Algorithm For Cognitive Radio Networks Research Articles

Implementation of Enhanced Chimp Optimization Algorithm in Cognitive Radio Networks for Vehicular Mobile Communication

In recent years, 6G technology has been extended to many different applications, especially mobile communications. As a result, the volume of mobile data increases, which poses a problem with the load on the plane of control (IoE, IoT). This problem is solved by efficient use of resources and reduced power consumption in cognitive radio networks (CRNs). In the literature, many methods have been developed by researchers to control spectrum sensing as well as energy -efficient operation, but they still need to be improved to improve system efficiency and processing power. Therefore, in this paper, an energy efficient method for Opposition Function -based Chimpanzee Optimization Algorithm (OFCOA) is developed in CRN for energy management as well as resource allocation. The proposed method is a combination of Opposition Function (OF) and Chimpanzee Optimization Algorithm (COA). In COAs, the optimal decision process is enhanced by the use of OF. The proposed method provides energy efficient operation in CRN through energy management taking into account spectrum measurements. The proposed method was tested under four Primary User (PU) and Secondary User (SU) conditions with channel occupation and CRN findings. The proposed methodology is implemented in MATLAB and performance is measured based on performance metrics such as processing power, network life, transmission rate, delay, flush, power consumption and overhead. The performance of the proposed methodology is compared with traditional methods such as Chimpanzee Optimization Algorithm (COA), Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO).

Improved Random Forest Algorithm for Cognitive Radio Networks' Distributed Channel and Resource Allocation Performance

Improved Random Forest Algorithm for Cognitive Radio Networks' Distributed Channel and Resource Allocation Performance

Detection of Primary User Emulation Attack Using the Differential Evolution Algorithm in Cognitive Radio Networks

Cognitive Radio Network (CRN) is an emerging technology used to solve spectrum shortage problems in wireless communications. In CRN, unlicensed secondary users (SUs) and licensed primary users (PUs) use spectrum resources at the same time by avoiding any interference from SUs. However, the spectrum sensing process in CRN is often disturbed by a security issue known as the Primary User Emulation Attack (PUEA). PUEA is one of the main security issues that disrupt the whole activity of CRN. The attacker transmits false information to interrupt the spectrum sensing process of CRN, which leads to poor usage of the spectrum. The proposed study uses a proficient Time Difference of Arrival (TDOA) based localization method using the Differential Evolution (DE) algorithm to identify the PUEA in CRNs. The DE algorithm is used to solve the objective function of TDOA values. The proposed methodology constructs a CRN and identifies PUEA. The proposed method aims to sense and localize PUEA efficiently. Mean Square Error (MSE) is the performance evaluation parameter that is used to measure the accuracy of the proposed technique. The results are compared with the previously proposed Firefly optimization algorithm (FA). It is clear from the results that DE converges faster than FA.

A Robust Power Allocation Algorithm for Cognitive Radio Networks Based on Hybrid PSO.

The use of a cognitive radio power allocation algorithm is an effective method to improve spectral utilization. However, there are three problems with traditional cognitive radio power allocation algorithms: (1) based on the ideal channel model analysis, channel fluctuation is not considered; (2) they do not consider fairness among cognitive users; and (3) some algorithms are complex and locating the optimal power allocation scheme is not an easy task. For the above problems, this study establishes a robust model which adds the cognitive user transmission rate variance constraint to solve the maximum channel capacity time power allocation scheme by considering the worst-case channel transmission model, and finally solves this complex non-convex optimization problem by using the hybrid particle swarm algorithm. Simulation results show that the algorithm has good robustness, improves the fairness among the cognitive users, makes full use of the channel resources under the constraints, and has a simple algorithm, fast convergence, and good optimization results.

Energy-efficient and intelligent cooperative spectrum sensing algorithm in cognitive radio networks

Green communication is the demand of current and future wireless communication. As the next-generation communication network, cognitive radio network also needs to meet the requirements of green communication. Therefore, improving energy efficiency is an inevitable requirement for the development of cognitive radio networks. However, there is a need to compromise sensing performance while improving energy efficiency. To take into account the two important indicators of sensing performance and energy efficiency, a grouping algorithm is proposed in this article, which can effectively improve the energy efficiency while improving the spectrum sensing performance. The algorithm obtains the initial value of the reliability of the nodes through training, and sorts them according to the highest reliability value, then selects an even number of nodes with the highest reliability value, and divides the selected nodes into two groups, and the two groups of nodes take turns in Alternate work. At this time, other nodes not participating in cooperative spectrum sensing are in a silent state, waiting for the instruction of the fusion center. The experimental results show that compared with the traditional algorithm, the proposed algorithm has a great improvement in the two indicators of sensing performance and energy efficiency.

USRP Implementation of Spectrum Sensing OFDM-Based Cognitive Radio Networks Using Energy Ratio Algorithm

This paper explains the Universal Software Radio Peripheral (USRP) Experiment results of Spectrum Sensing Algorithms based on the Energy Ration Algorithm for Cognitive Radio Networks which is latterly suggested in Spectrum observation for OFDM-Based Cognitive Radio Networks by using Energy Ratio Algorithm. This is completed through detecting the variance in the strength of the signal during a variety of confined OFDM subcarriers are used to ensure that the availability of the essential user is facilely discovered. Extensive experiments are performed, in particular, the effects of Signal to Noise Ratio (SNR). This paper observed that the experimental results gave lower detection performance compared to the simulation results. That’s due to existence of other systems which operate on same frequency band of 2.4GHz.

Optimal Relay Node Selection Using Multi-Objective based Pity Beetle Optimization Algorithm for Cognitive Radio Networks

Optimal Relay Node Selection Using Multi-Objective based Pity Beetle Optimization Algorithm for Cognitive Radio Networks

An Improved Scheduling Algorithm for Cognitive Radio Networks under Different Traffic Patterns

In this paper we propose a spectrum sharing scheduling algorithm for an infrastructure based cognitive radio network. The problem is formulated to allocate channel to the secondary user (SU) based on the idle frequency band, distance, power, bit error rate and SINR. Two scheduling algorithms namely tournament scheduling (TMS) and optimized back–tracking scheduling (OBS) has been developed for the scenario where few primary and many secondary exist in a typical cognitive radio environment. The OBS and TMS algorithm has been implemented and simulated under different traffic patterns namely, Poisson and Pareto over constant bit rate (CBR), variable bit rate (VBR) and best effort services. The simulation result shows a considerable reduction in execution time for the OBS method under varying traffic load.

A survey on Rendezvous Algorithms in Cognitive Radio Networks Under Jamming Attacks

The problem of congestion in the licensed radio channels spectrum can be solved by Cognitive Radio Networks (CRN). Several algorithms exist to ensure the rendezvous between Secondary Users (SUs), they are increasingly efficient, allowing faster rendezvous under multiple scenarios. In parallel, several jamming algorithms are developed to counter rendezvous which are also improving. The goal in CRN is to ensure the rendezvous by warding such jammers with robust algorithms. In this paper, we classify various jamming techniques and analyze the performance of various well-known rendezvous algorithms under jamming attacks.

A Wideband 5G Cyclostationary Spectrum Sensing Method by Kernel Least Mean Square Algorithm for Cognitive Radio Networks

This brief proposes a new cyclostationary method on the basis of the Gaussian Kernel Least Mean Square (KLMS) algorithm for wideband spectrum sensing in the upcoming 5G mobile networks. To focus on a special fragment of the 5G network frequency spectrum, the KLMS algorithm uses a set of inaccurate cyclic frequencies of low complexity belonging to the signal of the primary user (PU). A suboptimal detector is designed on the basis of the KLMS weights and the performance of the proposed detector is measured and compared with other recent spectrum sensing techniques. The method is very easy to implement and less complex to compute than other spectrum-sensing counterparts. Analytical and simulation results have been verified by an experimental 5G communication setup which shows the effectiveness of the proposed method.

Multi-objective optimization of cognitive radio networks

New generation networks, based on Cognitive Radio technology, allow dynamic allocation of the spectrum, alleviating spectrum scarcity. These networks also have a resilient potential for dynamic operation for energy saving. In this paper, we present a novel wireless network optimization algorithm for cognitive radio networks based on a cloud sharing-decision mechanism. Three Key Performance Indicators (KPIs) were optimized: spectrum usage, power consumption, and exposure. For a realistic suburban scenario in Ghent city, Belgium, we determine the optimal trade-off between the KPIs. Compared to a traditional Cognitive Radio network design, our optimization algorithm for the cloud-based architecture reduced the network power consumption by 27.5%, the average global exposure by 34.3%, and spectrum usage by 34.5% at the same time. Even for the worst-case optimization (worst achieved result of a single KPI), our solution performs better than the traditional architecture by 4.8% in terms of network power consumption, 7.3% in terms of spectrum usage, and 4.3% in terms of global exposure.

Spectrum Sensing using NMLMF algorithm in Cognitive Radio Networks for Health Care Monitoring Applications

Spectrum Sensing using NMLMF algorithm in Cognitive Radio Networks for Health Care Monitoring Applications

A new fast k‐nearest neighbor classification algorithm in cognitive radio networks based on parallel computing

SummaryThe field of telecommunication has undergone a very rapid technological evolution, which has forced researchers to find techniques that allow better exploitation of hardware and software. Among the proposed technologies, cognitive radio, a concept that was designed after several technologies such as software radio. Cognitive radio has been widely used for opportunistic access of the shared spectrum and has defined the cognitive nodes by their ability to intelligently adapt the environment to achieve specific objectives through advanced techniques. In this context, clustering techniques were adopted in cognitive radio networks (CRNs) due to their great advantages especially for routing. In this article, we propose a parallel mode of the k‐NN algorithm. The aim is to make a fast assignment of radio nodes in CRNs organized in the form of clusters. The obtained results are very satisfactory because we have been able to reduce to about 50% the execution time of the basic algorithm (sequential).

Optimal spectrum and power allocation using evolutionary algorithms for cognitive radio networks

Cognitive radio is a promising technology for efficient spectrum utilization. It explores dynamic spectrum access features while satisfying interference constraints. In this work, a joint power and spectrum allocation algorithm is proposed to maximize the cognitive network throughput while satisfying interference constraints of both primary and secondary users in the network. Evolutionary algorithms are used to solve the joint power and spectrum allocation problem. Furthermore, the algorithmic performance is compared in terms of quality of solution. And also we optimized the maximum utilization of the network and capacity of each user simultaneously by using Multi‐Objective Differential Evolution (MODE) and Nondominated Sorting Genetic Algorithm II (NSGA‐II). Simulation results show that the pareto optimal fronts provide the trade‐off solutions between total network utilization and individual sum capacity of user.

Robust Spectrum Sensing via Double-Sided Neighbor Distance Based on Genetic Algorithm in Cognitive Radio Networks

In cognitive radio networks (CRNs), secondary users (SUs) can access vacant spectrum licensed to a primary user (PU). Therefore, accurate and timely spectrum sensing is vital for efficient utilization of available spectrum. The sensing result at each SU is unauthentic due to fading, shadowing, and receiver uncertainty problems. Cooperative spectrum sensing (CSS) provides a solution to these problems. In CSS, false sensing reports at the fusion center (FC) received from malicious users (MUs) drastically degrade the performance of cooperation in PU detection. In this paper, we propose a robust spectrum sensing scheme to minimize the effects of false sensing reports by MUs. The proposed scheme focuses on double-sided neighbor distance (DSND) based on genetic algorithm (GA) in order to filter out the MU sensing reports in CSS. The simulation results show that the sensing results are more accurate and reliable for the proposed GA majority-voting hard decision fusion (GAMV-HDF) and GA weighted soft decision fusion (GAW-SDF) compared to conventional equal gain combination soft decision fusion (EGC-SDF), maximum gain combination soft decision fusion (MGC-SDF), and majority-voting hard decision fusion (MV-HDF) schemes in the presence of MUs.

Effect of Dynamic Spectrum Aggregation and Allocation Algorithms in Cognitive Radio Networks

With rapid proliferation of radio technology, discontinuous spectrum aggregation and access in a single radio has become conceivable. When the bandwidth of the spectrum fragment is narrow, then spectrum aggregation can be utilized to bundle multiple small fragments in one radio. This can positively improve the efficiency of the spectrum utilization. Though, aggregation of fragmented segments leads to improved spectrum utilization it results in further spectrum fragmentation leading to practical hardware constraints. As a solution, we use the popular memory allocation techniques to perform spectrum aggregation so that further fragmentation can be reduced. We derive at an efficient Spectrum Aggregation Algorithm based on Memory Allocation techniques.

Hardware-Efficient and Fast Sensing-Time Maximum-Minimum-Eigenvalue-Based Spectrum Sensor for Cognitive Radio Network

This paper proposes an implementation-friendly maximum-minimum-eigenvalue (MME)-based spectrum sensing algorithm for cognitive radio network. An iterative power method has been applied for the first time to compute maximum and minimum eigenvalues that reduces the computational complexity of this MME algorithm. We suggest new digital architecture of MME-based spectrum sensor with shorter critical-path delay that lowers its sensing time. This spectrum-sensor architecture has been resource shared to further enhance the hardware efficiency. Performance analysis of the suggested MME algorithm with 1024 input-signal samples delivers adequate performance at −10 dB of SNR with the detection probability of 0.8. Suggested MME algorithm performs better than the energy detection-based spectrum sensing under noise uncertainty. It has detection gain of 5.3 dB compared to the cyclostationary feature detection-based spectrum sensing algorithm at 0.7 detection probability. Hardware prototyping of our MME spectrum sensor has been carried out in FPGA platform and its real-time testing is performed using the communication environment of DVB-T standard. We synthesized and post-layout simulated proposed digital sensor in 90 nm-CMOS process that resulted in 0.42 mm2 of area, operating at a maximum clock frequency of 404 MHz which results in the sensing time of $53.5~\mu \text{s}$ . Comparison of our work with literature shows that the suggested MME spectrum-sensor has $2.5\times $ shorter sensing time and lowest area-time-product of 0.023, indicating better hardware-efficiency, than the state-of-the-art implementations.

Combination weighted clustering algorithms in cognitive radio networks

SummaryThe topology of the distributed cognitive radio network is volatile as influenced by the behavior of primary users, and this condition leads to the large communication overhead and low utilization of spectrum resources. A combination weighted clustering algorithm is proposed in the study to reduce the communication overhead of the distributed cognitive network and maintain the stability of the network structure. First, a clustering algorithm considering the available channel, geographic location, and experienced data (used for collecting the behavior of secondary users (SUs) in the network and the evaluation on it) of SUs is put forward through analyzing the characteristics of the idle channels in cognitive network. Three factors, namely, average channel capacity, stability, and channel quality, are converted into quantifiable values. The cluster head is determined on the basis of the three factors. Then, the cluster members and gateway nodes are determined using the weighting formula and the location information of the cluster head. Results show that the proposed clustering algorithm can generate 15% more clusters than other algorithms and reduce 40% of network communication overhead when the transmission distance between cognitive users and the channel number change. Thus, the stability of the cluster structure is maintained and the communication overhead is decreased. This study provides references for the construction of the stable distributed cognitive network.

An Optimal Power Allocation Algorithm for Cognitive Radio Networks Based on Maximum Rate and Interference Constraint

An Optimal Power Allocation Algorithm for Cognitive Radio Networks Based on Maximum Rate and Interference Constraint

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.