Underlay Cognitive Radio Networks Research Articles

Outage Minimization of Energy Harvesting-Based Relay-Assisted Random Underlay Cognitive Radio Networks With Interference Cancellation

This paper incorporates interference cancellation in the outage analysis of a wireless energy harvesting cognitive relay network. Based on an interference threshold, primary receivers are assumed to be able to cancel a fraction of the strongest interferers in the primary network which often dominates the total interference. To achieve this, the coefficient of cancellation is adapted into the analysis to reduce the level of interference. The rationale is to improve the successful transmission probability of the primary network by cancelling interference at its receivers. Interestingly, this reduces the overhead incurred by the secondary network to guarantee the primary outage constraint. In this work, the optimal relay selection range is derived and deployed to minimize the secondary outage probability. Analytical results show that this approach can be used to significantly reduce the secondary outage probability which in turn improves the network performance. However, this is at the cost of reducing the energy harvesting success probability of the relays within the secondary network.

Secrecy Performance of SIMO Underlay Cognitive Radio Networks Over α – μ Fading Channels

This paper investigates the secrecy performance of a single-input multiple-output (SIMO) based underlay cognitive radio networks. In particular, a single-antenna secondary transmitter transmits its secret message to a multi-antenna equipped secondary node in the presence of a multi-antenna equipped passive eavesdropper under a peak interference constraint at the single-antenna primary receiver. We proficiently derive the expressions for secrecy outage probability (SOP), intercept probability, and ergodic secrecy capacity (ESC) for the considered system over $\alpha -\mu $ fading channels. Further, we carry out the asymptotic analysis of SOP and intercept probability under two cases of interest; 1) when the average signal-to-noise ratio (SNR) of the main link, i.e., between the secondary source and secondary destination, goes to infinity with a fixed average SNR of wiretap link between the secondary source and secondary eavesdropper, and 2) when the SNRs of both the main and wiretap links tend to infinity. We can infer that a secrecy diversity order of $\frac {\alpha _{D}N_{D}\mu _{D}}{2}$ can be achieved under case 1, where $\alpha _{D}$ and $\mu _{D}$ are the main link fading parameters, and $N_{D}$ denotes the number of antennas at the destination. Whereas, under case 2, the system’s secrecy diversity order becomes zero. Moreover, we also present some interesting findings for the ESC when the interference constrained secondary transmitter power is considerably high. Further, we validate our analytical framework through extensive simulation and numerical results, and demonstrate the effects of system/channel parameters on the secrecy performance of the considered system. Our results reveal that the higher number of eavesdropper antennas can have a more deleterious impact on the system’s secrecy performance.

Energy efficient transmission in underlay CR-NOMA networks enabled by reinforcement learning

In order to improve the energy efficiency (EE) in the underlay cognitive radio (CR)networks, a power allocation strategy based on an actor-critic reinforcement learning is proposed, where a cluster of cognitive users (CUs) can simultaneously access to the same primary spectrum band under the interference constraints of the primary user (PU), by employing the non-orthogonal multiple access (NOMA) technique. In the proposed scheme, the optimization of the power allocation is formulated as a non-convex optimization problem. Additionally, the power allocation for different CUs is based on the actor-critic reinforcement learning model, in which the weighted data rate is set as the reward function, and the generated action strategy (i.e. the power allocation) is iteratively criticized and updated. Both the CU's spectral efficiency and the PU's interference constrains are considered in the training of the actor-critic reinforcement learning. Furthermore, the first order Taylor approximation as well as other manipulations are adopted to solve the power allocation optimization problem for the sake of considering the conventional channel conditions. According to the simulation results, we find that our scheme could achieve a higher spectral efficiency for the CUs compared to a benchmark scheme without learning process as well as the existing Q-learning based method, while the resultant interference affecting the PU transmission can be maintained at a given tolerated limit.

Secrecy Analysis of Reconfigurable Underlay Cognitive Radio Networks With SWIPT and Imperfect CSI

In the present work, an underlay cognitive radio network (CRN) is considered to examine the secrecy performance for Simultaneous Wireless Information and Power Transfer (SWIPT) with imperfect channel state information (CSI). The eavesdropper as well as secondary receivers are considered to have energy harvesting potential. The eavesdropper is the malicious node that tries to sniff the information from the main link and degrades the performance of the system. The maximal ratio combining (MRC) technique is considered at all the nodes to optimize the signal from multiple antennas. Further, to deal with a more practical scenario, the path losses are also considered and therefore, the imperfect CSI is considered at all the receiving nodes. The main objective is to derive the exact, intercept and asymptotic secrecy outage probability for the Rayleigh fading channel. Moreover, the best antenna selection is also considered at the secondary transmitter for data transmission. The validation of the analytical result is done with the help of Monte-Carlo simulation.

Optimal Resource Allocation for RF-Powered Underlay Cognitive Radio Networks With Ambient Backscatter Communication

In this paper, we study radio frequency (RF)-powered underlay cognitive radio networks (CRNs) with power-domain non-orthogonal multiple access (NOMA). In these networks, by using the harvest-then-transmit (HTT) mode, secondary transmitters (STs) can use the harvested energy to simultaneously transmit data based on power-domain NOMA. However, in this mode, the throughput of the secondary system heavily depends not only on the harvested energy, but also on the stringent interference threshold imposed by the primary users. Furthermore, ambient backscatter communication (ABC) has been introduced as a promising technique which enables STs to transmit information by modulating and reflecting ambient RF signals. Therefore, it has the potentiality to be integrated into the RF-powered underlay CR-NOMA networks to improve the throughput of the secondary system. In these networks, each ST works on either the HTT mode or the ABC mode, but not simultaneously. In order to meet the interference constraint of the primary users, STs control their transmit power by finding the appropriate tradeoff between the HTT mode and the ABC mode. We formulate an optimization problem with the goal of achieving the maximum throughput by finding the optimal time resource allocation between the HTT mode and the ABC mode under the strict transmit power constraint at STs. Then the Lagrangian multiplier iterative algorithm is adopted to solve this optimization problem. Simulation results demonstrate that our proposed scheme can significantly improve the performance of the secondary system by comparing it with the other two baseline schemes.

Energy efficiency optimization-based resource allocation for underlay RF-CRN with residual energy and QoS guarantee

How to achieve energy-efficient transmission in radio frequency energy harvesting cognitive radio network (RF-CRN) is of great importance when nodes in CRN are self-maintained. This paper presents a radio frequency (RF) energy harvesting hardware-based underlay cognitive radio network (RH-CRN) structure, where a secondary transmitter (ST) first harvests energy from RF signals source originating from the primary network, and then communicates with a secondary receiver (SR) in underlay mode by using the harvested energy. The total consumed energy by the secondary user (SU) must be equal to or less than the total harvested energy referred to as energy causality constraint, In addition, the ST possesses some initial energy which may be the residual energy from the former transmission blocks, and we consider the energy loss of energy harvesting circuit as a systematic factor as well. Our goal is to achieve the maximum energy efficiency (EE) of the secondary network by jointly optimizing transmitting time and power. To guarantee the quality of service (QoS) of secondary transceiver, a minimum requirement of throughput constraint is imposed on the ST in the process of EE maximization. As the EE maximization is a nonlinear fractional programming problem, a quick iterative algorithm based on Dinkelbach’s method is proposed to achieve the optimal resource allocation. Simulation results show that the proposed strategy has fast convergence and can improve the system EE greatly while ensuring the QoS.

A Graph Convolutional Network-Based Deep Reinforcement Learning Approach for Resource Allocation in a Cognitive Radio Network.

Cognitive radio (CR) is a critical technique to solve the conflict between the explosive growth of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can effectively achieve spectrum sharing and co-channel interference (CCI) mitigation. In this paper, we propose a joint channel selection and power adaptation scheme for the underlay cognitive radio network (CRN), maximizing the data rate of all secondary users (SUs) while guaranteeing the quality of service (QoS) of primary users (PUs). To exploit the underlying topology of CRNs, we model the communication network as dynamic graphs, and the random walk is used to imitate the users’ movements. Considering the lack of accurate channel state information (CSI), we use the user distance distribution contained in the graph to estimate CSI. Moreover, the graph convolutional network (GCN) is employed to extract the crucial interference features. Further, an end-to-end learning model is designed to implement the following resource allocation task to avoid the split with mismatched features and tasks. Finally, the deep reinforcement learning (DRL) framework is adopted for model learning, to explore the optimal resource allocation strategy. The simulation results verify the feasibility and convergence of the proposed scheme, and prove that its performance is significantly improved.

Secrecy analysis of multiple‐input multiple‐output underlay cognitive radio networks with energy harvesting

AbstractThe cognitive radio (CR) technique is an encouraging approach to deal with spectrum scarcity in wireless networks. However, this technique raises the issues of secrecy and privacy in the CR network. In this work, a multiple‐input multiple‐output underlay CR network is considered, which consists of a primary network, a secondary network, and an eavesdropper. All the primary, secondary as well aseavesdropper's nodes are assumed to have multiple antennas with radio frequency energy harvesters to enhance their spectral and energy efficiency properties. The eavesdropper node manipulates it's simultaneous wireless information and power transfer architecture in a pursuit to intercept the secure communication between the cognitive transmitter and the cognitive receiver. This technique and considered network are combined with the traditional selection combining (SC) and maximal ratio combining (MRC) scheme and the results are compared. The Monte Carlo simulation is done for the validation of results. The main aim is to drive the closed‐form analytical expression for exact and asymptotic secrecy outage probability. The present results show that the SC scheme at eavesdropper provides better secrecy in comparison to the MRC technique and also the number of antennas affects the secrecy of the system. Moreover, the impact of power splitter is considered and also the optimal value of power splitter is derived to maximize information decoding.

Outage Analysis of Energy-Harvesting-Based Relay-Assisted Random Underlay Cognitive Radio Networks With Multihop Primary Transmissions

This article exploits the recent advances in wireless energy harvesting to develop a framework for the outage probability of energy harvesting underlay cognitive radio networks. In this model, N-hop primary users are equipped with constant energy source while secondary users (SUs) harvest energy from the transmissions of primary devices. The transmit power of secondary users is regulated to ensure that it does not violate the target end-to-end outage probability constraint of the N-hop primary network. Potential relays that have harvested sufficient energy are eligible to relay data for other SUs within the network. Using this model, we reveal the impacts of the number of primary hops on the relay selection region and harvested energy. An expression for the total outage probability that encapsulates the impact of N primary hops is also derived. Moreover, the effects of other relevant parameters on the outage probability are shown in detail.

Secrecy Performance of Cognitive Radio Sensor Networks Over $\alpha -\mu$ Fading Channels

This letter addresses the physical layer security for an underlay cognitive radio sensor network over α - μ fading channels, where α and μ denote the nonlinearity and clustering of fading channels, respectively. In the considered network, a secondary sensor transmitter communicates with the secondary sensor receiver in the presence of a primary sensor receiver, and a passive secondary eavesdropper intercepts the information. Specifically, the exact and asymptotic expressions for the secrecy outage probability are derived. Based on the asymptotic analysis, it is revealed that the system achieves a secrecy diversity order of α <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> μ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> /2 (α <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> and μ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> are fading parameters of the main link) when signal-to-noise ratio (SNR) of main link tends to infinity with fixed wiretap link SNR. However, the secrecy diversity order becomes zero when SNRs of both the main and wiretap links tend to infinity. Numerical and simulation results validate the proposed analytical studies, and demonstrate the impacts of channel conditions on the system secrecy performance.

A PSD Maps Estimation Algorithm for Underlay Cognitive Radio Networks Based on the Convolutional Neural Network

In the underlay cognitive radio networks (CRNs), the power spectral density (PSD) maps play a foundational role in detecting the idle radio resources. However, it is hard to get a high-accurate PSD map estimation result because of the complicated radio environment. For this reason, we propose a novel convolutional neural network- (CNN-) based PSD map estimation algorithm named map reconstruction CNN (MRCNN). Using the CNN to estimate PSD maps for underlay CRNs has not been reported until now. First, on the basis of the proposed color mapping process, we transform the PSD map estimation task to the image reconstruction task. Then, we train the MRCNN to learn the radio environment characteristics from the training data, rather than making direct biased or imprecise wireless environment hypotheses as in the conventional methods. We utilize the extracted knowledge in the training process to reconstruct the PSD map images. As demonstrated in the simulations, the proposed MRCNN method has a better PSD map estimation performance than the conventional methods.

Outage Performance of Power Beacon-Aided Multi-Hop Cooperative Cognitive Radio Protocol Under Constraint of Interference and Hardware Noises

In this paper, we evaluate end-to-end outage probability of a multi-hop decode-and-forward relaying protocol in underlay cognitive radio network. In the proposed protocol, named COOP, secondary nodes including source and relays have to harvest radio-frequency energy from multiple secondary power beacons, and adjust their transmit power, follows a pre-determined interference threshold given by multiple primary users. To enhance the outage performance for the secondary network under an joint constraint of the interference threshold, Rayleigh fading channel and hardware noises caused by imperfect transceiver hardware, the secondary relays on the source-destination path cooperate to forward the source data to the destination. Particularly, they attempt to receive the source data from their previous nodes, and forward it to the secondary destination if requested. Moreover, whenever the destination cannot receive the source data successfully, a successful relay that has the shortest distance to the destination is selected for retransmission. Due to usage of the cooperative transmission, the proposed COOP protocol obtains better performance, as compared with the corresponding multi-hop relaying one (denoted DIRECT) which only uses direct transmission at each hop. We evaluate the outage performance of COOP and DIRECT via both simulation and theory. The obtained results present a significant performance enhancement, as comparing COOP with DIRECT.

Secrecy analysis of a Cognitive Radio Network with an adaptive path selection based transmission scheme and JTAPS relay

This paper investigates the secrecy outage performance of a Cognitive Radio Network (CRN), where we propose an adaptive path selection based transmission scheme to transmit the information from the source to destination. The transmit power of the secondary user (SU) and relay is limited due to underlay CRN to maintain the maximum acceptable interference at the primary user (PU) receiver. The secondary network uses an intermediate relay between source and destination to harvest energy from the transmission of the radio frequency (RF) signal of SU following a joint time allocation and power splitting (JTAPS) protocol. Here we use the hybridization of two known energy harvesting (EH) protocols by combining the time allocation (TA) and power splitting (PS) factor to make more efficient use of the EH method in evaluating physical layer secrecy. We develop analytical expressions for secrecy outage under JTAPS and the effect of time allocation (TA) and power splitting (PS) factor on the secrecy outage probability (SOP) is investigated. Our proposed JTAPS scheme yields 12% reduction in SOP with respect to TSR scheme at an energy harvesting factor α=0.6.

A Radio Environment Maps Estimation Algorithm based on the Pixel Regression Framework for Underlay Cognitive Radio Networks Using Incomplete Training Data.

In the underlay cognitive radio networks, the radio environment maps (REMs) estimation is the main challenge in sensing the idle wireless spectrum resources. Traditional deep learning-based algorithms estimate the REMs on the basis of the high-quality, large-scale complete training images. However, collecting the complete radio environment images is time-consuming and requires a numerous number of power spectrum sensing nodes. For this reason, we propose a generative adversarial networks-based pixel regression framework (PRF) for underlay cognitive radio networks. The PRF algorithm relaxes the requirement of the complete training images, and estimates the radio environment maps only on the basis of the incomplete REMs images, which are easier to be collected. First, we transform the radio environment maps estimation task into a pixel regression task through the color mapping progress. Then, to extract helpful information from the incomplete training data, we design a feature enhancing module for the PRF algorithm, which intelligently learns and emphasizes the important features from the training images. Finally, we use the trained pixel regression framework to reconstruct the radio environment maps in the target area. The proposed algorithm learns accurate radio environment characteristics from the incomplete training data rather than making direct biased or imprecise radio propagation assumptions as in the traditional methods. Thus, the PRF algorithm has a better REMs reconstruction performance than the traditional methods, as verified by simulations.

Secure transmission using optimal antenna selection for MIMO underlay CRN with multiple primary users

The authors present a method of utilisation diversity combining to improve the secrecy performance of an underlay cognitive radio network (CRN) with single or multiple primary users (PUs) over Rayleigh fading environment. They consider a practical scenario where a secondary transmitter $({ST_X})$ ( S T X ) , a secondary receiver $({SR_X})$ ( S R X ) , and an eavesdropper (Eve) receiver are equipped with multiple antennas while all PUs are employed with a single antenna. The confidential information is transmitted from ${ST_X}$ S T X to ${SR_X}$ S R X under the malicious attempt of an Eve. Generalised selection combining is applied at the ${SR_X}$ S R X , and maximal ratio combining is adopted at the Eve. Depending upon the availability of the global channel state information of the main and eavesdropper's channels at ${ST_X}$ S T X , the secrecy outage performances of optimal antenna selection and sub-optimal antenna selection schemes have been examined. They derive a closed-form expression for the exact secrecy outage probability, asymptotic secrecy outage probability and intercept probability in a Rayleigh fading environment. Numerical results show that secrecy performance of the network improves with increasing the transmitting antennas while it degrades with increasing the number of PUs.

Transmit Power Policy and Ergodic Multicast Rate Analysis of Cognitive Radio Networks in Generalized Fading

This paper determines the optimum secondary user (SU) power allocation and ergodic multicast rate of point-to-multipoint communication in a cognitive radio network (CRN) in the presence of various quality of service (QoS) constraints for the primary users (PUs). Using tools from extreme value theory (EVT), it is first proved that the limiting distribution of the minimum of independent and identically distributed (i.i.d.) signal-to-interference ratio (SIR) random variables (RVs) is a Weibull distribution, when the user signal and the interferer signals undergo independent and non-identically distributed (i.n.i.d.) $\kappa -\mu $ shadowed fading. Also, the rate of convergence of the actual minimum distribution to the Weibull distribution is derived. This limiting distribution is then used for determining the optimum transmit power of a secondary network in an underlay CRN subject to three different QoS constraints at the primary network in a generalized fading scenario. Furthermore, the optimum transmit power and the asymptotic ergodic multicast rate of SUs is analyzed for varying channel fading parameters.

Error Performance of NOMA-Based Cognitive Radio Networks With Partial Relay Selection and Interference Power Constraints

Non-orthogonal multiple access (NOMA)-based cognitive radio (CR) networks have recently emerged as a promising solution to enhance the spectral efficiency and massive connectivity problems. In this paper, we investigate the error rate performance of relay-assisted NOMA with partial relay selection in an underlay cognitive radio network. In this setup, $K$ relays are used to assist in transmission between secondary NOMA users and a secondary base station (SBS), where the relay (R) with the strongest link with the SBS is selected to amplify-and-forward (AF) its received signals to the secondary receivers. We derive an accurate approximation for the pairwise error probability (PEP) of the secondary users with imperfect successive interference cancellation (SIC). Subsequently, the derived PEP expression is utilized to deduce a union bound, which is considered as an upper bound on the bit error rate (BER). We further formulate an optimization problem to calculate the optimum power coefficients that minimize the derived union bound. Numerical and Monte Carlo simulation results are presented to corroborate the derived analytical expressions and give some useful insights into the error rate performance of each user.

Performance Analysis for Multihop Cognitive Radio Networks With Energy Harvesting by Using Stochastic Geometry

Cognitive multihop relaying has been widely considered for device-to-device (D2D) communications for applications in the physical layer of the Internet of Things. In this article, we construct a multihop cellular D2D communications system model with energy harvesting (EH) in underlay cognitive radio networks. The locations of primary user equipments (PUEs) and cellular base stations are considered as a Poisson point process in this model. The transmit power of secondary devices is collected from the power beacon with time-switching EH policy. Two charging policies for different applications are considered in this article. Then, the end-to-end outage probability analysis expressions of these two scenarios for the transmission scheme subject to interferences from PUEs are derived. The optimal harvesting time ratio is obtained to get the maximum capacity for end-to-end D2D communications. The analytical results are validated by performing the Monte Carlo simulation of the end-to-end outage probability, which is based on the half-duplex transmission scheme. The results of this article provide a potential pathway to reduce reliance on grid or battery energy supplies and, hence, further strengthen the benefits for the environment and deployment of future smart devices.

A Power Spectrum Maps Estimation Algorithm Based on Generative Adversarial Networks for Underlay Cognitive Radio Networks.

In the underlay cognitive radio networks, the main challenge in detecting the idle radio resources is to estimate the power spectrum maps (PSMs), where the radio propagation characteristics are hard to obtain. For this reason, we propose a novel PSMs estimation algorithm based on the generative adversarial networks (GANs). First, we constructed the PSMs estimation model as a regression model in deep learning. Then, we converted the estimation task into an image reconstruction task by image color mapping. We fulfilled the above task by designing an image generator and an image discriminator in the proposed maps’ estimation GANs (MEGANs). The generator is trained to extract the radio propagation characteristics and generate the PSMs images. However, the discriminator is trained to identify the generated images and help to improve the generator’s performance. With the training process of MEGANs, the abilities of the generator and the discriminator are enhanced continually until reaching a balance, which means a high-accuracy PSMs estimation is achieved. The proposed MEGANs algorithm learns and utilizes accurate radio propagation features from the training process rather than making direct imprecise or biased propagation assumptions as in the traditional methods. Simulation results demonstrate that the MEGANs algorithm provides a more accurate estimation performance than the conventional methods.

End-to-end performance of Hybrid DF/AF (HDAF) relayed underlay cognitive radio networks

In the presented work, the performance analysis of an underlay cognitive relay network with a hybrid-decode/amplify-and-forward relaying protocol is undertaken. Since the system is an underlay system, the secondary network will communicate simultaneously with the primary network. Hence interference temperature will act as a constraint to keep the secondary communication from interfering with the primary transmission. To ensure this, secondary transmit powers need to be kept below a pre-defined threshold level. Severe multi-path fading and shadowing or a long distance between the secondary source and the destination make a direct link between the two infeasible. These nodes can only be linked by a relay between the two. All the radio links in the system are characterized by the extended generalized-K fading distribution. The closed form expressions for the outage probability and ergodic channel capacity of the system are derived. The system outage performance and capacity with hybrid-decode/amplify-and-forward relay is compared to the performance with amplify-and-forward and decode-and-forward relays. Optimum relay position for best outage performance is also investigated. Further, the results of the performance analysis of the system are also illustrated by the numerical plots.