Model For Cognitive Radio Networks Research Articles

Energy consumption optimization in green cognitive radio networks based on collaborative spectrum sensing

In the realm of green communications, the focus is on achieving high spectrum efficiency and low energy consumption. This paper addresses the crucial goal of reducing energy usage in green cognitive radio networks (CRNs) during communication between secondary users (SUs) and primary users (PUs). This paper proposed an energy consumption optimization model (ECOM) for green CRN utilizing collaborative spectrum sensing thereby minimizing the environmental impact and prolonging the operational lifetime of devices. The collaborative spectrum sensing proved its role in optimizing the energy consumption in the green CRN. An energy-efficient scheduling algorithm is implemented in ECOM, in which the SUs can be scheduled to perform their sensing process in a time-division manner to reduce energy consumption. Applied collaborative spectrum sensing serves as a valuable resource for researchers, network operators, and policymakers seeking to balance the increasing demand for wireless communication services with the imperative of sustainability. The simulation results and mathematical proof emphasize that ECOM demonstrates reduced energy usage and increased average effective throughput when compared to other recent models.

Improved spectrum prediction model for cognitive radio networks using hybrid deep learning technique

Cognitive Radio (CR) technology has been highlighted as one of the most likely answers to the issue of spectrum shortage with the rise of fifth generation and beyond communication. Secondary users (SUs) in cognitive radio networks (CRN) must continuously monitor the spectrum to forecast channel occupancy by primary users (PUs) based on fundamental factors, such as location, time, and RF band. A hybrid deep learning model called LSTM-MLP (Long Short-Term Memory-Multilayer Perceptron) is proposed to improve idle channel prediction probability thus reducing the overall sensing time by cognitive users during spectrum sensing. Performance evaluation for the proposed model is done in terms of prediction error and efficiency, the GSM-900 spectrum dataset demonstrates that LSTM-MLP performs better in terms of improved prediction accuracy compared to existing state-of-art prediction techniques.

Efficient Optimized ATSDERP Routing Based DEQRL Spectrum Sharing HPNCS Network Coding Model in Cognitive Radio Networks

Efficient Optimized ATSDERP Routing Based DEQRL Spectrum Sharing HPNCS Network Coding Model in Cognitive Radio Networks

Optimum energy harvesting model for bidirectional cognitive radio networks

Wireless devices’ energy efficiency and spectrum shortage problem has become a key concern worldwide as the number of wireless devices increases at an unparalleled speed. Wireless energy harvesting technique from traditional radio frequency signals is suitable for extending mobile devices’ battery life. This paper investigates a cognitive radio network model where primary users have their specific licensed band, and secondary users equipped with necessary hardware required for energy harvesting can use the licensed band of the primary user by smart sensing capability. Analytical expressions for considered network metrics, namely data rate, outage probability, and energy efficiency, are derived for uplink and downlink scenarios. In addition, optimal transmission power and energy harvesting power are derived for maximum energy efficiency in downlink and uplink scenarios. Numerical results show that outage probability improves high transmission power in the downlink scenario and high harvested power in the uplink scenario. Finally, the result shows that energy efficiency improves using optimum transmission power and energy harvesting power for downlink and uplink scenarios.

Spectral decision for cognitive radio networks in a multi-user environment

Cognitive radio networks promote better spectral efficiency of the electric radio spectrum. The vast majority of current spectral decision models for cognitive radio networks evaluate their performance based on a single secondary user. In reality, the network can experience multiple requests from spectral opportunities. Based on this, the intent of this article is to present and evaluate a spectral decision model for cognitive radio networks in a multi-user environment taking into account the effect of the decisions of the SU on the usefulness of the other SU. To achieve this, a spectral decision model was developed that allows secondary users to share relevant information before accessing the spectrum so that they can select the most appropriate spectral opportunities. The evaluation and validation of the model was performed using three multicriteria decision-making algorithms under the metric of the number of total handoffs in a conventional scenario and a real scenario, in the conventional scenario, only users that match the input of the multiuser module are included; in the real scenario, in addition to the conventional users, users that enter and leave at random times are included, a feature that alters the models for estimating the behavior of the radio environment. The results show better performance of the TOPSIS algorithm over VIKOR and SAW. The most important contribution of this work is the evaluation of the performance of the spectral decision algorithms implemented in a multi-user environment that allows multiple access and exchange of information between users, with experimental spectral occupation data.

Spectral decision in cognitive radio networks based on deep learning

Cognitive radio networks (CRN) have gained great relevance in the efficient use of the radio spectrum, and one of the key aspects of this technology is the spectral decision. The performance of secondary user communication depends largely on the intelligent choice of an appropriate spectral opportunity. The purpose of this research is to propose and assess the performance of a spectral decision model for CRN based on the Deep Learning technique. To achieve this, a classifier was adapted through the feature extraction technique that identifies three levels of traffic (high, medium and low) in a spectral occupation experimental power matrix that models the primary user. The extraction of features is done by Deep Learning and the process of classifying the successful set of features is done by a Support Vector Machine (SVM). These were used along with five evaluation metrics—total handoffs, failed handoffs, bandwidth, delay and throughput—to measure the performance of the proposed spectral decision model based on the Deep Learning technique, and to compare the results with the Multi-Criteria Optimization and Compromise Solution (VIKOR), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Simple Additive Weighting (SAW). This work presents five contributions: incorporation of the real behavior of licensed users, implementation of performance metrics for spectral mobility, proposal of an RGB conversion algorithm based on the threshold level, feedback in the classifier and a methodology based on priorities and scores to establish the channels with the highest availability. The results of this evaluation show that the proposed model has a better performance in the five metrics compared to the other techniques.

Heuristic Load-Balancing Optimization Model For Cognitive Radio Networks Using Iot

The Internet of Things (IoT), via different communication models, connects network resources through the Internet. A main IoT technology is the cognitive radio network, which can resolve spectrum problems in IoT applications effectively. A novel approach is proposed in our paper for IoT sensor networks to achieve the channel status in typical Cognitive-IoT model produces more spectrum holes which it leads a Quality-of-Service issue due to the channel allocation and spectrum allocation errors. To minimize the error rate, we proposed a Heuristic Load-balancing optimization model (HLBO) for OFDM-based Cognitive radio network model. Proposed model categorizes channel scheduling process by considering resource allocation and load. The proposed HLBO employs a load optimization algorithm to enhance channel status, based on different traffic states the load optimization model predict the spectrum allocation rate based on the channel and sub-channel status.

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.

QoS Provision in a Dynamic Channel Allocation Based<?brk?> on Admission Control Decisions

Cognitive Radio Networks have emerged in the last decades as a solution of two problems: spectrum underutilization and spectrum scarcity. In this work, we propose a dynamic spectrum sharing mechanism, where primary users have strict priority over secondary ones in order to improve the mean spectrum utilization with the objective of providing to secondary users a satisfactory grade of service with a small interruption probability. We study a stochastic model for Cognitive Radio Networks with fluid limits techniques. Our main findings consist in a Gaussian limit theorem in the sub-critical case, and a non-Gaussian limit theorem, under a different scaling scheme, in the critical case. These results provide us practical QoS criteria for sharing policies. We support our analysis with representative simulated examples in both scenarios.

Analysis of spectrum handoff delay using finite queuing model in cognitive radio networks

Analysis of spectrum handoff delay using finite queuing model in cognitive radio networks

Analysis of Spectrum Handoff delay using Finite Queuing model in Cognitive Radio Networks

Spectrum handoff is an inevitable phenomenon to exploit dynamic spectrum access in cognitive radio networks (CRNs) for better spectrum utilisation. When a licensed or primary user reclaims its operating channel, the cognitive user has to initiate a suitable handoff procedure for successful completion of its ongoing transmission. This paper analyses the prioritised proactive spectrum handoff decision for a finite capacity cognitive radio network using preemptive resume priority (PRP) M/G/1/K queuing model. Cumulative handoff delay (CHD) and total service time (TST) are taken to investigate the performance of the handoff strategies. We establish an analytical model of cumulative handoff delay and total service time of proactive switching spectrum handoff for the proposed finite length queuing network. The impact of queue length on performance measuring metrics such as CHD and TST in terms of arrival rate of primary users and mobility factor of spectrum holes are presented extensively.

Residual Energy Maximization in Cognitive Radio Networks With Q-Routing

The present work explores a multihop cognitive radio network model that adopts energy harvesting (EH) and reinforcement learning based Q-routing. An optimization problem is formulated that maximizes the total residual energy (RE) in the secondary network under the constraints of primary user (PU) cooperation rate, secondary outage probability, and interference to the primary receiver. Closed-form expressions of EH duration, equivalent transmit power, and its consequent allocation factor on the secondary data transmission and the PU cooperation are found. A large set of simulation results show a gain by ~4.45%, ~21.80%, ~12.09%, and 7.937 times in the RE, secondary outage probability, network lifetime, and path set-up time, respectively, over the existing algorithms.

Throughput maximisation in cognitive radio networks with residual bandwidth

Recent progress in cognitive radio networks (CRNs) promises to meet device-to-device communication requirements for spectrum utilisation and power control to support billions of machines/devices to be connected worldwide. The architecture of the CRN must maintain a high data rate (throughput) at low power consumption, which requires both spectrum efficient and energy efficient system design. To this aim, the proposed work adopts a CRN model, which operates in an interweave mode that allows spectrum sensing followed by opportunistic secondary user (SU) data transmission over the unused bandwidth of the primary user (PU) in a non-overlapping frame structure. Closed form expressions of the optimal spectrum sensing duration, bandwidth, and power allocation of each secondary node are obtained to maximise the sum throughput of the overall CRN while maintaining the constraints of sensing reliability of PU, individual SU transmission outage probability, permissible interference, and residual bandwidth. Numerical results highlight that the proposed scheme maximises the sum network throughput by $\sim 50.54$~50.54 and $\sim 63.76\%$~63.76% over the other existing techniques.

Radio Resource Allocation with The Fairness Metric for Low Density Signature OFDM in Underlay Cognitive Radio Networks.

Low density signature orthogonal frequency division multiplexing (LDS-OFDM), one type of non-orthogonal multiple access (NOMA), is a special case of multi-carrier code division multiple access (MC-CDMA). In LDS-OFDM, each user is allowed to spread its symbols in a small set of subcarriers, and there is only a small group of users that are permitted to share the same subcarrier. In this paper, we study the resource allocation for LDS-OFDM as the multiple access model in cognitive radio networks. In our scheme, SUs are allocated to certain subcarriers based on minimum interference or higher SINR in each subcarrier. To overcome the problem where SUs were allocated less than the subcarriers, we propose interference limit-based resource allocation with the fairness metric (ILRA-FM). Simulation results show that, compared to the ILRA algorithm, the ILRA-FM algorithm has a lower outage probability and higher fairness metric value and also a higher throughput fairness index.

Blind Spectrum Sensing Approaches for Interweaved Cognitive Radio System: A Tutorial and Short Course

Spectrum sensing is one of the essential tasks to have a cognitive radio system, which will allow an unlicensed user, called secondary user, to utilize the spectrum while the licensed user, called primary user, is not occupying it. The spectrum sensing approaches can be classified as blind and knowledge aided approaches. This tutorial summarizes blind spectrum sensing (BSS) approaches that require no prior knowledge of the licensed user’s signal characteristics, specifically for an interweave cognitive radio network model. The tutorial provides a thorough background, major implementations, and limitations of the BSS approaches, which are energy detector approach, maximum to minimum eigenvalue approach, maximum eigenvalue approach, covariance absolute value approach, and covariance Frobenius norm approach. Moreover, the tutorial compares these approaches based on performance metrics and complexity requirements. Furthermore, for a higher interference protection, the combination of two different spectrum sensing approaches, namely two-stage detection technique is presented and discussed. Besides, the tutorial discusses the challenges and possible future research directions. The fundamental objective of this tutorial is to provide insightful views and design aspects of BSS approach to researchers. For this purpose, the tutorial includes pseudo codes and simulation examples to illustrate more about the practical aspects of the above-mentioned approaches.

Incremental voting based spectrum sensing model for cognitive radio networks

Incremental voting based spectrum sensing model for cognitive radio networks

Cooperative Prediction-and-Sensing-Based Spectrum Sharing in Cognitive Radio Networks

This paper proposes prediction-and-sensing-based spectrum sharing, a new spectrum-sharing model for cognitive radio networks, with a time structure for each resource block divided into a spectrum prediction-and-sensing phase and a data transmission phase. Cooperative spectrum prediction is incorporated as a sub-phase of spectrum sensing in the first phase. We investigate a joint design of transmit beamforming at the secondary base station (BS) and sensing time. The primary design goal is to maximize the sum rate of all secondary users (SUs) subject to the minimum rate requirement for all SUs, the transmit power constraint at the secondary BS, and the interference power constraints at all primary users. The original problem is difficult to solve since it is highly nonconvex. We first convert the problem into a more tractable form, then arrive at a convex program based on an inner approximation framework, and finally propose a new algorithm to successively solve this convex program. We prove that the proposed algorithm iteratively improves the objective while guaranteeing convergence at least to local optima. Simulation results demonstrate that the proposed algorithm reaches a stationary point after only a few iterations with a substantial performance improvement over existing approaches.

Performance analysis of cognitive radio networks for secondary users with slotted central control

This paper deals with analysis, performance evaluation and optimization of cognitive radio networks with central controller. The main principle of this technology is that secondary users are enabled to make opportunistic use of the spectrum part, which is actually unused by the primary users. The considered network enables heterogeneous slotted structure for the channel, in which the secondary user’s packets are transmitted on a slot basis, while the primary user’s packets are forwarded in super-slots, i.e. in fixed length slot-blocks. This heterogeneous slotted channel structure enables more flexible operation leading to more realistic system model of cognitive radio network. We model the cognitive radio networks by preemptive priority queueing model with two classes of customers. We solve the model by applying Markov chain technique and derive the steady-state distributions of the number of primary user’s packets and secondary user’s packets in the system. We provide the formulas for several performance measures including the interruption rate, loss rate, throughput, and average latency of secondary users. After validating the analysis by simulation the influence of the secondary user’s buffer capacity on various system performance measures is investigated. In the last part of the paper we address the question of optimal design of secondary user’s buffer capacity.

Multivariable adaptive handoff spectral model for cognitive radio networks

Multivariable adaptive handoff spectral model for cognitive radio networks

Resource Allocation in Heterogeneous Buffered Cognitive Radio Networks

Resources available for operation in cognitive radio networks (CRN) are generally limited, making it imperative for efficient resource allocation (RA) models to be designed for them. However, in most RA designs, a significant limiting factor to the RA’s productivity has hitherto been mostly ignored, the fact that different users or user categories do have different delay tolerance profiles. To address this, in this paper, an appropriate RA model for heterogeneous CRN with delay considerations is developed and analysed. In the model, the demands of users are first categorised and then, based on the distances of users from the controlling secondary user base station and with the assumption that the users are mobile, the user demands are placed in different queues having different service capacities and the resulting network is analysed using queueing theory. Furthermore, to achieve optimality in the RA process, an important concept is introduced whereby some demands from one queue are moved to another queue where they have a better chance of enhanced service, thereby giving rise to the possibility of an improvement in the overall performance of the network. The performance results obtained from the analysis, particularly the blocking probability and network throughput, show that the queueing model incorporated into the RA process can help in achieving optimality for the heterogeneous CRN with buffered data.