terms of spectrum reuse and lifetime prolongation, the energy-harvesting cognitive radio networks (EH-CRNs) have been a hot issue in the wireless networking research community. While satisfying the minimal throughput demand of primary users (PUs), it is aimed to maximize the throughput of secondary users (SUs) in the EH-CRN with multiple SUs. Specifically, the problem of secondary throughput maximization (STM) is first formulated as a non-linear optimization problem, then its convexity is proven, and finally an efficient algorithm is proposed that jointly uses the Fibonacci search method and equal interval search method to obtain the optimal time allocation among primary transmitter (PT)'s energy transfer and each SU's packet transmission, and the optimal transmit power of PT. Furthermore, for the scenarios where the circuit power is negligible, the convex problem is first proven, and a more efficient algorithm is presented for the problem of STM. Simulation results demonstrate that, with the increase of the minimal throughput demand of PUs, the reduction rate of the maximal secondary throughput increases.

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.

Energy Efficient and Interference-aware Spectrum Sensing Technique for Improving the Throughput in Cognitive Radio Networks

Cognitive Radio network (CRN) depends on opportunistic spectrum access and spectrum sensing for improving the wireless networks’ spectrum efficiency. Since throughput maximization can result in high energy consumption, the spectrum sensing technique should address the energy-throughput trade off. The spectrum sensing time has to be determined by the considering the residual battery energies of each secondary user (SU). The Primary User (PU) interference degrades the throughput of the entire network. Hence the transmit power level should be determined considering the PU interference and SU battery energy. This paper proposes Energy Efficient and Interference-aware Spectrum Sensing (EEISS) technique for improving the throughput in CRN. In this work, the sensing time is dynamically estimated based on the battery energy levels of SUs and the transmit power is determined depending on PU signal and battery energy levels of SUs. A Game theory model is formulated to maximize the throughput based on these constraints. Experimental results show that EEISS attains improved throughput, higher probability of detection and higher residual energy.

Dynamic Threshold Based Throughput Enhancement in Cognitive Radio Network Using Hidden Markov Model with State Prediction

The enhancement of throughput is one of the key issues in the cognitive radio network. In this paper, a channel status prediction scheme based on hidden Markov model is proposed for enhancing the throughput of cognitive radio network. Unlike the conventional scheme which relies on channel sensing alone, the proposed scheme provides an additional advantage of being able to predict the primary user’s state along with channel sensing, thus increasing spectrum utilization and system throughput. For achieving enhanced reliability in spectrum sensing process of the network, a dynamic threshold based energy detection technique considering noise uncertainty and target detection probabilities is proposed. Comparative analyses of the performance of the dynamic threshold based energy detection technique with that of existing fixed and dynamic threshold based detection schemes are presented. The analyses reveal that the proposed detection scheme performs better than the existing detection schemes with regard to probability of detection and probability of false alarm. It is shown that the hidden Markov model-based prediction scheme makes the cognitive radio network more efficient in terms of throughput than existing schemes.

Throughput enhancement of cognitive radio networks‐nonorthogonal multiple access with energy harvesting and adaptive transmit power

AbstractIn this article, we derive the theoretical throughput of cognitive radio networks using nonorthogonal multiple access with energy harvesting nodes. We also optimize both power allocation coefficients and harvesting duration to maximize the throughput. In the primary network, there are a primary source and a primary destination. The secondary network contains a secondary source, K relays Rk, and N nonorthogonal multiple access users Di. Secondary source and relays harvest energy from radio frequency signals received from node A. Secondary nodes adapt their powers to generate low interference at primary destination. We also suggest two strategies for user classification using average or instantaneous channel gains while taking into account the interference from primary source. We show that users' ranking using instantaneous channel gains offers the largest throughput. In addition, the suggested optimal power allocation and optimal harvesting duration allow to increase the throughput. Our theoretical derivations are also compared with computer simulations performed in the presence of Rayleigh fadingchannels.

Performance optimisation of cooperative spectrum sensing in mobile cognitive radio networks

Cooperative spectrum sensing is a key technology of cognitive radio networks (CRNs) to the reliability of spectrum sensing but is prone to be affected by a series of user characteristics, which results in a serious decline in the throughput of CRNs and the harmful interference to the primary user network. The performance analysis and optimisation of existing cooperative spectrum-sensing schemes do not completely cover key user characteristics. In this study, the joint effects of key user characteristics are studied with the objective to determine the parameters that affect the cooperative spectrum-sensing functionality. To this aim, the mobile CRN model and spatial–temporal spectrum-sensing model are formulated. Furthermore, the authors propose a dynamic double threshold energy detection (DDTED) scheme to derive the miss-detection probability and false alarm probability involving user characteristics. Moreover, the performance optimisation is carried out by a dynamic threshold factor. Finally, simulation results show that a variety of user characteristics have different effects on the performance of cooperative spectrum sensing, and the proposed DDTED scheme can achieve better performance than the existing approach.

Session based cooperation in cognitive radio networks: network level approach

&lt;p&gt;The cognitive radio prototype performance is to alleviate the scarcity of spectral resources for wireless communication through intelligent sensing and quick resource allocation techniques. Secondary users (SU’s) actively obtain the spectrum access opportunity by supporting primary users (PU’s) in cognitive radio networks (CRNs). In present generation, spectrum access is endowed through cooperative communication based link-level frame-based cooperative (LLC) principle. In this SUs independently act as conveyors for PUs to achieve spectrum access opportunities. Unfortunately, this LLC approach cannot fully exploit spectrum access opportunities to enhance the throughput of CRNs and fails to motivate PUs to join the spectrum sharing processes. Therefore to overcome this con, network level cooperative (NLC) principle was used, where SUs are integrated mutually to collaborate with PUs session by session, instead of frame based cooperation for spectrum access opportunities. NLC approach has justified the challenges facing in LLC approach. In this paper we make a survey of some models that have been proposed to tackle the problem of LLC. We show the relevant aspects of each model, in order to characterize the parameters that we should take in account to achieve a spectrum access opportunity.&lt;/p&gt;

Scalable Learning-Based Heterogeneous Multi-Band Multi-User Cooperative Spectrum Sensing for Distributed IoT Systems

The emerge of Internet of Things (IoT) brings up revolutionary changes to wireless communications. Cognitive radio (CR) can be seen as one of the prominent solutions to spectrum scarcity in IoT, where multi-band cooperative spectrum sensing (CSS) is the key. However, lack of centralized control and increase in number of devices place a room for many challenges. One of the main challenges is secondary users' (SUs') scheduling to sense a subset of channels in heterogeneous distributed CR networks (CRNs). To overcome the aforementioned challenge, in this paper, we propose a novel heterogeneous multi-band multi-user CSS (HM2CSS) scheme. The proposed scheme allows heterogeneous SUs to sense multiple channels and consists of two stages. We formulate a mathematical model to optimize leader-selection for each channel in the first stage. We then formulate another optimization problem to determine corresponding cooperative SUs to sense these channels in the second stage. After that, diffusion learning is used to decide on the availability of channels. Simulations illustrate that the proposed scheme improves detection performance and CRN throughput, is scalable in terms of detection performance, and provides fair energy consumption for CSS on all channels compared to existing multi-band CSS schemes.

A Trust Framework to Detect Malicious Nodes in Cognitive Radio Networks

Cognitive radio is considered as a pioneering technique in the domain of wireless communication as it enables and permits the Cognitive Users (CU) to exploit the unused channels of the Primary Users (PU) for communication and networking. The CU nodes access the vacant bands/channels through the Cognitive Radio Network (CRN) cycle by executing its different phases, which are comprised of sensing, decision making, sharing (accessing) and hand-off (mobility). Among these phases, hand-off is the most critical phase as the CU needs to switch its current data transmissions to another available channel by recalling all the previous functions upon the emergence of a PU. Further, from the security perspective, a Malicious User (MU) may imitate the PU signal with the intention to never allow the CU to use its idle band, which ultimately degrades the overall network performance. Attacks such as the Cognitive User Emulation Attack (CUEA) and Primary User Emulation Attack (PUEA) may be encountered by the handoff procedure, which need to be resolved. To address this issue, a secure and trusted routing and handoff mechanism is proposed specifically for the CRN environment, where malicious devices are identified at the lower layers, thus prohibiting them from being part of the communication network. Further, at the network layer, users need to secure their data that are transmitted through various intermediate nodes. To ensure a secure handoff and routing mechanism, a Trust Analyser (TA) is introduced between the CU nodes and network layer. The TA maintains the record of all the communicating nodes at the network layer while also computing the rating and trust value of the Handoff Cognitive User (HCUs) using the Social Impact Theory Optimizer (SITO). The simulation results suggest that the proposed solution leads to 88% efficiency in terms of better throughput of CRN during data communication, the packet loss ratio, the packet delivery ratio and the maximum and average authentication delay and clearly outperforms the prevailing mechanisms in all the parameters.

Proposed Two-Stage Detection Rules for Improving Throughput in Cognitive Radio Networks

Cognitive Radio (CR) is a communication technology developed to solve the problem of spectrum scarcity. One way to improve the throughput of CR is the use of efficient decision rules of the fusion center to combine the collected information from cooperative sensors and produce the right final decision. For this purpose, hard decision rules like AND and OR and soft decision rules like Square Law Combination (SLC) and Maximum Ratio Combination (MRC) can be combined to optimize the throughput performance. In this paper, three decision rules, each consist of two decision stages (hard and soft) are proposed to improve the throughput of CR in cooperative scenario. The simulation results showed that the proposed rules enhance the throughput as compared with traditional ones. They demonstrated that the first proposed rule enhances the throughput by 106% and 58.9% at SNR equals -10 dB in Rayleigh fading channel over the classical OR-SLC and the AND-SLC rules, respectively. Under the same simulation conditions, the second proposed rule enhanced the throughput by 163% and 97.5%, while the third proposed method enhances throughput by 210% and 135%, respectively.

A Method to Enhance the Throughput of Cognitive Radio Network Using Kullback Leibler Divergence with Optimized Sensing Time (KLDOST)

Efficient, quick and reliable spectrum sensing in consonance with maximum throughput is required at the cognitive radios to achieve maximum utilization of the network. In cognitive radio environment, the intelligent secondary users shall effectively sense the presence of primary user signal to avoid interference, in order to achieve maximum operational feasibility. A cooperative sensing scheme using Kullback Leibler divergence with higher throughput and lower sensing time to avail and identify the underutilized frequency spectrum is presented in this paper. The optimized sensing scheme utilizes the log-likelihood ratio estimated at the secondary user during each sensing instance, with fusion centre acquiring and assimilating this information to estimate the likelihood of spectrum availability. The optimized scheme is implemented in a robust manner assuming the signal and noise are gaussian distributed. Simulation results highlighting the competitive edge of the proposed scheme, with higher throughput over various false alarm probability and signal to noise ratio are presented.

A Switching-Based and Delay-Aware Scheduling Algorithm for Cognitive Radio Networks

In this article, an opportunistic inter-frame spectrum scheduler that maximizes the throughput of cognitive radio networks over a span of multiple time-slots is proposed. An optimization problem is formulated to find the optimum inter-frame scheduler while taking into account the switching delay, the primary user (PU) activity, historical information on the PU behavior, the channel quality as well as the secondary user (SU) status. Simulation results show that the proposed inter-frame scheduler significantly improved the overall aggregate throughput and average switching delay of the cognitive radio network when compared to the values obtained when scheduling is done on a slot-by-slot basis.

Energy harvesting with adaptive transmit power for cognitive radio networks

This article derives the packet error probability (PEP) of different relay selection techniques for cognitive radio networks (CRN). The power of secondary source and relays is adaptive to not cause high interference to primary receiver $$P_R$$. Secondary nodes harvest energy from radio frequency signals to be able to communicate. CRN with secondary nodes transmitting with adaptive power was already studied in the literature. However, in all previous studies, secondary nodes use their own batteries to transmit. The main motivation of our paper is to study CRN with energy harvesting and adaptive transmit power (ATP). We derive new expressions of PEP and throughput for CRN with energy harvesting and ATP. Our results are valid for opportunistic relaying, partial relay selection and reactive relay selection. Our main contribution is to optimize harvesting duration to maximize the system throughput.

Hybrid Fuzzy Logic Scheme for Efficient Channel Utilization in Cognitive Radio Networks

The proliferation of mobile devices and the heterogeneous environment of wireless communications have increased the need for additional spectrum for data transmission. It is not possible to altogether allocate a new band to all networks, which is why fully efficient use of the already available spectrum is the demand of the day. Cognitive radio (CR) technology is a promising solution for efficient spectrum utilization, where CR devices, or secondary users (SUs), can opportunistically exploit white spaces available in the licensed channels. SUs have to immediately vacate the licensed channel and switch to another available channel when they detect the arrival of the incumbent primary user. However, performance for the SU severely degrades if successive channel switching happens. Moreover, taking the channel-switching decisions based on crisp logic is not a suitable approach in the brain-empowered CR networks (CRNs) where sensing information is not only imprecise and inaccurate but also involves a major uncertainty factor. In this paper, we propose a fuzzy logic-based decision support system (FLB-DSS) that jointly deals with channel selection and channel switching to enhance the overall throughput of CRNs. The proposed scheme reduces the SU channel switching rate and makes channel selection more adaptable. The performance of the proposed scheme is evaluated using a Matlab simulator, and a comprehensive comparison study with a baseline scheme is presented. The simulation results are promising in terms of the throughput and the number of handoffs and making our proposed FLB-DSS a good candidate mechanism for SUs while making judicious decisions in the CR environment.

Multihop multibranch relaying with energy harvesting and adaptive transmit power for cognitive radio networks

This paper provides the packet error probability (PEP) and throughput of cognitive radio networks (CRN) with energy harvesting (EH) and adaptive transmit power (ATP). Multihop multibranch relaying is considered and only the best amplify and forward (AF) branch is activated. Our analysis is valid forEHsystem using harvested energy from radio frequency (RF) signals. The powers of secondary nodes are adaptive so that interference to primary receiver PR is lower than interference threshold I. We also choose the harvesting duration to enhanced the end-to-end (e2e) throughput.