OFDM-based Cognitive Radio Networks Research Articles

Enhancement of spectral access of optical OFDM system using cognitive radio

Abstract This work presents the research on spectrum sensing in optical OFDM (orthogonal frequency division multiplexing) systems. Spectrum sensing is crucial for cognitive radio networks to efficiently utilize available spectral resources. Various spectrum sensing techniques are explored, including energy detection (ED), cyclostationary feature detection (CD), and matched filter detection (MF). These techniques enable the detection of occupied and unoccupied subcarriers in optical OFDM systems, facilitating dynamic spectrum access and spectrum sharing. The research highlights the importance of accurate spectrum sensing in maximizing spectral efficiency, optimizing network performance, and enabling coexistence of multiple users in optical OFDM-based cognitive radio networks. The findings contribute to the development of future wireless communication systems. The parameters such as probability of detection (pd), probability of false alarm (pfa), bit error rate (BER), and power spectral density (PSD) is analysed using computer simulation. The simulation results reveal that the MF achieved a gain of 2.8 dB and 3.6 dB as compared with conventional spectrum sensing algorithms.

An AHP-TOPSIS Integrated Model for QoS-Aware Energy Efficiency in Green Cognitive Radio Networks

The selection of the best spectrum band available to meet the quality of service requirements of secondary users without interfering with the transmission of the primary users is considered as a challenge in cognitive radio networks. On the other side, green network is a recent concept that refers to the processes used to optimize a network in order to make it more energy efficient. In this paper, we propose a new algorithm ensuring the selection of the best available spectrum satisfying the demands of the secondary users based on TOPSIS and AHP in OFDM-based cognitive radio networks. We will assess the need for the secondary users in terms of quality of service and energy efficiency, by analyzing the characteristics of the available channels and taking into account the interference generated with the presence of the primary user. It efficiently manages energy because it allows a significant reduction of the transmission power used by the secondary user and therefore presents an effective solution in green networking.

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.

Interference-Aware QoS Guarantees in OFDM-Based Cognitive Radio Networks Based on Branch and Bound

In the past decade, the OFDM access method has been widely used in different types of networks. Indeed; OFDM is the technology of choice for all major wireless systems, including WIFI, WiMAX, 3G, 4G and 5G. In this paper, we are interested in its application within a cognitive radio networks. The main objective is to provide an acceptable quality of service for the secondary user while minimizing interference with the primary user. This problem has been formulated in the literature in the form of a multi-objective function with three modes of communication (multimedia, reliable and low battery). In this paper, we exploit the efficiency of the bounding operators of the branch and bound method in order to solve this problem. The simulation results showed the effectiveness of our proposal by comparing it with the cuckoo search algorithm which has already been validated in the literature for this type of problem. Our proposal surpasses the cuckoo search algorithm for two modes of communication in terms of fitness and execution time.

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.

Energy-Efficient Power Allocation with Guaranteed QoS Under Imperfect Sensing for OFDM-Based Heterogeneous Cognitive Radio Networks

This paper investigates the energy efficient resource allocation scheme for orthogonal frequency division multiplexing based heterogeneous cognitive radio network (HCRN) under imperfect spectrum sensing scenario with guaranteed quality of service (QoS). The objective of this paper is to maximize the energy efficiency (EE) of the HCRN subject to total transmission power, interference and QoS Constraints. To solve the mixed integer nonlinear programming problem efficiently, the primal problem has been transformed into a linear programming problem by separating the resource allocation scheme into two steps, i.e., subcarrier assignment and power allocation. Consequently an energy efficient power allocation (EEPA) algorithm has been anticipated based on fractional programming and sub-gradient method. Numerical results confirm that the proposed EEPA algorithm can achieve higher EE than conventional equal power allocation method.

Uplink achievable rate in underlay random access OFDM-based cognitive radio networks

This paper investigates the uplink achievable rate of secondary users (SUs) in underlay orthogonal frequency division multiplexing based cognitive radio networks, where the SUs randomly access the subcarriers of the primary network. In practice, the primary base stations (PBSs), such as cellular base stations, may not be placed close to each other to mitigate the interferences among them. In this regard, we model the spatial distribution of the PBSs as a β-Ginibre point process which captures the repulsive placement of the PBSs. It is assumed that in order to alleviate the interferences at the PBSs from the SUs, each SU controls its transmit power based on the average interference level at the closest PBS induced by the SU. We first analytically identify the characteristics of the transmit powers at the SUs. Then, tight approximations of the uplink achievable rate of the secondary network are provided in two different scenarios that assume either a decentralized or centralized allocation of the SUs’ subcarriers, respectively. The accuracy of our analytical results is validated by simulation results.

Bio-inspired Approaches for OFDM Based Cognitive Radio

Link adaptation algorithms design for OFDM-based cognitive radio networks is a challenging task. The main concern is to provide a high quality of service for the secondary user while the mutual interference between this last and the primary user persists within a tolerable range. This issue can be formulated as a multiobjective optimisation constraint problem. To tackle this optimisation problem in a multiobjective constraint framework, in this paper we exploit three of the most recent powerful bio-inspired algorithms: firefly, bat, and cuckoo search. Simulation results revealed that, in contrast to the classical genetic algorithm and particle swarm optimisation-based link adaptation, our proposed algorithms exhibit better performance in terms of convergence speed and solution quality with saving rates reaching over 98.93% and 46.60%, respectively.

Secrecy Energy Efficiency Optimization for Artificial Noise Aided Physical-Layer Security in OFDM-Based Cognitive Radio Networks

In this paper, we investigate the power allocation of primary base station (PBS) and cognitive base station (CBS) across different orthogonal frequency division multiplexing (OFDM) subcarriers for energy-efficient secure downlink communication in OFDM-based cognitive radio networks (CRNs) with the existence of an eavesdropper having multiple antennas. We propose a secrecy energy efficiency maximization (SEEM) scheme by exploiting the instantaneous channel state information (ICSI) of the eavesdropper, called ICSI based SEEM (ICSI-SEEM) scheme with a given total transmit power budget for different OFDM subcarriers of both PBS and CBS. As for the case when the eavesdropper's ICSI is unknown, we also propose an SEEM scheme through using the statistical CSI (SCSI) of the eavesdropper, namely SCSI based SEEM (SCSI-SEEM) scheme. Since the ICSI-SEEM and SCSI-SEEM problems are fractional and non-convex, we first transform them into equivalent subtractive problems, and then achieve approximate convex problems through employing the difference of two-convex functions approximation method. Finally, new two-tier power allocation algorithms are proposed to achieve $\varepsilon$-optimal solutions of our formulated ICSI-SEEM and SCSI-SEEM problems. Simulation results illustrate that the ICSI-SEEM has a better secrecy energy efficiency (SEE) performance than SCSI-SEEM, and moreover, the proposed ICSI-SEEM and SCSI-SEEM schemes outperform conventional SR maximization and EE maximization approaches in terms of their SEE performance.

A Novel Physical Layer Security Scheme in OFDM-Based Cognitive Radio Networks

In this paper, a physical-layer-security scheme for an underlay relay-based cognitive radio network (CRN) that uses orthogonal frequency-division multiplexing (OFDM) as the medium access technique is proposed. Resource allocation in relay-aided CRNs becomes a hard problem especially if it is under security threat. Different from conventional relay-based OFDM schemes, in the paper, we consider the relay network which has two dedicated relay nodes; one relay which is capable of subcarrier mapping forward the received signal to the destination and the other sends a jamming signal to add noise to the signal received by the eavesdropper. Optimization is performed under a unified framework where power allocation at the source node, power allocation, and subcarrier mapping in the relay network are optimized to maximize the secrecy rate of the CRN while satisfying the maximum transmission power constraints and the interference threshold of the PU. The power allocation problem at the forwarding relaying node is a non-convex optimization problem. Therefore, at first, the optimization problem is simplified and a closed-form solution is obtained which satisfies the maximum PU interference constraint. Afterward, the optimization problem is solved for satisfying the maximum transmission power constraint. An algorithm is also proposed for subcarrier mapping at the forwarding relaying node. The proposed power allocation method and a subcarrier mapping scheme have low complexity, compared with the baseline schemes. Finally, simulation results are provided for different parameters to show the performance improvement of the proposed scheme in terms of secrecy rate.

Resource allocation in two-way OFDM-based cognitive radio networks with QoE and power consumption guarantees

In this paper, a resource allocation algorithm in two-way orthogonal frequency division multiplexing (OFDM) based cognitive radio networks with quality of experience (QoE) and power consumption guarantees is proposed. We define the overall QoE perceived by secondary users (SUs) per power consumption as QoEW. The power consumption model consists of fixed circuit power, dynamic circuit power, and transmit power which depends on the efficiency of the power amplifiers at different terminals. Under the constraint of total maximum transmit power, the optimization objective is to maximize QoEW while meeting the minimum QoE demands of SUs and maintaining interference threshold limitations of multiple primary users. The resource allocation problem is formulated into a nonlinear fractional programming and transformed into an equivalent convex optimization problem via its hypograph form. Based on the Lagrange dual decomposition method and cross-layer (CL) optimization architecture, this convex optimization problem is separately solved in the physical layer and the application layer. The optimal QoEW is achieved through the proposed CL alternate iteration algorithm. Numerical simulation results demonstrate the impacts of system parameters on QoEW and the effectiveness and superiority of the proposed algorithm.

Power and sub-channel optimization of JPEG 2000 image transmission over OFDM-based cognitive radio networks

Cognitive Radio is an efficient way of spectrum utilization since secondary users with bandwidth-demanding applications such as multimedia can get access to licensed frequency resources opportunistically and resolve their bandwidth limitations. Among all multimedia formats, JPEG 2000 is a suitable candidate for cognitive radio networks thanks to its unique features. In conventional resource allocations for Cognitive Radio systems, all data bits are assumed equally important. However, different parts of the JPEG 2000 bit stream have different contributions to the quality of the received image. Therefore, in this paper, an unequal power allocation method is used to allocate the available power to the coded bits based on their importance in the image quality. Simulated annealing method is used to solve the unequal power allocation problem. Furthermore, bits with higher significance are further protected by using sub-channels with better channel quality. Thus, the likelihood of significant bits being received correctly is increased. The optimal solution is obtained by minimizing the image distortion without violating the interference constraint to the primary users. The performance of the proposed method is demonstrated by simulation results.

Robust Power Control for OFDM-Based Cognitive Radio Networks with QoS Guarantee

Traditional power control schemes in Orthogonal Frequency Division Multiplexing based Cognitive Radio Networks (CRNs) are achieved under perfect channel state information (e.g., exact parameter information). Due to the effect of channel estimation errors and feedback delays, however, channel uncertainties are inevitable in practical CRNs. In this paper, considering bounded uncertainties of channel gains, a robust power control algorithm is proposed to minimize the total transmit power of Secondary Users (SUs) subject to interference power constraint of primary user and quality of service constraint of SU where the non-convex optimization problem is converted into a convex optimization problem that is solved by using robust optimization theory. Computer experiments demonstrate the effectiveness of the proposed algorithm by comparing with the non-robust algorithm in the aspect of suppressing the effect of parameter perturbation.

Simultaneous Power and Subchannel Allocation in Interference Limited OFDM-Based Cognitive Radio Network with Quality of Service Considerations

Despite the ever increasing demand for radio spectrum we are not able to achieve complete efficiency in utilizing the spectrum. The spectrum itself being restricted can only accommodate limited number of users. Therefore, it is very critical to efficiently utilize it. Cognitive radio is perceived as a crucial mechanism for improving spectral efficiency. In this paper we focus on allocating resources to secondary users (SU) which co-exist with primary user (PU) in orthogonal frequency division multiplexed based cognitive radio network. Simultaneous power and subchannel allocation for each SU is considered with the aim to maximize the total throughput or capacity of the system. We also aim to maximize the power efficiency of system and simultaneously keeping a check on total interference caused to PUs. The flexible rate demand for each SU is considered and hence categorized as real time and non real time secondary users. The problem is solved using non-dominant sorting genetic algorithm version II. The results procured are compared with existing solution and optimal solution.

Robust Power Allocation for OFDM-Based Cognitive Radio Networks: A Switched Affine Based Control Approach

In this paper, we investigate the robust power allocation issue in orthogonal frequency division multiplexing-based cognitive radio networks (CRNs) with unavoidable uncertainties (channel perturbations and variable environment). In this case, to control the performance degradation due to the uncertainties, we maximize the data rate of secondary users (SUs) by considering maximum allowable interference constraints and total power budget of SUs. To solve this problem, we design a controller for a switched affine system with state constraint. This system is based on a distributed projected dynamic system in accordance with the classical distributed convex optimization model for the power allocation and dynamic property of the Nash equilibrium. The robust controller design is on the basis of Lyapunov stability theory and linear matrix inequality to better realize the original power allocation from the control perspective. To the best of our knowledge, we are the first to solve the aforementioned problem by this kind of approach under the control frame, which is more practical for the realization in CRNs. Simulation results are provided to show the validation of the effectiveness of this approach in comparison with iterative water filling algorithm and the worst-case method.

A joint technique for sidelobe suppression and peak-to-average power ratio reduction in non-contiguous OFDM-based cognitive radio networks

ABSTRACTIn non-contiguous orthogonal frequency division multiplexing (NC-OFDM)-based interweave cognitive radio networks, the sidelobe power of secondary users (SUs) must be strictly controlled to avoid the interference between the SUs and the primary users (PUs) of the adjacent bands. Similarly, the inherent issue of high peak-to-average power ratio (PAPR) of the OFDM signal is another drawback of the cognitive radio communication system based on the NC-OFDM technology. A few methods are available in the literature to solve either of these problems individually, while in this paper, we propose a new method for the joint minimisation of sidelobe power and PAPR in NC-OFDM-based cognitive radio networks using Zadoff–Chu (ZC) sequence. In this method, the sidelobe power suppression of SUs is benefited by PUs and the PAPR is reduced for SUs. We modelled a new optimisation problem for minimising the sidelobe power with a constraint on the maximum tolerable PAPR and sidelobe power. The proper selection of ZC sequence, which is crucial for minimising both the issues simultaneously, is achieved by solving the proposed optimisation problem. The proposed technique is shown to provide 7 dB and 20 dB reduction in PAPR and sidelobe power, respectively, without causing any signal distortion along with the improvement in bit error rate (BER) performance.

A Proposed Sub-optimal Power Allocation using Simulated Annealing in Cognitive Radio Networks

Due to the rapid demand for wireless services and the increase in the wireless device count, there is a lack of available spectrum bands which constrain the further development of wireless communication .Therefore, Cognitive Radio (CR) has been adopted as a promising solution because of its ability to exploit the inefficiently used spectrum of licensed bands. Orthogonal Frequency Division Multiplexing (OFDM) become the enabling technique for CR due to its flexibility of allocating the available spectrum in dynamic environment. In this paper, a proposed distributed resource allocation framework based on Simulated Annealing (SA) algorithm for downlink OFDM-Based Cognitive Radio Network (CRN) will be applied. This algorithm gives less computational complexity for maximizing the total SUs transmission capacity. Moreover, the interference introduced from other Secondary Users (SUs) will be considered. For the sake of comparison, Lagrange dual method will be used. Simulation results showed that the proposed algorithm gives a better transmission capacity compared with Lagrange dual method. The parameters which are considered for comparison are maximum transmitted power, number of Primary Users (PUs) and number of SUs.

SBL-Based GLRT for Spectrum Sensing in OFDMA-Based Cognitive Radio Networks

In this letter, a novel sparse Bayesian learning (SBL)-based spectrum sensing technique is proposed for an orthogonal frequency-division multiple access-based cognitive radio network. The SBL framework is employed to acquire the temporally sparse channel estimates, which are subsequently incorporated in the generalized likelihood ratio test (GLRT) to obtain the novel decision statistics. In addition, two other GLRT-based schemes are developed, considering the known and unknown sparsity information, respectively, of the wireless multipath channel at the secondary user. In this context, closed form expressions are derived to characterize the theoretical probability of false-alarm and the probability of detection performance. Finally, some interesting inferences are presented with respect to the derived asymptotic GLRT bounds.

Link maintenance scheme for OFDM-based cognitive radio Ad Hoc networks

Transmission among the cognitive users (CUs)' is always interrupted by the primary users (PUs)' reclaim of the spectrum and the fading effect of wireless channels. To maintain reliable continuous communication among CUs, an efficient scheme for link maintenance in OFDM-based cognitive radio Ad Hoc networks is proposed in this paper. In the scheme, redundant sub-channels (RSC) are employed by CUs to tackle the outage problem caused by the PUs' reclaim of the spectrum and wireless fading effect in the transmission. Meanwhile, backup sub-channels (BSC) are arranged to help select sub-channels with better channel quality. Additionally, to lower the overhead and improve the accuracy of the spectrum sensing, a partial sensing module is designed to enable the CUs to inherit and update the original idle spectrum list until it expires. Simulation results show that the proposed scheme can realize higher throughput and lower sensing overhead with slight reduction of the forced termination probability (FTP) performance compared to the existing approaches.

Subband PUEA Detection and Mitigation in OFDM-Based Cognitive Radio Networks

This paper considers malicious user detection and energy harvesting in orthogonal frequency division multiplexing-based cognitive radio networks under primary user emulation attack (PUEA). The digital TV (DTV) model adopted is the second generation terrestrial digital television standard (DVB-T2). In this paper, first, we propose an efficient advanced encryption standard (AES)-based DTV scheme, where the existing reference sequence used to generate the P2 pilot symbols in the DVB-T2 frames is encrypted using the AES algorithm to facilitate accurate primary user and malicious user detection. With the proposed scheme, we can detect PUEA accurately over all subcarriers or subbands where the P2 symbols present. Second, we come up with an effective communication scheme for the secondary users (SUs) under PUEA by exploiting the energy harvesting techniques. Optimal power splitting is considered for sum-rate maximization. As the optimal solution relies on multidimensional exhaustive search, we propose an effective suboptimal solution with much lower computational complexity. It is observed that the sum-rate of the SU network can be improved significantly with the energy harvesting technique. Third, we evaluate the worst case PUEA interference in terms of minimizing the sum-rate for the SUs. We show that for practical systems, as channel state information among the SUs is not available to the malicious user, the worst jamming for the SUs is when the malicious user performs equal power allocation over all the white space subcarriers. Simulation results are provided to illustrate the proposed approaches.