Cognitive Radio Scenario Research Articles

Wireless powered NOMA-based cognitive radio for 6G networks

The need for physically charging mobile devices is anticipated to become a thing of the past in the not-too-distant future. In this paper, a novel approach for wireless-powered mobile devices is presented, which is based on non-orthogonal multiple access (NOMA) and multi-user multiple-input multiple-output (MU-MIMO) antenna systems. The proposed method is specifically designed for use in a cognitive underlay radio (CR) scenario, where the primary network requires a certain quality of service (QoS). Meanwhile, secondary network users can download their data using the same spectrum. The main objective of this paper is to maximize the sum rate of the secondary network. We have optimized a joint beamforming vector for both primary and secondary networks and a time-switching coefficient for energy harvesting and information transfer to achieve this objective. The initial problem formulation was non-convex, requiring the implementation of various techniques to ensure an efficient and low-complexity algorithm. To this end, we employed semi-definite programming, successive convex approximation, and alternating optimization. The proposed NOMA-based solution outperforms the TDMA-based benchmark scheme regarding sum data rate, specifically in the low transmit power region.

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.

Energy efficiency in cognitive cooperative amplify and forward networks

The emphasis on improving energy efficiency has gradually replaced that on network throughput or data rate alone in the past, because the former not only concerns about network throughput or data rate, but also concentrate on energy consumption according to the goal of green and low carbon nowadays. In the context of increasingly clamoring environmental protection, this paper delivers the energy efficiency problem in amplify and forward cooperative relay networks, and then extends this problem to the cognitive radio scenarios. Traditional Dinkelbach parametrized method needs to transform the original problem into a series of nonlinear iterative problems. On the surface, this method simplifies the original energy efficiency optimization problem, but it often requires multiple iterations, and the number of iterations cannot be predicted in advance. In order to get rid of this dilemma, this paper directly starts with the partial derivative of the original problem, and obtains a closed-form solution of power distribution based on the Lambert function, instead of the water-filling form in traditional method. Then this Lambert result is extended to the cognitive radio cooperative relay system under the dual constraints of power budget and interference threshold. Finally, the performance of energy efficiency is confirmed by a large number of simulation experiments for distinct parameter settings.

Differential Privacy in Cognitive Radio Networks: A Comprehensive Survey

Integrating cognitive radio (CR) with traditional wireless networks is helping solve the problem of spectrum scarcity in an efficient manner. The opportunistic and dynamic spectrum access features of CR provide the functionality to its unlicensed users to utilize the underutilized spectrum at the time of need because CR nodes can sense vacant bands of spectrum and can also access them to carry out communication. Various capabilities of CR nodes depend upon efficient and continuous reporting of data with each other and centralized base stations, which in turn can cause leakage in privacy. Experimental studies have shown that the privacy of CR users can be compromised easily during the cognition cycle, because they are knowingly or unknowingly sharing various personally identifiable information (PII), such as location, device ID, signal status, etc. In order to preserve this privacy leakage, various privacy preserving strategies have been developed by researchers, and according to us differential privacy is the most significant among them. In this article, we provide a thorough survey on how differential privacy can play an active role in preserving privacy of cognitive radio networks (CRN). Firstly, we provide a thorough comparison of our work with other similar studies to show its novelty and contribution, and afterwards, we provide a thorough analysis from the perspective of various CR scenarios which can cause privacy leakage. After that, we carry out an in-depth assessment from the perspective of integration of differential privacy at different levels of CRN. Then, we discuss various parameters which should be considered while integrating differential privacy in CRN alongside providing a comprehensive discussion about all integrations of differential privacy carried out till date. Finally, we provide discussion about prospective applications, challenges, and future research directions. The discussion about integration of differential privacy in different CR scenarios indicates that differential privacy is one of the most viable mechanisms to preserve privacy of CRN in modern day scenarios. From the discussion in the article, it is evident that the proposed integration of differential privacy can pave the way for futuristic CRN in which CR users will be able to share information during the cognition cycle without the risk of losing their private information.

Cross-layer design based hybrid MAC protocol for cognitive radio network

A minimal interrupted communication link setup is the primary objective of the MAC layer. The MAC layer is responsible for accessing the communication channel. At MAC, a control channel is used in the selection of collision free paths for data transfer. Therefore, the design of the control channel plays a pivotal role in achieving desired QoS in cognitive radio (CR) technology. Various schemes of control channel design help the CR network (CRN) to obtain better performance. The reported work focuses on a hybrid MAC protocol. The novelty of the scheme lies in the process of hybridization. A cross-layer framework is proposed for hybridization. The cross-layering has been done between network and MAC layer to achieve hybridization between different control channel design approaches. The broad categorization of control channel designs is between licensed in-band and dedicated unlicensed out-band approaches. In the in-band control channel design approach, the opportunistic use of data channel as control channel fulfills the decorum of CR technology. As soon as the primary user activity rises in the data channels, the in-band approach suffers from poor performance. On the other hand, the dedicated unlicensed out-band control channel design approach provides global coverage and all-time availability but suffers from channel saturation and intruder attacks. Interference in the control channel limits the use of out-band design. This motivates authors to develop a hybrid MAC protocol that can float between licensed in-band design and unlicensed out-band design to access the control channel. The hybridization is possible by sharing a primary user free channel list (PCL) among CR nodes. In conventional hybrid MAC protocols, the PCL is shared as a control beacon in the channel. Extra packet requirement as control beacon affects the performance of CR scenario. The proposed cross-layer design based hybrid MAC protocol avoids the need of an extra control beacon for PCL transmission. Further, the hybridization helps in achieving advantages of both in-band and out-band control channel design approaches. The simulation results show that the proposed hybrid MAC protocol performs satisfactorily in terms of packet delivery ratio, average throughput, average delay and control overhead. The performances are also tested in the worst scenarios.

Energy Efficient Power Allocation with Antenna Selection for Interference Alignment based MIMO-OFDM Cognitive Radio Networks

Energy efficient communications in cognitive radio (CR) networks has been of great interest. In order to address the power consumption problem and to maximise the energy efficiency (EE) in MIMO-OFDM underlay CR systems, power allocation (PA) and antenna selection (AS) algorithms are proposed. Interference alignment, which helps in mitigating the interference at primary and secondary receivers, is considered for setting-up the underlay CR scenario. The AS transmit is employed for the secondary users (SUs) on a per-subcarrier basis to improve their QoS. Further, to improve the QoS of both primary user (PU) and SUs, the AS is followed by energy efficient PA. Due to the fractional nature of EE, the EE maximisation problem is transformed into an equivalent parametric optimisation problem based on Dinkelbach method. Simulation results confirm that the proposed EE aware algorithms can improve the EE performance of both PU and SUs than the SE aware algorithms.

Energy efficient power allocation with antenna selection for interference alignment-based MIMO-OFDM cognitive radio networks

Energy efficient communications in cognitive radio (CR) networks has been of great interest. In order to address the power consumption problem and to maximise the energy efficiency (EE) in MIMO-OFDM underlay CR systems, power allocation (PA) and antenna selection (AS) algorithms are proposed. Interference alignment, which helps in mitigating the interference at primary and secondary receivers, is considered for setting-up the underlay CR scenario. The AS transmit is employed for the secondary users (SUs) on a per-subcarrier basis to improve their QoS. Further, to improve the QoS of both primary user (PU) and SUs, the AS is followed by energy efficient PA. Due to the fractional nature of EE, the EE maximisation problem is transformed into an equivalent parametric optimisation problem based on Dinkelbach method. Simulation results confirm that the proposed EE aware algorithms can improve the EE performance of both PU and SUs than the SE aware algorithms.

Performance Optimization of Hybrid Satellite-Terrestrial Relay Network Based on CR-NOMA

The non-orthogonal multiple access (NOMA) scheme realizes the transmission of multiple user signals at the same time and frequency resource block through power domain multiplexing, which improves the system transmission rate and user fairness. In this paper, we propose a joint relay-and-antenna selection scheme based on the cognitive radio scenario. This scheme can achieve the maximum communication rate of the secondary user when the primary user maintains the optimal outage performance. In the considered system both terrestrial relays and users are deployed with multi-antenna configurations and the terrestrial relays adopt the decode-and-forward (DF) strategy to achieve communication between satellites and users. Then, we derive the exact outage probability expression of each user in the system and the asymptotic probability expression under high signal-to-noise ratio (SNR). Numeric results demonstrate that increasing the number of relays and antennas on the terrestrial nodes can both improve system outage performance. Moreover, the number of relays imposes a more obvious effect on the achievable system performance.

Mobile Edge Computing Against Smart Attacks with Deep Reinforcement Learning in Cognitive MIMO IoT Systems

In wireless Internet of Things (IoT) systems, the multi-input multi-output (MIMO) and cognitive radio (CR) techniques are usually involved into the mobile edge computing (MEC) structure to improve the spectrum efficiency and transmission reliability. However, such a CR based MIMO IoT system will suffer from a variety of smart attacks from wireless environments, even the MEC servers in IoT systems are not secure enough and vulnerable to these attacks. In this paper, we investigate a secure communication problem in a cognitive MIMO IoT system comprising of a primary user (PU), a secondary user (SU), a smart attacker and several MEC servers. The target of our system design is to optimize utility of the SU, including its efficiency and security. The SU will choose an idle MEC server that is not occupied by the PU in the CR scenario, and allocates a proper offloading rate of its computation tasks to the server, by unloading such tasks with proper transmit power. In such a CR IoT system, the attacker will select one type of smart attacks. Then two deep reinforcement learning based resource allocation strategies are proposed to find an optimal policy of maximal utility without channel state information(CSI), one of which is the Dyna architecture and Prioritized sweeping based Edge Server Selection (DPESS) strategy, and the other is the Deep Q-network based Edge Server Selection (DESS) strategy. Specifically, the convergence speed of the DESS scheme is significantly improved due to the trained convolutional neural network (CNN) by utilizing the experience replay technique and stochastic gradient descent (SGD). In addition, the Nash equilibrium and existence conditions of the proposed two schemes are theoretically deduced for the modeled MEC game against smart attacks. Compared with the traditional Q-learning algorithm, the average utility and secrecy capacity of the SU can be improved by the proposed DPESS and DESS schemes. Numerical simulations are also presented to verify the better performance of our proposals in terms of efficiency and security, including the higher convergence speed of the DESS strategy.

Application of deep neural network and deep reinforcement learning in wireless communication.

ObjectiveTo explore the application of deep neural networks (DNNs) and deep reinforcement learning (DRL) in wireless communication and accelerate the development of the wireless communication industry.MethodThis study proposes a simple cognitive radio scenario consisting of only one primary user and one secondary user. The secondary user attempts to share spectrum resources with the primary user. An intelligent power algorithm model based on DNNs and DRL is constructed. Then, the MATLAB platform is utilized to simulate the model.ResultsIn the performance analysis of the algorithm model under different strategies, it is found that the second power control strategy is more conservative than the first. In the loss function, the second power control strategy has experienced more iterations than the first. In terms of success rate, the second power control strategy has more iterations than the first. In the average number of transmissions, they show the same changing trend, but the success rate can reach 1. In comparison with the traditional distributed clustering and power control (DCPC) algorithm, it is obvious that the convergence rate of the algorithm in this research is higher. The proposed DQN algorithm based on DRL only needs several steps to achieve convergence, which verifies its effectiveness.ConclusionBy applying DNNs and DRL to model algorithms constructed in wireless scenarios, the success rate is higher and the convergence rate is faster, which can provide experimental basis for the improvement of later wireless communication networks.

Unsupervised Deep Spectrum Sensing: A Variational Auto-Encoder Based Approach

In cognitive radio (CR), the test statistics of most spectrum sensing algorithms are generated from the model-based features such as the signal energy and the eigenvalues from the sample covariance matrix (CM). Despite their low complexity, their detection performance depends very much on the accuracy of the presumed model. Also, these model-based statistics may not be able to exploit the full potential of the signal samples. To this end, the data-driven deep learning-based detectors have been proposed, with test statistics generated directly from signal samples in an automatic manner. However, existing deep learning-based detectors are all supervised learning-based and they usually require a massive amount of labeled training data to achieve decent detection performance. In practical CR scenarios, however, obtaining a large amount of labeled training data may be difficult. To address this issue, in this paper, we propose an unsupervised deep learning based spectrum sensing method named unsupervised deep spectrum sensing (UDSS). The UDSS algorithm requires no prior information such as the noise power or the signal's statistical CM. Moreover, the UDSS only requires a small amount of samples collected in absence of the primary user's (PU) signals ($H_0$ labeled data). Simulation results show that the proposed UDSS algorithm is able to approach the performance of the benchmark deep supervised learning-based spectrum sensing algorithm and outperforms the model-based benchmark algorithms under both Gaussian noise and Laplace noise.

Performance Analysis of Grouped Multilevel Space-Time Trellis Coding Technique Using Cognitive Radio in Different Deployment Models

In this era, the number of users in a network is increasing tremendously at a faster rate; as a consequence, quality of service (QoS) is drastically deteriorating. To compensate such kinds of problems, we attempted to enhance the QoS of the network, which leads to an improvement in throughput, link quality, spectral efficiency, and many more. To meet the requirements mentioned above, many researchers intervene to advance and propose different techniques with an appropriate design methodology. In this work, we try to emphasize symbol error rate (SER) and frame error rate (FER) by implementing some of the existing space-time coding techniques like Space-Time Trellis Coding (STTC), multilevel space-time trellis coding (MLSTTC), and grouped multilevel space-time trellis coding (GMLSTTC). Though all these techniques are proved to be efficient enough, we explicitly included a powerful method of cooperative diversity-based spectrum sensing in cognitive radio scenario. From this analysis, we landed on to the conclusion that this technique is far better to deal with all these parameters, which can improve the QoS of the network. This paper has also analyzed the effect of the proposed model of GMLSTTC with cognitive radio on various deployment setups such as urban, suburban, and rural macrodeployment setup of the ITU-R M.2135 standard.

Improved MB Cognitive Radio Spectrum Sensing Using Wavelet Spectrum Filtering

In cognitive radio (CR), the sensed aggregate bandwidth could be as large as several GHz. This is especially challenging if the bandwidths and central frequencies of the sensed signals are unknown and need to be estimated. This work discusses a new improved method for MB spectrum sensing (iMB-SS) based on edge detection and using Wavelet Spectrum Filtering. The proposed iMB-SS method uses a Welch power spectrum density (PSD) estimate and a multi-scale Wavelet approach to reveal the spectrum transition (edges), which is deployed to characterize the spectrum occupancy in CR scenarios where the operation frequencies of the primary users (PUs) are unknown. The focus of this work lies in improving the performance of the MB spectrum sensor, particularly by refining the spectral edge location and reducing misleading detection. A comprehensive analytical description and numerical analysis have been carried out by focusing on orthogonal-frequency-division-multiplexing (OFDM) signal applications in CR networks. Numerical results corroborate the effectiveness of the proposed iMB-SS approach. The simulated results for the multiple-PU’s OFDM-based transmission CR system demonstrate that the proposed iMB-SS method can achieve high performance even under low signal-to-noise ratio (SNR) regime, turning it out as an attractive choice for SS in the MB CR systems.

Spectrum Sensing and Recognition in Satellite Systems

In the scenario of frequency coexistence between the geostationary (GEO) and non-geostationary (NGEO) satellite networks, the NGEO system should not incur harmful interference to the GEO system according to the policy of the Radio Regulations. Therefore, spectrum sensing as a promising solution is applied widely in this scenario. With the increasing number of NGEO satellites in the space, one NGEO system could be affected by other NGEO systems while sensing the signal from the GEO system. Given these preconditions, the cognitive radio scenario considered in this paper is that: the GEO system is regarded as the primary user, one NGEO system is regarded as the secondary user, and another NGEO system is regarded as the interfering user. Meanwhile, all the satellite systems are supposed to operate with more than one discrete transmit power levels, which is practical and fits the concept of adaptive power control. In our context, we propose a spectrum strategy using hypothesis testing as well as maximum a posteriori to differentiate the GEO signal from the interfering NGEO and noise, and then identify the specific power level utilized by the GEO system. Moreover, we derive the closed-form expressions for threshold of verifying the status of the GEO and for decision regions to determine its power level. Finally, extensive simulations are provided to verify the proposed studies.

Cooperative NOMA System With Virtual Full Duplex User Relaying

Cooperative non-orthogonal multiple access (NOMA) system employs user relaying to enhance performance. Although full-duplex (FD) user relaying scheme does not incur additional bandwidth for relaying, the imperfect self-interference cancellation degrades the performance significantly. Alternatively, a virtual full-duplex (VFD) user relaying scheme can be used. In this paper, we study a cooperative NOMA system in cognitive radio scenario using VFD relaying scheme, where two half-duplex (HD) secondary users take turn to relay message of a primary user with decode-and-forward (DF) strategy. First, we derive the closed-form and asymptotic expressions of the outage probability and ergodic rate of the users. Then, we demonstrate the outage probability, ergodic rate, and ergodic sum rate performance of the proposed scheme using Monte Carlo simulations. We also provide a comprehensive comparison between the proposed VFD user relaying and existing FD and HD user relaying in orthogonal multiple access and NOMA systems. The simulation results verify the analytical results and demonstrate the advantages of the proposed VFD user relaying over existing schemes.

Radio spectrum collision avoidness in cluster cognitive network through gazer nodes

The spectrum deficiency in cognitive radio can be solved effectively by utilisation of radio spectrum. The spectrum is not effectively shared among all the other users. Since the users are spread across different locations the spectrum allocation and spectrum sharing is important to use spectrum effectively and to allocate communication channel to all the devices in the network, by doing so all the nodes in the network can communicate covering large area. In cognitive radio, spectrum sensing, spectrum allocation, and reuse scenarios approaches with the different algorithm help improve the utilisation of the spectrum. Traditional spectrum allocation technique such as fuzzy logic and harmony search replaces the spectrum with the new spectrum scheme. However, the new technique brings more efficiency in achieving spectrum utilisation. Still the cognitive in mesh network has the problem of collision between the secondary and primary users. To minimise the effect of collision we introduce a gazer-based cognitive radio network (GCRN) which provides more freedom for frequency sharing paradigm. The novel algorithm provides the network to adopt automatically for every change in the environment of the cluster in cognitive radio network.

Deep Reinforcement Learning Based Intelligent User Selection in Massive MIMO Underlay Cognitive Radios

Cognitive radio (CR) and massive multiple-input multiple-output (MIMO) have attracted much interest recently due to the amazing ability to accommodate more users and improve spectrum utilization. This paper investigates the QoS-aware user selection approach for massive MIMO underlay cognitive radio. Two main CR scenarios are considered: 1) the channel state information (CSI) of the cross channels are available at the secondary base station (SBS), and 2) any CSI of cross-network is unavailable at SBS. For the former, we develop the low-complexity increase-user-with-minimum-power algorithm (IUMP) and decrease-user-with-maximum-power algorithm (DUMP) which both can address the problem of user selection with power allocation. However, the CSI is typically not available in practice. To address the intractable issue, we propose a deep reinforcement learning-based approach, which can enable the SBS to realize efficient and intelligent user selection. The simulation results show that the IUMP and DUMP algorithms have obvious performance advantages over traditional user selection methods. In addition, results also verify that our constructed neural network can efficiently learn the optimal user selection policy in the unknown dynamic environment with fast convergence and high success rate.

How Many Cognitive Channels Should the Primary User Share?

Despite almost two decades of research, many challenges remain unresolved in Cognitive Radio scenarios, especially the fundamental problem of reliable spectrum sensing. Hence, we propose a Channel Partitioning Scheme based Cognitive Radio relying on realistic imperfect spectrum sensing. The channels licensed to the primary user are partitioned into two sets. Channels in one of the sets are exclusively reserved for the PU, while those in the other can be accessed by both the PU and the secondary user after spectrum sensing. The PU prioritizes its transmissions over the two sets by first transmitting using the channels in the reserved set and then moves on to the unreserved set, if required. It is shown that this simple policy significantly improves the overall detection performance as well as the simultaneous achievable rates and reduces the overhead associated with sensing.

A Sensing Contribution-Based Two-Layer Game for Channel Selection and Spectrum Access in Cognitive Radio Ad-hoc Networks

In cognitive radio (CR) networks, the secondary users (SUs) sense the spectrum licensed to the primary users (PUs) to identify and possibly transmit over temporarily unoccupied channels. Cooperative sensing was proposed to improve the sensing accuracy, but in heterogeneous scenarios, SUs do not contribute equally to the cooperative sensing result because they experience different received PU signal quality at their sensors. In this paper, a two-layer cooperative game is developed for distributed sensing and access in multichannel CR ad hoc networks, where the SUs’ transmission opportunities are commensurate with their sensing contributions, thus fostering cooperation and eliminating free-riders. Numerical results show that the proposed two-layer game is computationally efficient and outperforms previously investigated collaborative sensing and spectrum access approaches in heterogeneous multichannel CR scenarios in terms of energy efficiency, throughput, SU fairness, and complexity. Moreover, it is demonstrated that this game is robust to changes in the network topology and the number of SUs. Finally, a new physical-layer approach is proposed to distribute the network-level miss-detection constraints fairly among the interfering SUs for guaranteed PU protection and demonstrate the performance advantages of the AND-rule combining of spectrum sensing results for heterogeneous SUs.

Adaptive Data-Driven Wideband Compressive Spectrum Sensing for Cognitive Radio Networks

This paper presents a novel adaptive wideband compressed spectrum sensing scheme for cognitive radio (CR) networks. Compared to the traditional CSSbased CR scenarios, the proposed approach reconstructs neither the received signal nor its spectrum during the compressed sensing procedure. On the contrary, a precise estimation of wide spectrum support is recovered with a fewer number of compressed measurements. Then, the spectrum occupancy is determined directly from the reconstructed support vector. To carry out this process, a data-driven methodology is utilized to obtain the minimum number of necessary samples required for support reconstruction, and a closed-form expression is obtained that optimally estimates the number of desired samples as a function of the sparsity level and number of channels. Following this phase, an adjustable sequential framework is developed where the first step predicts the optimal number of compressed measurements and the second step recovers the sparse support and makes sensing decision. Theoretical analysis and numerical simulations demonstrate the improvement achieved with the proposed algorithm to significantly reduce both sampling costs and average sensing time without any deterioration in detection performance. Furthermore, the remainder of the sensing time can be employed by secondary users for data transmission, thus leading to the enhancement of the total throughput of the CR network.