Cognitive Radio Capabilities Research Articles

GGCNN: An Efficiency-Maximizing Gated Graph Convolutional Neural Network Architecture for Automatic Modulation Identification

Automatic modulation identification (AMI) is a technique to detect the modulation type and order of a received signal, which has the potential to enhance cognitive radio capabilities for future generations of communication devices. However, AMI classifiers traditionally have exhibited low efficiency in low signal-to-noise ratio (SNR) environments. Hence, to address this problem we present our novel Gated Graph Convolutional Neural Network (GGCNN) classifier for feature-based AMI. This architecture includes a robust feature extraction stage to extract deep correlative patterns about the received symbols. Not only does this feature extraction stage use the temporal characteristics of the received symbols, but it also takes advantage of embedded signaling features from the received signal. In the proposed classifier, the received constellations are treated as a graph, allowing it to outperform state-of-the-art classifiers due to its strong performance in graph classification. This is observed clearly in the visualization of the extracted features, even for high-order modulation schemes. In this paper, we present our systematic research conducted for maximizing the efficiency obtainable by our classifier. Extensive simulation results demonstrate a significant accuracy improvement of 18.44 percentage points, and an efficiency increase by 60.78% for our GGCNN-AMI classifier compared to state-of-the-art classifiers in low-SNR environments.

Implementation of Spectrum Sensing Algorithm Based on Television White Space Scenarios for Spectrum Aggregation Applications

With the proliferation of mobile devices and diverse mobile applications, wireless operators are experiencing phenomenal growth in the demand for mobile services around the world. These demands have led to the technological responses such as the advancement in radio access, coding and modulation schemes as well as spectrum (carrier) aggregation and cognitive radio capabilities. The aspect of spectrum or carrier aggregation uses cognitive radio technology that implements the dynamic spectrum access for spectrum utilization by sensing and using the underutilized spectrum on co-primary basis without interference to the primary users. In this work, an energy-based, non-parametric TVWS spectrum sensing algorithm was implemented in MATLAB environment. The developed sensing algorithm was aimed at finding free TVWS frequency channel(s) that would be used during spectrum aggregation of channels from TVWS and LTE-A spectrums. The chosen free channel would be configured as the component carrier two (CC2) at the configuration management platform of the TVWS eNode B. The result showed the instantaneous TV channels statuses; free, busy and interfered by computing the PSD expressed in dB while the estimated received power level in dBm for free channels was computed for the UHF range from channel 21 through channel 69. The detectable TV signals’ threshold value was set at -114 dBm. In the ten simulation five cycles carried out, the developed algorithm instantaneously selected channels: 68,34,48,60 and 28 corresponding to the centre (carrier) frequencies of 850MHz, 578MHz, 690MHz, 786MHz and 530MHz, would be dynamically configured as the component carrier two (CC2) in the TVWS eNode Bs during spectrum aggregation process.

Performance analysis in overlay-based cognitive D2D communications in 5G networks

With the advent of fifth-generation 5G networks, mobile communication data rates are steadily rising. The existing base stations (BSs) must reduce the loads with minimal cost and effort. With traffic demands constantly rising, available spectrum resources must be utilized effectively. One of the most exciting developments in 5G networks, particularly cellular networks, is the potential for devices to communicate directly with one another through a technology called device-to-device communication (D2D). With the transmission split between D2D users on one side and cellular users and the BS on the other, it will be effective in the rapidly expanding capacity and offload traffic from the BS. With the dynamic spectrum access in mind, this article proposes using cognitive radio direct-to-device (CRD2D) communication in 5G networks to make use of the unused spectrum. Throughput and latency are examined, for a carrier sense multiple access (CSMA) based medium access control (MAC) protocol that uses a common control channel (CCC) for device users to negotiate the data channel and to address contention among device users. Device users can take advantage of cognitive radio to access multiple data channels simultaneously in a shared interference zone. Since this property has not been utilized, in prior works, we use it to measure the possible delay and throughput. Cognitive radio capability applied to D2D communication and the allocation of a common control channel for device users significantly increases aggregated network throughput by more than 60% compared to individual D2D throughput without causing any harmful interference to cellular users. The wait time can also be scale back using this method.

RL-IoT: Reinforcement Learning-Based Routing Approach for Cognitive Radio-Enabled IoT Communications

Internet of Things (IoT) devices are widely being used in various smart applications and being equipped with cognitive radio (CR) capabilities for dynamic spectrum allocation. Our objectives in this work are to achieve higher data rates and minimize end-to-end routing delays in CR-enabled IoT communication in order to maximize throughput. We propose a reinforcement learning (RL)-based routing approach in the cognitive radio network (CRN)-based IoT environment. The idea is to add the channel selection decision capability to the network layer in order to minimize packet collisions as well as end-to-end delay (EED). We perform a comprehensive performance evaluation of the proposed RL-IoT routing mechanism by simulating the cognitive radio-enabled Internet of Things (CR-IoT) communication environment in the cognitive radio cognitive network (CRCN) simulator and comparing the network performance achieved by our proposed mechanism with that of the recent AODV-based routing mechanism for IoT (AODV-IoT), ELD-CRN, and SpEED-IoT routing approaches. Our evaluation results show that the RL-IoT model performs better than existing approaches in terms of average data rate, throughput, packet collision, and EED.

Applications of Cognitive Radio Networks: A Review

Cognitive radio network is one of the future technology of wireless communication. Recently, wireless communications are facing the problem of spectrum scarcity due to the huge demand for various applications. There are many applications such as health services, personal entertainment, military and public safety, smart home appliances, smart farming, traffic monitoring, controlling etc., using wireless communication, and most of them are delay sensitive and real time applications. The wireless communication can improve their performance with the capabilities of cognitive radio. In this study, many cognitive radio enabled wireless applications are discussed. It shows that it can be easily deployed with cognitive radio enabled devices without any cost. With this, unused licensed spectrum is utilized efficiently and moreover, it solves the problem of spectrum scarcity.

ENHANCED HYBRID COGNITIVE RADIO AD HOC NETWORKS (CRAHNs) FOR MOBILE NETWORK

In sensor networks, a very crucial aspect of the maintenance of the communications of secure data is the data, which is the most difficult of all tasks. The sensor network consists of hierarchical elements like the Base Station (BS), the Cluster Head (CH) and the Sensor Nodes (SNs), and this will have three different keys, which are the public and private, the cluster and the master keys. Through the opportunistic use of the currently available wireless spectrum, cognitive radio (CR) technology is intended to address the issues in wireless networks brought on by the limited amount of range accessible and the inefficient use of spectrum. With the inherent capabilities of cognitive radio, CR networks will offer the most advanced spectrum-aware communication paradigm in wireless communications. However, the significant spectral fluctuation and various quality-of-service (QoS) requirements faced by CR networks provide unique difficulties. The distributed multihop design, the dynamic network topology, and the time- and location-varying spectrum availability are essential differentiating elements in cognitive radio ad hoc networks (CRAHNs).

Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities

Collaborative spectrum access requires wireless devices to perform spectrum-related tasks (such as sensing) on request from other nodes. Thus, while joining the network, they need to inform neighboring devices and/or the central coordinator of their capabilities. During the operational phase, nodes may request other permissions from the the controller, like the opportunity to transmit according to the current policies and spectrum availability. To achieve such coordinated behavior, all associated devices within the network need a language for describing radio capabilities, requests, scenarios, policies, and spectrum availability. In this paper, we present a thorough comparison of the use of two candidate languages—Web Ontology Language (OWL) and eXtensible Markup Language (XML)—for such purposes. Towards this goal, we propose an evaluation method for automating quantitative comparisons with metrics such as precision, recall, device registration, and the query response time. The requests are expressed in both SPARQL Protocol and RDF Query Language (SPARQL) and XML Query Language (XQuery), whereas the device capabilities are expressed in both OWL and XML. The evaluation results demonstrate the advantages of using OWL semantics to improve the quality of matching results over XML. We also discuss how the evaluation method can be applicable to other scenarios where knowledge, datasets, and queries require richer expressiveness and semantics.

Enhanced detection methods for compressive spectrum sensing based on antieigenvalues

Spectrum sensing is a crucial process in devices that dynamically and opportunistically attempt to access the spectrum based on its availability, including secondary users in a cognitive radio network and internet-of-things (IoT) devices with cognitive radio capabilities. Eigenvalue-based detection methods have recently gained considerable attention over classical detection techniques due to their efficiency. In this paper, we propose two new detection methods based on antieigenvalues, which have proven to be more efficient than existing eigenvalue-based methods. The first method uses the smallest antieigenvalue of the incoming signals’ covariance matrix as a test statistic, and the second method uses the slope of the antieigenvalues, whose behavior and probabilistic distribution is studied and presented in this paper. The two methods are also applied in a compressive sensing framework, where the samples are acquired at rates below the Nyquist rate. Both these methods have also proven to be more efficient than the only other existing method based on antieigenvalues, which uses the sum of the smallest P antieigenvalues — where P is the number of primary user signals — as a test statistic.

Opportunistic non-contiguous OFDMA scheduling framework for future B5G/6G cellular networks

In this paper, we consider the problem of resource allocation within Beyond 5G (B5G) and the envisioned 6G wireless networks with Cognitive Radio (CR) capability. CR technology allows opportunistic and dynamic access to the unused licensed spectrum bands. Unlike the conventional orthogonal frequency division multiple access (OFDMA) schemes that impose spectrum contiguity constraints in assigning the OFDMA resource blocks (RBs), the Dis-contiguous OFDMA (D-OFDMA) approach allows for flexible time–frequency RB scheduling, in which each contending CR user can access adjacent and/or non-adjacent RBs. This can provide a more efficient and flexible spectrum utilization compared to conventional OFDMA schemes. Specifically, we formulate the spectrum assignment and access problem as an optimization problem that attempts to effectively utilize the time–frequency spectrum holes of the D-OFDMA RBs. The main goal is to improve the total network throughput by maximizing the achieved sum-rate of all contending CR users subject to achieving a set of interference and performance requirement constraints. The formulated problem is classified as a binary-linear program (BLP), which is, in general, NP-hard. Thus, we develop a sub-optimal polynomial-time solution based on a relaxing-and-rounding method. Simulation results reveal that the proposed D-OFDMA RB scheduling protocol significantly improves the network throughput while maintaining fairness compared to conventional contiguous-based OFDMA protocols. Specifically, the proposed protocol achieves up to 90% and 150% performance improvements over the contiguous frequency-OFDMA and contiguous time-OFDMA, respectively.

Highly Reliable Transmission and Channel Assignment for CR-IoT Networks

In this article, we propose a highly reliable transmission strategy based on exploiting channel diversity for Internet of Things (IoT) networks equipped with cognitive radio (CR) capabilities (referred to as CR-IoT networks). Specifically, we investigate the channel-assignment problem with the objective of minimizing the number of assigned channels for each CR transmission subject to link reliability, data rate, and success probability constraints. We use the theory of continuous-time Markov chains to derive mathematical expressions for channel availability and reliability. These expressions are employed in a binary linear program model to formulate the channel assignment optimization problem, which is suboptimally solved using a polynomial-time sequential fixing procedure. Theoretical performance analysis of the proposed channel assignment scheme is derived in terms of a lower bound on the achievable probability of successful packet transmission. Through simulation experiments, we demonstrate significant performance enhancement compared to existing reliability-unaware CR channel assignment algorithms.

Internet of Things (IoT) for MC-CDMA-Based Cognitive Radio Network (CRN) in 5G: Performance Results

Both the internet-connected devices, i.e. IoT and Cognitive Radio Network (CRN) are considered to be the future technologies for the fifth generation of cellular wireless standards (5G). On the one hand, Internet of Things (IoT) focuses primarily on how to allow general objects to see, hear, and smell their own physical environment and make them connected to share the observations. On the other hand, a CRN is based on a complex spectrum allocation system, and licenced primary users (PUs) or unlicenced secondary users (SUs) are allowed to share the spectrum, provided they do not cause significant interference. The IoTs are meaningless if IoT objects are not equipped with cognitive radio capability. In cognitive radio, it is important to control the transmission power of SUs so that the interference should not be harmful to the quality of service of PUs. In this paper, the authors addressed the effects of imperfect power control between primary users (PUs) and the secondary users (SUs) of an IoT-based CRN. The effect of the co-channel interference (CCI) and adjacent channel interferences (ACIs) occurring in CRN using MC-CDMA system is also analysed. A new expression of the signal-to-interference-noise ratio (SINR) for CRN-based MC-CDMA system over a Nakagami-[Formula: see text] fading channel with imperfect power control condition is derived and investigated. The performance of IoT-based CRN using MC-CDMA system over the frequency selective multipath fading channel is examined with varying the number of users, the SINR per bit, number of fading path and number of sub-carriers. From the simulation results, we have seen that the SINR performance is affected by these parameters. The result of the analysis will provide relevant information to design the physical layer protocol for high-speed IoT-based CRN system for 5G.

Novel Spectrum Sensing Technique and Its Evaluation for Cognitive Radio Wireless Sensor Network

Cognitive Radio based Wireless Sensor Network is a novel concept that integrates the dynamic spectrum access capability of cognitive radio into wireless sensor networks for the futuristic sensor networks and wireless communication technology. Spectrum sensing plays a quintessential role in a cognitive radio network but is a major constraint for a battery powered sensor with stringent energy limitations. The spectrum sensing algorithms are expected to yield acceptable detection probability at low SNR under noise uncertainty with minimum power consumption in a WSN. In this paper, a new spectrum sensing method has been proposed to overcome sensing failure under low SNR environment. The proposed technique is based on adaptive double threshold theory which improves the detection performance by 39.63 and 27.22% at SNR = −10dB as compared to the conventional energy detection and available double threshold-based method respectively. Furthermore, the proposed method of spectrum sensing is evaluated for its deployment into a CR-WSN using the evaluation metrics: Time and Sample Complexity. The comparative evaluation of the spectrum sensing method in a WSN through simulations shows that the proposed technique offers substantial reduction in sample and time complexity of the wireless sensor nodes.

Energy Minimization of Multi-Cell Cognitive Capacity Harvesting Networks With Neighbor Resource Sharing

In this paper, we investigate the energy minimization problem for a cognitive capacity harvesting network (CCHN), where secondary users (SUs) without cognitive radio (CR) capability communicate with CR routers via device-to-device (D2D) transmissions, and CR routers connect with base stations (BSs) via CR links. Different from traditional D2D networks that D2D transmissions share the resource of cellular transmissions in the same cell, we consider the scenario that D2D transmissions share the uplink cellular frequency bands (CFBs) of neighbor cells. To ensure that the transmissions from SUs do not affect the transmissions for the cellular users (CUs) in the neighbor cells, an inter-cell handshake process is proposed. We formulate the energy minimization problem for SUs as a mixed integer non-linear programming (MINLP). To solve this problem, we decompose it into two nested subproblems: a transmit power optimization subproblem and a CR router and uplink CFB selection subproblem. For the first subproblem, it is proved to be convex, and thus can be efficiently solved. For the second subproblem, we propose a two-level nested game theoretic approach to finding its solution. Simulation results show that the proposed algorithms can significantly improve the performance. With the help of CR routers/the neighbor resource sharing, the energy consumption for SUs can be saved around 30%-37% on average.

Cognitive Radio Based System for Best Effort Communications in Sound-based Covert Channel for IoT Environments

Nowadays, the use of nodes with different sensing capabilities is pervasive. This leads to many environments capable of monitoring human activity for security, comfort, and connectivity applications among others. However, this also leads to many opportunities for security breaches in personal mobile devices when they are in such environments. In this work, we focus on a covert channel communication system based on sound waves with cognitive radio capabilities. In such system, the mobile nodes are infected with malware that allows the attacker to obtain information from the devices, sending data using a covert channel (hidden communications) to neighbor sensor nodes used for IoT applications through sound waves imperceptible to human ears. A cognitive radio design is based on allowing such hidden communications to occur when the primary channel is unused (i.e., when the user is not using or handling its device). As such, the primary network is considered to be any activity of the mobile (communication, application or service) and the secondary network is the covert channel communication system. Such a system is studied and mathematically analyzed using Continuous Time Markov Chains in order to obtain the throughput in the covert channel for different system parameters. A main result from this work is that, a careful selection of the activation parameter can lead to a more efficient covert data transfer system, irrespective of the rest of the system parameters.

Dynamic Spectrum Sensing with Automatic Modulation Classification for a Cognitive Radio Enabled NomadicBTS

An existing Nomadic Base Transceiver Station (NomadicBTS) architecture designed and implemented in the literature was built on software defined radio technology. This technology performs radio functions via software modules. Cognitive radio on the other hand is essentially built on software defined radio technology integrated with artificial intelligence. This research work extends the existing NomadicBTS architecture with cognitive radio capability for the purpose of introducing dynamic (opportunistic) spectrum sensing for efficient spectrum utilization in mobile networks. This is achieved by developing an Automatic Modulation Classification (AMC) model for spectrum sensing based on MultiLayer Perceptron (MLP) Artificial Neural Network (ANN). A suitable AMC model with optimum accuracy of 92.57% and Mean Square Error (MSE) of 0.0185 was empirically determined and deemed acceptable for incorporation into the NomadicBTS architecture.

Design and Analysis of a Miniaturized Meandered Slot-Line-Based Quad-Band Frequency Agile MIMO Antenna

In this work, a novel miniaturized meandered slot-line-based quad-band multiple-input-multiple-output (MIMO) antenna system is presented. The proposed design consists of two-antenna elements meandered in an H-shaped configurations. The miniaturization techniques of meandered slot-line and reactive loading are implemented to operate the antenna in sub-1 GHz bands with compact structure which results in an antenna size of $0.08\lambda _{o}$ corresponding to its lowest operating band. The frequency reconfigurability in the design is achieved by reactively loading the slots using varactor diodes. Covering most of the modern communication standards, the antenna operates in 0.665–1.13 GHz, 1.415–2.005 GHz, 2.42–3.09 GHz, and 3.18–3.89 GHz bands. Furthermore, complete analysis of frequency reconfigurability and design procedure is provided as a guideline for designing such an antenna system for any desired band of operation. The proposed two-element design is realized on a board dimensions of $60\times 120\times0.76$ mm3 using an RO4350 substrate. The proposed design is well-suited for wireless terminals with second generation cognitive radio (CR) capabilities for radio-frequency (RF) front-end applications.

A Reliable Energy Efficient Dynamic Spectrum Sensing for Cognitive Radio IoT Networks

The Internet of Things (IoT) that allows connectivity of network devices embedded with sensors undergoes severe data exchange interference as the unlicensed spectrum band becomes overcrowded. By applying cognitive radio (CR) capabilities to IoT, a novel cognitive radio IoT (CR-IoT) network arises as a promising solution to tackle the spectrum scarcity problem in conventional IoT network. CR is a form of wireless communication whereby a radio is dynamically programmed and configured to detect available spectrum channels. This enhances the spectrum utilization efficiency of radio frequency while avoiding interference and overcrowding to other users. Energy efficiency in CR-IoT network must be carefully formulated since the sensor nodes consume significant energy to support CR operations, such as in dynamic spectrum sensing and switching. In this paper, we study channel spectrum sensing to boost energy efficiency in clustered CR-IoT networks. We propose a two-way information exchange dynamic spectrum sensing algorithms to improve energy efficiency for data transmission in licensed channels. In addition, the concern of the energy consumption in dynamic spectrum sensing and switching, we propose an energy efficient optimal transmit power allocation technique to enhance the dynamic spectrum sensing and data throughput. Simulation results validate that the proposed dynamic spectrum sensing technique can significantly reduce the energy consumption in CR-IoT networks.

Priority Schemes for Life Extension and Data Delivery in Body Area Wireless Sensor Networks with Cognitive Radio Capabilities

Wireless sensor networks have recently been used to monitor bioelectric signals generated by the human body. However, since the dimensions and capabilities of the network nodes are small and limited, a problem is generated inherently by the energy consumption and radioelectric interference. In view of this, we propose two priority schemes to improve the performance of the network. In the first one, we aim at increasing the system lifetime by reducing the number of transmissions of nodes with low energy levels. In the second scheme, we aim at conveying priority data to the sink node as fast and efficient as possible. In the former scheme, users can be monitored for long time periods considering a low data generation. On the other hand, the latter scheme allows expediting transmission of important data when energy efficiency is not relevant. In this article, we mathematically model, analyze, and study the performance of the system for both priority schemes considering cognitive radio capabilities to make efficient use of resources and limit the radioelectric interference when the network transmits both continuous monitoring and event detection information. Numerical results provided in this work allow a careful parameter selection for practical implementation of BANETs.

A Spectrum-Aware Priority-Based Link Scheduling Algorithm for Cognitive Radio Body Area Networks.

With the development of wireless communication technology, wireless body area networks (WBANs) have become a fundamental support tool in medical applications. In a real hospital scenario, however, the interference between wireless medical devices and WBANs may cause a high packet drop rate and high latency, which is harmful to patients using healthcare services. Nonetheless, cognitive radio is a promising technology for sharing the precious spectrum, which has high efficiency of the wireless resource. Thus, WBANs with cognitive radio capability are also exploited. We propose a spectrum-aware priority-based link scheduling (SPLS) algorithm for cognitive radio body area networks (CRBANs) in a real hospital scenario. In SPLS, three channels are used: DataCh, EDataCh, and CtrlCh for normal data, emergency data, and control messages, respectively. To avoid collision during data transmission, neighboring CRBANs send messages regarding the channel state with CtrlCh before the scheduling. The CRBANs can share DataCh in the time domain for improving the throughput. The SPLS algorithm allows a CRBAN to access idle channels on the licensed and unlicensed spectrum according to the CRBAN traffic. Our simulation results show that the proposed SPLS outperformed the conventional scheme in terms of packet delivery ratio, system throughput, latency, and energy efficiency.

Sustainable and Efficient Data Collection in Cognitive Radio Sensor Networks

C ognitive Radio is a promising technology that maximize spectrum efficiency and can apply to Wireless Sensor Networks. This paper proposes a system architecture which introduces enhancements at lower layers of a Software Defined Network of Wireless Sensors Network with Cognitive Radio capabilities for efficient sensors’ power management, energy consuming channel handoffs elimination, efficient spectrum brokerage, and QoS provision in terms of data rate to the sensors’ applications via the SDN flows. The large Wireless Sensors Network is divided into clusters for power efficiency - as sensor operate in lower power - which connect to a cloud-assisted Central Controller. The protocol encompasses an optimal reinforcement learning scheme for efficient spectrum utilization that enables efficient sensors’ data collection, while sustainability issues are satisfied. Software Defined Wireless Sensor Network dynamically adapts to the spectrum and interference conditions on per flow basis and predicts Primary Users’ traffic to totally avoid collision with the licensed users. The paper is concentrated on sustainable solutions for sensors’ data collection by the cluster heads leveraging the Cognitive Radio Network facilities and taking into account the demands of the applications running on the sensors. The Cognitive Radio Sensor Network is considered as large organized on a local basis to extend networks lifetime and allow resource reuse.