Primary User Network Research Articles

Robust transmission for multi-user OFDM-based IRS-assisted cognitive radio networks

In cognitive radio networks (CRNs) employing underlay multi-user scenarios, intelligent reflecting surface (IRS) plays a crucial function in augmenting total transmission rate for cognitive radio (CR) users, while adhering to restrictions caused by the interference from secondary users (SUs) to primary users (PUs). However, due to the passive characteristics of IRS, it operates in a non-cooperative relationship with both PU and SU network, resulting in delayed information acquisition and channel errors. Therefore, in this research, considering channel uncertainties, limited interference, and interference among SUs, we put forward a robust power and subcarrier allocation strategy for orthogonal frequency division multiplexing (OFDM) with IRS-assisted CRNs in a multi-user scenario. Formulated as a resource allocation (RA) problem in an underlay spectrum sharing mode for multi-user CR, the joint optimization problem encompasses subcarrier allocation and power allocation for SUs, a phase shift matrix for IRS, and minimum rate constraints. We transform the problem using worst-case criterion into a deterministic convex optimization problem to handle the constraints of bounded channel uncertainties. By utilizing matching theory, variable substitution, scaling methods, convex-concave programming (CCP), and alternating optimization techniques, the converted non-convex problem is transformed into a convex one. Simulation results showcase that the proposed algorithm achieves significant gains in transmission rate and exhibits robustness.

A Petri Net Model for Cognitive Radio Internet of Things Networks Exploiting GSM Bands

Quality of service (QoS) is a crucial requirement in distributed applications. Internet of Things architectures have become a widely used approach in many application domains, from Industry 4.0 to smart agriculture; thus, it is crucial to develop appropriate methodologies for managing QoS in such contexts. In an overcrowded spectrum scenario, cognitive radio technology could be an effective methodology for improving QoS requirements. In order to evaluate QoS in the context of a cognitive radio Internet of Things network, we propose a Petri net-based model that evaluates the cognitive radio environment and operates in a 200 kHz GSM/EDGE transponder band. The model is quite flexible as it considers several circuit and packet switching primary user network loads and configurations and several secondary user types of services (that involve semantic transparency or time transparency); furthermore, it is able to take into account mistakes of the spectrum sensing algorithm used by secondary users. Specifically, we derive the distribution of the response time perceived by the secondary users, where it is then possible to obtain an estimation of both the maximum throughput and jitter. The proposed cognitive radio scenario considers a secondary user synchronized access to the channel when using the GSM/EDGE frame structure.

Deep Reinforcement Learning Optimal Transmission Algorithm for Cognitive Internet of Things With RF Energy Harvesting

Spectrum scarcity and energy limitation are becoming two critical issues in designing Internet of Things (IoT). As two promising technologies, cognitive radio (CR) and radio frequency (RF) energy harvesting can be used together to improve both energy and spectral efficiency. In this paper, an optimal transmission problem in a cognitive IoT (CIoT) with RF energy harvesting capability is investigated, where the optimization problem is formulated as a Markov decision process (MDP) without any priori-knowledge. Considering that the channel activity states of primary user network (PUN), RF energy arrival process and channel information are not available in advance, a deep reinforcement learning (DRL) based deep deterministic policy gradient (DDPG) algorithm is proposed to deal with the dynamic uplink access, working mode selection and continuous power allocation to maximize a long term uplink throughput. The simulation results show that the proposed algorithm is valid and efficient to achieve better performances when compared with deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -network (DQN) based, myopic and random algorithms.

A novel method for the optimization of Spectral -Energy efficiency tradeoff in 5 G heterogeneous Cognitive Radio Network

Cognitve Radio (CR) technology has recently become popular for future communication networks since it allows co-existence of Secondary User (SU) and Primary User (PU) networks. Compared to interweave and overlay, underlay mode of operation is best suited for practical scenarios since it facilitates PU-SU co-existence with minimum overhead. Aiming to enhance Spectral Efficiency (SE) alone using CR may lead to huge power consumption and thus increase the overall cost of network operation. Hence, effort must be taken to maintain a tradeoff between SE and Energy Efficiency (EE). In this work, a novel technique, called Superior Population Generation Algorithm (SPGA), is developed to solve the crucial problem of SE-EE tradeoff for a Fifth generation (5 G) heterogeneous network which uses key future technologies such as CR and Carrier Aggregation (CA). The proposed technique performs optimum resource allocation, by modifying regular Genetic Algorithm (GA) to incorporate the capabilities of conventional techniques. SPGA follows a multi objective optimization approach towards obtaining SE-EE tradeoff, rather than focusing on SE alone. Simulation results show that SPGA outperforms conventional methods and regular GA in terms of SE-EE tradeoff with about 5% and 10% better EE for the same SE respectively. It is also observed that SPGA converges to the Pareto optimal solutions, much faster using only 25% of iterations required for regular GA. All simulations in this work are performed according to 3GPP specifications for 5 G communication networks. Also, power consumption of various base station components is also considered in addition to the transmit power to make the EE calculations further close to reality.

Statistical Modelling of Dynamic Interference Threshold and Its Effect on Network Capacity

In this paper, we present the case of utilizing interference temperature (IT) as a dynamic quantity rather than as a fixed quantity in an orthogonal frequency division multiple access (OFDMA) based spectrum sharing systems. The fundamental idea here is to reflect the changing capacity demand of primary user (PU) over time in setting the interference power threshold for secondary user (SU). This type of dynamic IT will allow the SU to opportunistically have higher transmit power during relaxed IT period, thereby resulting in higher network capacity. The cognitive radio network (CRN) considered in this paper has an underlay network configuration in which the available spectrum of the PU is accessed concurrently by SU provided that the interference power at the PU receiver from SU is under a certain power threshold. This power threshold is set to maintain and guarantee a certain level of quality of service (QoS) for PU network. Theoretical expressions for outage probability and mean capacity for SU network are derived, and validated with simulation results, and it is observed that utilizing dynamic IT results in high network performance gains as compared to utilizing a fixed IT in cognitive radio system.

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.

Performance evaluation of combining SMT‐ETX metric with POS scheme for implementing multilayer multicast mobile ad hoc network (MANET) CRN

In this study, the implementation of a multicast router protocol with a multilayer and multi-hop consideration on the ad hoc CRN mobile was achieved through the use of a combined expected transmission count metric (ETX) with the Steiner minimal tree (SMT). The conjoined SMT-ETX multicast CRN channel spectrums, which were used for measuring the availability of channels and the time required for data transmission from a given cognitive radio (CR) source to all of the CR destinations nodes was found to have resulted in the most efficient channel that was largely based on the possibility of success (POS) channel assignment scheme's probability success. Apart from demonstrating the best throughput and packet delivery rate performances for SMT-ETX multicast CRN with POS scheme than those of the three multicast router protocol (MASA, MDR and RS) schemes with different primary users (PUs) traffic load and network parameters at Rayleigh fading channel, the SMT-ETX multicast CRN with the decreasing PUs traffic load had also implied a higher chance of achieving the available secondary users channels in the CR network.

NOMA in Cooperative Underlay Cognitive Radio Networks Under Imperfect SIC

In conventional cognitive radio (CR), the users in secondary network (SN) can only access the idle spectrum when users in primary network (PN) are absent. This novel strategy provides higher spectrum efficiency when detecting the presence of the PN. Hence, spectrum utilization of the traditional scheme can be further improved as exploiting application of non-orthogonal multiple access (NOMA). As combination of CR and NOMA, such CR-NOMA has been proposed to improve spectrum efficiency to adapt to requirements in 5G communications. In this study, the relaying scheme is employed in the SN of the proposed CR-NOMA and the relay is allowed to energy harvesting (EH) from the secondary transmitter to serve signal forwarding to distant secondary users. With this regard, the complex model of EH-assisted CR-NOMA is explored in outage behavior and throughput performance as awareness on imperfect successive interference cancellation (SIC) at the receiver. As most important results, the exact closed-form of the exact outage probability is derived for each NOMA destination by assuming that the channel coefficients among considered links follow Rayleigh distribution. Furthermore, performance gap between two NOMA users can be controlled by various parameters such as transmit power, energy harvesting coefficients and levels of imperfect SIC. Simulation results verify our analytical results.

Orthogonal codes-based dynamic spectrum access in cognitive radio networks

Dynamic spectrum access (DSA) in cognitive radio (CR) networks became a challenging research area recently. In CR technology, the DSA between primary users (PUs) and secondary users (SUs) simultaneously can be achieved without degrading the performance of the PUs by SUs interference. This can be achieved by donating incentive power to the PUs in order to compensate the interference caused by the SUs. Consequently, PUs allow SUs to share the spectrum. In this paper, orthogonal codes-based dynamic spectrum access (OC-DSA) technique is proposed. OC-DSA technique employs orthogonality between PUs and SUs transmitted data symbols in addition to the incentive power donation. Compared to other techniques, the proposed technique uses a simple encoder at the SU network for the same PU network infrastructure. By applying orthogonal codes, the interference caused by SUs is canceled and hence the donated power to incentivize the PUs is reduced. Also, the SU packet rate is increased significantly. The simulation results show that the proposed technique provides effective improvements over other existing techniques in the signal strength and the bit error rate performance of both the PU network and the SU network at the receiver side. Moreover, the proposed technique requires less donated power to incentivize the PU and has higher packet rate.

A Survey and Performance Evaluation of Reinforcement Learning Based Spectrum Aware Routing in Cognitive Radio Ad Hoc Networks

Cognitive radio technology is an assuring solution for under-utilization of licensed spectrum bands and overcrowding of unlicensed spectrum bands, in which secondary user is permitted to access the primary users’ spectrum in an opportunistic manner. Opportunistic access of the spectrum requires complex changes across all the layers of a network protocol stack. Cognitive radio has to be an autonomous agent in order to configure itself to dynamic spectrum environment. And, the characteristics of reinforcement learning, a subfield of artificial intelligence in which the agent learns the surrounding operating environment through continuous interaction and takes an optimum decision on the fly, is in compliance with features of self-organized cognitive radio ad hoc network. Therefore, reinforcement learning is an appropriate option for incorporating intelligence and self-adaptivity into cognitive radio. This paper provides a comprehensive survey on the application of reinforcement learning for efficient spectrum aware routing in cognitive radio ad hoc network. The preliminaries of cognitive radio ad hoc networks and reinforcement learning are first introduced, and a review is investigated in the proposed research area along with a discussion on open research challenges with an aim to promote research. From the survey, reinforcement learning incorporated cognitive radio can learn the unknown primary user network model and the learned model can be then used for finding a suitable route to meet the Quality of Service requirements. With this in mind, the paper also proposes a multi-objective reinforcement learning based spectrum aware routing protocol with an aim to maximize the probability of successful transmission using a minimum hop path. The simulated results prove the performance of the algorithm.

Generalized FFT-Based One-Bit Quantization System for Wideband Spectrum Sensing

In this paper, we maximize the benefits from the ultra low power wideband sensing approach based on FFT-based 1-bit quantization by addressing the practical limitations to this method. Unlike the conventional architecture that assumes a fully synchronized and coordinated Primary User (PU) network, the proposed system relaxes these limitations by providing an analytical framework for an uncoordinated asynchronous FFT-based 1-bit quantization system. For this system, we analytically derive the sub-band power which is the main parameter for the closed-form expressions representing the false alarm and detection probabilities. Further, improving the system performance through cooperative sensing is considered. While respecting the decision fusion cooperation, the optimum threshold for the generalized FFT-based 1-bit quantization system is derived such that the aggregate error rate is minimized. In addition to its significant power and complexity reduction, the presented analysis expands the use of the FFT-based 1-bit quantization wideband sensing approach in practical deployments. The sensing performance and the analytical results are assessed through comparisons with respective results from computer simulations.

Hybrid ARQ-CQI Feedback-Based Access Scheme in Cognitive Radio Networks

In this paper, we consider a cognitive radio (CR) network where the primary network’s feedback information is utilized to develop an access scheme for the secondary network to exploit the underutilized primary spectrum resources. Secondary users (SUs) identify the spectrum opportunities by sensing the spectrum for primary users (PUs) activities and by listening to the PUs feedback. The feedback signals monitored in this research work are the channel quality indicator (CQI) and automatic repeat request (ARQ) available in the PUs network. For detecting the PUs activities, SUs employ soft energy sensing, where SUs access the PUs’ channel with access probabilities that are based on the sensed PUs’ energy level. The access probabilities are optimized to maximize the SUs service rate while maintaining the PUs queues’ stability. The system is modeled as a three-dimensional Markov chain (MC) that captures the number of packets in the PUs queues and the state of the two observed PUs’ feedback signals. The performance of the system is evaluated by deriving the SUs service rate and the average PUs packet delay. We compare the performance of the proposed system with other baseline systems utilizing different types of PUs’ feedback signals. Results reveal the improvement in the SUs service rate and the PUs’ delay of the proposed system compared to the baseline systems. This improvement is mainly due to the fact that in our proposed system SUs have access to extra information, in terms of PUs feedback, as compared to other systems. Therefore, SUs in our proposed system can have better inference on the PUs’ activities; thus more collisions between the PUs and the SUs can be avoided, resulting in significant performance gains in terms of SUs’ throughput and PUs’ average delay.Part of this work was published in [1] .

Machine Learning Meets Point Process: Spatial Spectrum Sensing in User-Centric Networks

This letter introduces a novel machine learning (ML)-based approach to approximate the distributions of the aggregated interference power in wireless networks. We focus on the application of spatial spectrum sensing (SSS) in user-centric networks where Poisson cluster process (PCP) is used to model the primary users. A nonlinear regression method, i.e., kernel regression, is introduced to learn the distributions of the aggregated interference power of the PCP modeled primary user network. Simulation results demonstrate the accuracy of our approach.

EVM based rate maximized relay selection for cooperative cognitive radio networks

Cooperative relaying being a novel technique in cognitive radio (CR) communications promises enhanced performance for secondary users (SU) by exploiting spatial diversity. This paper proposes a cooperative framework for the rate maximized relay selection (RRS) based on error vector magnitude (EVM). EVM is the measure of error vector between received constellation points to the ideal ones. The problem is transformed to convex optimization (CO) problem for maximizing the capacity for both primary user (PU) and SU network in a interweave communication model. Simulation result reveals that PU can achieve higher capacity with cooperative relaying compared to the direct transmission and energy constraint of the cognitive radio network (CRN) is met by implementing RRS algorithm. Energy efficiency is improved up to 19% more than the conventional schemes. Furthermore, PU provides the opportunistic time gain (OTG) for those SU’s that act as relay for its transmission.

Enhanced 5G Cognitive Radio Networks Based on Spectrum Sharing and Spectrum Aggregation

In this paper, new enhanced cognitive radio networks (E-CRNs) based on spectrum sharing (SS) and spectrum aggregation (SA) are proposed for fifth generation (5G) wireless networks. The E-CRNs jointly exploit the licensed spectrum shared with the primary user (PU) networks and the unlicensed spectrum aggregated from the industrial, scientific, and medical bands. The PU networks include TV systems in TV white space and different incumbent systems in the long term evolution time division duplexing bands. The harmful interference from the E-CRNs to the PU networks are delicately controlled. Furthermore, the coexistence between the E-CRNs and other unlicensed systems, such as WiFi, is studied. The E-CRNs framework including dynamic spectrum management (DSM) is designed for the key parameters of licensed SS and unlicensed SA. The essential tradeoff between sharing efficiency and aggregation efficiency for the E-CRNs is discussed. Based on this tradeoff, a spectrum lean-management scheme is proposed to fulfill the DSM. Moreover, a water-filling algorithm is designed to dynamically access the available spectrum. Numerical results demonstrate that the proposed E-CRNs can significantly improve the system performance in terms of data rate, outage probability, and spectrum efficiency. In particular, the E-CRNs framework provides a spectrum usage prototype for 5G wireless communication networks.

Outage analysis for multiuser underlay cognitive TWRN with antenna selection and user scheduling

This paper investigates the outage performance of cellular two-way amplify-and-forward (AF) cooperative spectrum sharing systems in the presence of primary user (PU) network and multiple secondary user (SU) destinations. A general Nakagami-m fading environment has been used, where distinct fading parameters, as well as unequal average fading powers between the interference and relaying links, are assumed. Focusing on the cooperation process among the SU nodes and making use of the underlay cognitive approach, an approximate closed-form expression for the overall outage probability (OOP) of the secondary network is derived. In this analysis, an opportunistic scheduling and transmit antenna selection (TAS) algorithm in the secondary network has been employed. Considering the interference constraints due to the presence of PU receiver, power allocation of secondary user is done. The effects of fading severity, secondary network relay placement, the number of PU & SU transmit antenna, and number of SUs on the end-to-end system performance are examined through some representative numerical plots. Moreover, to minimize the secondary network OOP, we investigate the optimization problems of relay position. Monte Carlo simulation results are presented to corroborate the proposed analysis.

Optimal Spectrum Sensing Policy in RF-Powered Cognitive Radio Networks

An orthogonal frequency division multiple access-based primary user (PU) network is considered, which provides different spectral access/energy harvesting opportunities in RF-powered cognitive radio networks. In this scenario, we propose an optimal spectrum sensing policy for opportunistic spectrum access/energy harvesting under both the PU collision and energy causality constraints. PU subchannels can have different traffic patterns and exhibit distinct idle/busy frequencies, due to which the spectral access/energy harvesting opportunities are application specific. Secondary user (SU) collects traffic pattern information through observation of the PU subchannels and classifies the idle/busy period statistics for each subchannel. Based on the statistics, we invoke stochastic models for evaluating SU capacity by which the energy detection threshold for spectrum sensing can be adjusted with higher sensing accuracy. To this end, we employ the Markov decision process model obtained by quantizing the amount of SU battery and the duty cycle model obtained by the ratio of average harvested energy and energy consumption rates. We demonstrate the effectiveness of the proposed stochastic models through comparison with the optimal one obtained from an exhaustive method.

Impact of residual time distributions of spectrum holes on spectrum handoff performance with finite switching delay in cognitive radio networks

Cognitive radio technology ascends as a mean to improve the overall spectrum efficiency by exploring the spectrum opportunity and sharing the available channels with secondary users or cognitive users without causing harmful interference to either of the networks. Spectrum handoff is appeared to be a key strategy in order to ensure the required QoS of the primary user network and increase the overall performance of Cognitive Radio Network (CRN). In this paper, we investigate the effect of finite handoff or switching delay on the performance of spectrum mobility in a cognitive radio network. We establish an analytical model for the probability of spectrum handoff and develop an analytical formula of average spectrum handoff of a cognitive user for general residual time distributions of spectrum holes considering finite switching delay. The impact of switching delay and residual time distributions of spectrum holes on performance measuring metrics are investigated. This paper also presents a detailed comparison of results between the proposed time relationship model of spectrum handoff with and without finite switching delay in cognitive radio networks. A close proximity of the simulation result with the analytical one validates the accuracy of the proposed model.

Cooperative Spectrum Sensing With Heterogeneous Devices: Hard Combining Versus Soft Combining

Practical cognitive radio networks (CRNs) would have users with different capabilities and access levels to prior information about the primary users (PUs). For instance, some users may have the privilege to access information about the PU network, which can be utilized to increase the detection performance, whereas others may need to use simple detectors due to energy consumptions constraints, etc. This level of heterogeneity must be accommodated to enable coexistence of different secondary users’(SUs) networks. This paper presents performance analysis and comparison of hard and soft combining cooperative spectrum sensing schemes in heterogeneous CRNs. A centralized approach is considered for cooperative spectrum sensing with SUs that may use either energy detector, cyclostationary detector, pilot-based detector, or orthogonal frequency division multiplexing-based detector. For hard combining, each cooperative SU senses the spectrum using one of the available detectors and reports one-bit local decision to the fusion center which then applies $m$ -out-of- $K$ rule for the final decision. For soft combining, we derive an optimal soft combining scheme based on the Neyman–Pearson criterion. Then, we drive a suboptimal rule to reduce the complexity of the proposed scheme. For the sake of simplicity, we consider only energy and cyclostationary detectors for soft combining. In addition, we study the impact of the cyclic frequency offset in case of a cyclostationary detector. Finally, the paper is supported with extensive simulation results that demonstrate the performance of proposed schemes in terms of probability of detection and probability of false alarm.

Performance of an Energy harvesting Cooperative Cognitive Radio Network with Hybrid Spectrum Access Scheme

In this paper, a hybrid spectrum access scheme is investigated for a relay based energy harvesting cooperative cognitive radio (CR) network. In the considered network model, a relay harvests energy from RF signal of primary user (PU) as well as from RF signal of CR also. The relay node forwards the CR data to the respective destination and also cooperates in PU transmission as well. The CR follows a hybrid spectrum access scheme, i.e., it employs an underlay scheme of co-existence if PU is found to be present in the given channel else it switches to the overlay scheme of transmission. In overlay scheme, a CR transmits with maximum transmission power while in underlay scheme, it controls its transmission power based on the knowledge of channel state information of the link between CR and PU receiver, to limit the interference at PU receiver. Three new ratio parameters namely, PU–CR time allocation parameter, PU two hop transmission parameter and CR two hop transmission parameter are considered to model the cooperative transmission of PU and CR network where the parameter β takes care of the allocated time for PU and CR transmission and the other two parameters θ and ϕ take care of two hop transmission for PU and CR respectively. Analytical expressions of combined throughput of a CR and PU are developed under the considered network scenario. The performance is investigated in terms of combined throughput of CR, combined throughput of PU, and the overall network throughput for several sensing parameter and new ratio parameters.