Cognitive Radio Engine Research Articles

Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review

Cognitive radios are expected to play an important role in capturing the constantly growing traffic interest on remote networks. To improve the usage of the radio range, a cognitive radio hub detects the weather, evaluates the open-air qualities, and then makes certain decisions and distributes the executives’ space assets. The cognitive radio works in tandem with artificial intelligence and artificial intelligence methodologies to provide a flexible and intelligent allocation for continuous production cycles. The purpose is to provide a single source of information in the form of a survey research to enable academics better understand how artificial intelligence methodologies, such as fuzzy logics, genetic algorithms, and artificial neural networks, are used to various cognitive radio systems. The various artificial intelligence approaches used in cognitive radio engines to improve cognition capabilities in cognitive radio networks are examined in this study. Computerized reasoning approaches, such as fuzzy logic, evolutionary algorithms, and artificial neural networks, are used in the writing audit. This topic also covers cognitive radio network implementation and the typical learning challenges that arise in cognitive radio systems.

Nakagami-m Fading Affected Multiuser Cognitive Radio System Design Using Moth Flame Optimization

Moth flame optimization (MFO), a novel natural surrounding based optimization technique, whose main character is the moth, from the family of butterflies. A special navigation system known as transverse orientation, used by moths to fly in the moon night, is the main inspiration behind this technique. This paper introduces the employment of MFO to a novel cognitive radio system for its optimal designing. A multiple secondary user cognitive radio system whose channels is affected by Nakagami-m fading and is working on IEEE802.22 WRAN standard is considered. Five different objectives to meet the aspired QoS defined by user are laid down. Their Fitness functions are modified to include fading. Then MFO is applied to them for optimizing the transmission parameters. Optimized results obtained by MFO are compared with the present literature including biogeography based optimization, genetic algorithm and simulated annealing to validate its effectiveness in cognitive radio systems. A non-parametric statistical testing is also conducted to further authenticate the implementation of MFO over cognitive radio problem. Hence MFO has emerged as a capable candidate for designing an efficient cognitive radio engine.

인컴번트 사용자 채널 점유확률 변화에 따른 SVM 기반의 무선 트래픽 이용환경 추론기법

In this paper, we propose a case-based reasoning Cognitive Radio(CR) engine that uses limited resources efficiently in military tactical wireless communication environments. A CR engine should be able to learn and infer, and thus predict the available channel information of a secondary user based on information about traffic usage. To be able to do so, a low probability of channel collision should be associated with the engine. The engine should, thereby, be able to indicate the probability of channel occupancy of an incumbent user who requires interference protection. We used a Support Vector Machine(SVM) to measure the histogram-type wireless traffic usage environment in accordance with the change in the probably of channel occupancy of the incumbent user. SVM is a sorting algorithm of machine learning. Next, we calculated the histogram

DYNAMIC SMART GRID COMMUNICATION PARAMETERS BASED COGNITIVE RADIO NETWORK

The demand for more spectrums in a smart grid communication network is a significant challenge in originally scarce spectrum resources. Cognitive radio (CR) is a powerful technique for solving the spectrum scarcity problem by adapting the transmission parameters according to predefined objectives in an active wireless communication network. This paper presents a cognitive radio decision engine that dynamically selects optimal radio transmission parameters for wireless home area networks (HAN) of smart grid applications via the multi-objective differential evolution (MODE) optimization method. The proposed system helps to drive optimal communication parameters to realize power saving, maximum throughput and minimum bit error rate communication modes. A differential evolution algorithm is used to select the optimal transmission parameters for given communication modes based on a fitness function that combines multiple objectives based on appropriate weights. Simulation results highlight the superiority of the proposed system in terms of accuracy and convergence as compared with other evolution algorithms (genetic optimization, particle swarm optimization, and ant colony optimization) for different communication modes (power saving mode, high throughput mode, emergency communication mode, and balanced mode).

A Survey on Optimization Based Spectrum Sensing Techniques to Reduce ISI and PAPR in OFDM Based Cognitive Radio System

Cognitive radio is emerging technologies in OFDM based wireless systems which are very important for spectrum sensing. By using cognitive radio (CR) high data can be transferred with low bit error rate. The key idea of OFDM is to split the total transmission bandwidth into the subcarriers which further reduce the intersymbol interference (ISI) and peak to average power ratio(PAPR) in the signal. There are many optimization based spectrum sensing techniques are existing for efficient sensing purpose but each has its own advantages and disadvantages. This leads to start the comprehensive study for reducing PAPR and ISI(Intersymbol interference) in terms of FPGA based partial configuration. In the first part of review OFDM characteristics of the signal has compared with several optimizations based ISI reduction techniques. The second part is to compare the various spectrum sensing techniques in cognitive radio engine and its application in FPGA.

A Survey on Optimization Based Spectrum Sensing Techniques to Reduce ISI and PAPR In OFDM Based Cognitive Radio System

Cognitive radio is emerging technologies in OFDM based wireless systems which are very important for spectrum sensing. By using cognitive radio (CR) high data can be transferred with low bit error rate. The key idea of OFDM is to split the total transmission bandwidth into the subcarriers which further reduce the intersymbol interference (ISI) and peak to average power ratio(PAPR) in the signal. There are many optimization based spectrum sensing techniques are existing for efficient sensing purpose but each has its own advantages and disadvantages. This leads to start the comprehensive study for reducing PAPR and ISI(Intersymbol interference) in terms of FPGA based partial configuration. In the first part of review OFDM characteristics of the signal has compared with several optimizations based ISI reduction techniques. The second part is to compare the various spectrum sensing techniques in cognitive radio engine and its application in FPGA

Applying Case-Based Reasoning to Tactical Cognitive Sensor Networks for Dynamic Frequency Allocation.

In this paper, a cognitive radio engine platform is proposed for exploiting available frequency channels for a tactical wireless sensor network while aiming to protect incumbent communication devices, known as the primary user (PU), from undesired harmful interference. In the field of tactical communication networks, there is an urgent need to identify available frequencies for opportunistic and dynamic access to channels on which the PU is active. This paper introduces a cognitive engine platform for determining the available channels on the basis of a case-based reasoning technique deployable as a core functionality on a cognitive radio engine to enable dynamic spectrum access (DSA) with high fidelity. To this end, a plausible learning engine to characterize the channel usage pattern is introduced to extract the best channel candidate for the tactical cognitive radio node (TCRN). The performance of the proposed cognitive engine was verified by simulation tests that confirmed the reliability of the functional aspect, which includes the learning engine, as well as the case-based reasoning engine. Moreover, the efficacy of the TCRN with regard to the avoidance of collision with the PU operation, considered the etiquette secondary user (SU), was demonstrated.

Experience level analysis for a cognitive radio engine

A cognitive radio engine (CE) is where the advanced adaptation algorithms for a cognitive radio is implemented. A CE is an intelligent agent which observes the radio environment and chooses the best communication settings that meet the application’s goal. In this process, providing reliable performance is one of the major challenges faced by a CE. Therefore, one of the most important issues in designing CEs is the ability to characterize and reliably predict performance of the CE in different operating scenarios. An operating scenario is defined as the set of the operating objective, channel availability, and channel quality metrics. In this paper, we develop several performance evaluation and prediction indices to quantify the amount of knowledge of different CE algorithms independently of the implementation approach and/or their operating scenarios. Using these new indices, we are able to provide a more accurate estimation of the learning process and future performance of each individual CE algorithm. A number of simulation-based experiments was conducted. Our results show that proposed contextual CE algorithms based on the developed knowledge indicators is able to improve the wireless communication system’s objective rewards significantly. In effect, the contextual CE is able to deliver about 10% more data than the CE with the fixed exploration rate.

Cognitive Radio Engine Design for IoT Using Real-Coded Biogeography-Based Optimization and Fuzzy Decision Making

The Internet of Things (IoT) paradigm expands the current Internet and enables communication through machine to machine, while posing new challenges. Cognitive radio (CR) Systems have received much attention over the last decade, because of their ability to flexibly adapt their transmission parameters to their changing environment. Current technology trends are shifting to the adaptability of cognitive radio networks into IoT. The determination of the appropriate transmission parameters for a given wireless channel environment is the main feature of a cognitive radio engine. For wireless multicarrier transceivers, the problem becomes high dimensional due to the large number of decision variables required. Evolutionary algorithms are suitable techniques to solve the above-mentioned problem. In this paper, we design a CR engine for wireless multicarrier transceivers using real-coded biogeography-based optimization (RCBBO). The CR engine also uses a fuzzy decision maker for obtaining the best compromised solution. RCBBO uses a mutation operator in order to improve the diversity of the population and enhance the exploration ability of the original BBO algorithm. The simulation results show that the RCBBO driven CR engine can obtain better results than the original BBO and outperform results from the literature. Moreover, RCBBO is more efficient when applied to high-dimensional problems in cases of multicarrier system.

Optimisation of Cognitive Engine Design using Cultural Algorithm

Objectives: To optimize the design of cognitive radio engine using Cultural Algorithm (CA). The simulated results are compared with commonly used Evolutionary Algorithms (EA) like Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA). Methods/Statistical Analysis: Use of CA has been proposed to find a suitable fitness score under varying channel conditions over multiple iterations. Matlab has been chosen as the platform for simulating various scenarios. An attempt has also been made to optimize the time of convergence. Findings: Simulations indicate that CA emerges as a potential candidate for designing of CR Engine (CRE) for deployment of a CR Network (CRN). Using CA, the fitness score has improved as compared to other EAs. Improvements: The algorithm shows faster convergence and improves its performance with each successive iteration.

Enhancing robustness and perturbation tolerance of cognitive radio engines with metacognition

A cognitive radio engine (CE) is an intelligent agent which observes the radio environment and chooses the best communication settings that best meet the application's goal. In this process, providing reliable performance is one of the major tasks in designing CEs for wireless communication systems. The main purpose of this work is providing predictable performance and controlling the cost of intelligent algorithms based on the CE's experience and complexity analysis respectively. In this work, we extend our meta-CE design to control the cost of computations and provide more reliable performance for providing the minimum requirement of the radio applications in different scenarios. To achieve this, we use robust training algorithm (RoTA) in two different levels alongside of the individual CE algorithms. The RoTA, enables radio to guarantee some minimum output performance based on the learning stages. RoTA uses confidence interval approximation for standard normal distribution to calculate the lower and upper bounds of CE's expected performance to analyze the reliability of decisions. Moreover, in the case of non-stationary environments, RoTA is facilitated by forgetfulness factor to provide minimum performance guarantees. The second level of RoTA operates in meta-level to control the amount of computation complexity of intelligent algorithms in all levels with respect to the obtained performance and complexity analysis.

Metacognition and the next generation of cognitive radio engines

Much of the previous research on cognitive radio has focused on developing algorithms based on artificial neural networks, the genetic algorithm, and reinforcement learning, each with its pros and cons. In this research, we present a new approach based on metacognition. We believe that the metacognitive framework can be the foundation for the next generation of CRs and further the performance improvements in CR. In this work, we present the elements involved in metacognitive radio, discuss the challenges in their development, present solutions to the challenges along with a possible meta-CR architecture, and show results from our implementation. Each cognitive engine (CE) algorithm has strengths and limitations that make it more suitable for certain operating scenarios (channel conditions, operating objective, available hardware, etc.) than other algorithms. A meta-CE can adapt faster and improve performance by exploiting the characteristics and expected performance of the individual CE algorithms. It understands the operational scenarios and utilizes the most appropriate algorithm for the current operational scenario by switching between the algorithms or adjusting them as necessary.

A Novel Cognitive Radio Decision Engine Based on Chaotic Quantum Bee Colony Algorithm

To improve the adaptive parameter adjustment function of the cognitive radio, a novel cognitive radio decision engine based on chaotic quantum bee colony optimized algorithm is proposed. First, the population was initialized by quantum coding and logistic mapping. Then, fast quantum rotation angle adjusting strategy, based on social cognitive, was used to conduct the neighborhood search of employed bees and onlooker bees. Finally, the new solution was generated by chaotic disturbing the solution that has reached the limit times of cycles. According to the simulated experiments under a multi-carrier system, the results indicate that the novel engine show a much better convergence and e‐ciency than the one based on quantum genetic algorithm or binary artiflcial bee colony algorithm, and the results of parameters reconflguration is consistent with user demands.

Comparative performance analysis of evolutionary algorithm based parameter optimization in cognitive radio engine: A survey

One of the important features of the cognitive radio engine is to adapt the parameters of radio to fulfill certain objectives in a time varying wireless environment. In order to achieve this adaptation, six evolutionary algorithms are employed for optimizing the predefined fitness functions in the radio environment. The performance of genetic algorithm, particle swarm optimization, differential evolution, bacterial foraging optimization, artificial bee colony optimization and cat swarm optimization algorithm in different modes of operation are studied in detail. Each algorithm is tested in single and multicarrier communication system in order to acknowledge the advantage of multicarrier communication systems in wireless environment. The spectral interference introduced by the cognitive user into the primary user’s band and that introduced by the primary user into the cognitive user’s band are also investigated. The performance of different algorithms are compared using convergence characteristics and four statistical metrics.

Cognitive-radio and antenna functionalities: A tutorial [Wireless Corner

This paper discusses the fundamental principles of a cognitive-radio RF system. The key points required to achieve a true cognitive-radio device are outlined. The operation of a cognitive-radio system is mainly divided into two tasks. In the first task, a cognitive-radio device searches and identifies any part of the spectrum that is not occupied. The second task consists of achieving an optimal mode of communication by allocating the appropriate channels to be used. In this paper, the RF requirements required to operate a cognitive-radio device are detailed. Such a device can adopt one of two scenarios of a cognitive-radio system: the “interweave” or “underlay” mode of operation. For both scenarios, a cognitive cycle is followed. This cycle consists of the following four steps: (1) observe, (2) decide, (3) act, and (4) learn. A cognitive-radio engine is responsible for managing and integrating these four functions together into a single cognitive-radio device. In this tutorial, the realization of the four functions of a cognitive-radio cycle are detailed for both types of cognitive radio, and various RF front-end examples are presented and discussed.

Implementation of LTE system on an SDR platform using CUDA and UHD

In this paper, we present an implementation of a long term evolution (LTE) system on a software defined radio (SDR) platform using a conventional personal computer that adopts a graphic processing unit (GPU) and a universal software radio peripheral2 (USRP2) with a URSP hardware driver (UHD) to implement an SDR software modem and a radio frequency transceiver, respectively. The central processing unit executes C++ control code that can access the USRP2 via the UHD. We have adopted the Ettus Research UHD due to its high degree of flexibility in the design of the transceiver chain. By taking advantage of this benefit, a simple cognitive radio engine has been implemented using libraries provided by the UHD. We have implemented the software modem on a GPU that is suitable for parallel computing due to its powerful arithmetic and logic units. A parallel programming method is proposed that exploits the single instruction multiple data architecture of the GPU. We focus on the implementation of the Turbo decoder due to its high computational requirements and difficulty in parallelizing the algorithm. The implemented system is analyzed primarily in terms of computation time using the compute unified device architecture profiler. From our experimental tests using the implemented system, we have measured the total processing time for a single frame of both transmit and receive LTE data. We find that it takes 5.00 and 8.58 ms for transmit and receive, respectively. This confirms that the implemented system is capable of real-time processing of all the baseband signal processing algorithms required for LTE systems.

Cognitive Radio Engine Model Utilizing Soft Fusion Based Genetic Algorithm for Cooperative Spectrum Optimization

Cognitive radio (CR) is to detect the presence of primary users (PUs) reliably in order to reduce the interference to licensed communications. Genetic algorithms (GAs) are well suited for CR optimization problems to increase efficiency of bandwidth utilization by manipulating its unused portions of the apparent spectrum. In this paper, a binary genetic algorithm (BGA) -based soft fusion (SF) scheme for cooperative spectrum sensing in cognitiveradio network is proposed to improve detection performance and bandwidth utilization. The BGA -based optimization method is implemented at the fusion centre of a linear SF scheme to optimize the weighting coefficients vector to maximize global probabilityof detection performance. Simulation results and analys es confirm that the proposed scheme meets real time requirements of cognitive radio spectrum sensing and it outperforms conventional natural deflection coefficient- (NDC-), modified deflection coefficient- (MDC-), maximal ratio combining- (MRC-) and equal gain combining- (EGC-) based SDF schemes as well as the OR -rule based hard decision fusion (HDF). The propose BGA scheme also converges fast and achieves the optimum performance, which means that BGA based method is efficient and quite stable also.

Cognitive Radio Engine Training

Training is the task of guiding a cognitive radio engine through the process of learning a desired system's behavior and capabilities. The training speed and expected performance during this task are of paramount importance to the system's operation, especially when the system is facing new conditions. In this paper, we provide a thorough examination of cognitive engine training, and we analytically estimate the number of trials needed to conclusively find the best-performing communication method in a list of methods sorted by their possible throughput. We show that, even if only a fraction of the methods meet the minimum packet success rate requirement, near maximal performance can be reached quickly. Furthermore, we propose the Robust Training Algorithm (RoTA) for applications in which stable performance during training is of utmost importance. We show that the RoTA can facilitate training while maintaining a minimum performance level, albeit at the expense of training speed. Finally, we test four key training techniques (ε-greedy; Boltzmann exploration; the Gittins index strategy; and the RoTA) and we identify and explain the three main factors that affect performance during training: the domain knowledge of the problem, the number of methods with acceptable performance, and the exploration rate.

The performance evaluation of a spectrum sensing implementation using an automatic modulation classification detection method with a Universal Software Radio Peripheral

Based on the inherent capability of automatic modulation classification (AMC), a new spectrum sensing method is proposed in this paper that can detect all forms of primary users’ signals in a cognitive radio environment. The study presented in this paper focuses on the sensing of some combined analog and digitally primary modulated signals. In achieving this objective, a combined analog and digital automatic modulation classifier was developed using an artificial neural network (ANN). The ANN classifier was combined with a GNU Radio and Universal Software Radio Peripheral version 2 (USRP2) to develop the Cognitive Radio Engine (CRE) for detecting primary users’ signals in a cognitive radio environment. The detailed information on the development and performance of the CRE are presented in this paper. The performance evaluation of the developed CRE shows that the engine can reliably detect all the primary modulated signals considered. Comparative performance evaluation carried out on the detection method presented in this paper shows that the proposed detection method performs favorably against the energy detection method currently acclaimed the best detection method. The study results reveal that a single detection method that can reliably detect all forms of primary radio signals in a cognitive radio environment, can only be developed if a feature common to all radio signals is used in its development rather than using features that are peculiar to certain signal types only.

Pareto optimization of cognitive radio parameters using multiobjective evolutionary algorithms and fuzzy decision making

The design of cognitive radio engine aims at adapting the radio parameters to a predefined set of objective functions in communication system and may be formulated as a constrained multiobjective optimization problem. In the proposed work, an efficient design of orthogonal frequency division multiplexing based cognitive radio is carried out using multiobjective evolutionary algorithms. The performances of different algorithms are assessed and compared using three statistical metrics. The simulation results show that our proposed approach outperforms other algorithms while designing a cognitive radio engine. Our proposed approach which is based on the concept of cat swarm optimization, not only efficiently computes but also finds better nondominating solutions. In this paper, multiobjective evolutionary algorithms are applied to the parameter adaptation of a OFDM based cognitive radio engine. The spectral interference between primary and cognitive users is taken into consideration which plays a major role in communication. Due to heuristic nature of evolutionary algorithms, the stability of the simulation results is verified using different statistical tests. A fuzzy logic based strategy is shown in order to find out a compromised solution on the Pareto front.