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The proposed novel algorithm named decision-making algorithm with geographic mobility (DMAGM) includes detailed analysis of decision-making for cognitive radio (CR) that considers a multivariable algorithm with geographic mobility (GM). Scarce research work considers the analysis of GM in depth, even though it plays a crucial role to improve communication performance. The DMAGM considerably reduces latency in order to accurately determine the best communication channels and includes GM analysis, which is not addressed in other algorithms found in the literature. The DMAGM was evaluated and validated by simulating a cognitive radio network that comprises a base station (BS), primary users (PUs), and CRs considering random arrivals and disappearance of mobile devices. The proposed algorithm exhibits better performance, through the reduction in latency and computational complexity, than other algorithms used for comparison using 200 channel tests per simulation. The DMAGM significantly reduces the decision-making process from 12.77% to 94.27% compared with ATDDiM, FAHP, AHP, and Dijkstra algorithms in terms of latency reduction. An improved version of the DMAGM is also proposed where feedback of the output is incorporated. This version is named feedback-decision-making algorithm with geographic mobility (FDMAGM), and it shows that a feedback system has the advantage of being able to continually adjust and adapt based on the feedback received. In addition, the feedback version helps to identify and correct problems, which can be beneficial in situations where the quality of communication is critical. Despite the fact that the FDMAGM may take longer than the DMAGM to calculate the best communication channel, constant feedback improves efficiency and effectiveness over time. Both the DMAGM and the FDMAGM improve performance in practical scenarios, the former in terms of latency and the latter in terms of accuracy and stability.

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Currently, there are saturated frequency bands that affect the quality of service for new users. Cognitive radio provides an alternative solution to this problem through dynamic spectrum access. However, the solutions proposed in the current literature are focused on a centralized network and do not allow demonstrating the behavior in a multi-user environment, much less the effect that cooperation between secondary users can have. This article establishes a decision-making model for the best spectral opportunity selection with a cooperative approach in decentralized cognitive radio networks and contrasts its results with three multi-criteria decision-making algorithms: SAW, TOPSIS, and VIKOR. So, this research suggests a cooperative decision-making model based on four main modules. (1) a collaborative module for the exchange of information between SU; (2) a module for PU characterization; (3) a module of the probability of SU arrival; and (4) the SO feedback selection module. The results are obtained through simulations fed with experimental spectral occupancy data captured in a measurement campaign. Handoff and throughput were used as evaluation metrics, along with five levels of collaboration: 10%, 20%, 50%, 80%, and 100%, and eight different scenarios based on the type of network: GSM and Wi-Fi, the application type: real-time and best-effort, and the level of traffic: high and low. The contribution of this study lies in the fact that no current work includes the following relevant aspects for an adequate validation and evaluation of this proposal: First, the consideration of a decentralized cognitive radio network; second, the decision-making with cooperative strategies; third, different techniques for SO selection; fourth, the validation and evaluation with experimental spectral occupancy data captured in measurement campaigns; finally, the performance analysis in diverse networks, traffic levels, and types of applications.

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The development and growth of Wireless Sensor Networks (WSNs) is significantly propelled by advances in Radio Frequency (RF) and Visible Light Communication (VLC) technologies. This paper endeavors to present a comprehensive review of the state-of-the-art in cognitive hybrid RF-VLC systems for WSNs, emphasizing the critical task of seamlessly integrating Cognitive Radio Sensor Networks (CRSNs) and VLC technologies. The central challenge addressed is the intricate landscape of this integration, characterized by notable trade-offs between performance and complexity, which escalate with the addition of more devices and increased data rates. This scenario necessitates the development of advanced cognitive radio strategies, potentially facilitated by Machine Learning (ML) and Deep Learning (DL) approaches, albeit introducing new complexities such as the necessity for pre-training with extensive datasets. The review scrutinizes the fundamental aspects of CRSNs and VLC, spotlighting key areas like Energy Efficient Resource Allocation, Industrial Scenarios, and Energy Harvesting, and explores the synergistic amalgamation of these technologies as a promising pathway for enhanced spectrum utilization and network performance. By delving into the integration of cognitive radio technology with visible light, this study furnishes valuable insights into the potential for innovative applications in wireless communication, presenting a balanced overview of the current advancements and prospective avenues in the field of cognitive hybrid RF/VLC systems.

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The concept of cognitive radio (CR) as a tool to optimize the obstacle of spectral coexistence has promoted the development of shared satellite-terrestrial wireless networks. Nevertheless, in some applications like Earth Exploration Satellite Services, which demand high spectral efficiency (bps/Hz) for downlink transmissions, spectral coexistence amidst interferences from cellular Base Stations is still challenging. Our research aims to mitigate these interferences on low-orbit satellite downlinks carrying imaging data received from a ground station. In order to fulfill this, we present cognitive radio approaches to enhance spectrum exploitation and introduce the adaptive modulation and coding (MODCOD) technique to increase RF power and spectral efficiencies. Therefore, we propose a combined methodology using CR and adaptive MODCOD (ACM) techniques. Afterwards, we applied the solution by monitoring the signal to interference plus noise ratio and the MODCOD strategy. Finally, we provide a real in situ case study at the Cuiabá ground station located in Brazil's central area, which receives images from an Earth observation satellite (EOS). In addition to demonstrating the strategy effectiveness in this scenario, we conducted a bench test emulating the interfering wireless communication system. In this sense, we demonstrated the proposed approach, successfully mitigating the harmful effects on the received EOS images.

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Recently, the Gini index detector (GID) has been proposed as an alternative for data-fusion cooperative spectrum sensing, being mostly suitable for channels with line-of-sight or dominant multi-path components. The GID is quite robust against time-varying noise and signal powers, has the constant false-alarm rate property, can outperform many the state-of-the-art robust detectors, and is one of the simplest detectors developed so far. The modified GID (mGID) is devised in this article. It inherits the attractive attributes of the GID, yet with a computational cost far below the GID. Specifically, the time complexity of the mGID obeys approximately the same run-time growth rate of the GID, but has a constant factor approximately 23.4 times smaller. Equivalently, the mGID takes approximately 4% of the computation time spent to calculate the GID test statistic, which brings a huge reduction in the latency of the spectrum sensing process. Moreover, this latency reduction comes with no performance loss with respect to the GID.

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Sub-GHz communication provides long-range coverage with low power consumption and reduced deployment cost. LoRa (Long-Range) has emerged, among existing LPWAN (Low Power Wide Area Networks) technologies, as a promising physical layer alternative to provide ubiquitous connectivity to outdoor IoT devices. LoRa modulation technology supports adapting transmissions based on parameters such as carrier frequency, channel bandwidth, spreading factor, and code rate. In this paper, we propose SlidingChange, a novel cognitive mechanism to support the dynamic analysis and adjustment of LoRa network performance parameters. The proposed mechanism uses a sliding window to smooth out short-term variations and reduce unnecessary network re-configurations. To validate our proposal, we conducted an experimental study to evaluate the performance concerning the Signal-to-Noise Ratio (SNR) parameter of our SlidingChange against InstantChange, an intuitive mechanism that considers immediate performance measurements (parameters) for re-configuring the network. The SlidingChange is compared with LR-ADR too, a state-of-the-art-related technique based on simple linear regression. The experimental results obtained from a testbed scenario demonstrated that the InstanChange mechanism improved the SNR by 4.6%. When using the SlidingChange mechanism, the SNR was around 37%, while the network reconfiguration rate was reduced by approximately 16%.

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Mobile cognitive radio networks (MCRNs) have arisen as an alternative mobile communication because of the spectrum scarcity in actual mobile technologies such as 4G and 5G networks. MCRN uses the spectral holes of a primary user (PU) to transmit its signals. It is essential to detect the use of a radio spectrum frequency, which is where the spectrum sensing is used to detect the PU presence and avoid interferences. In this part of cognitive radio, a third user can affect the network by making an attack called primary user emulation (PUE), which can mimic the PU signal and obtain access to the frequency. In this paper, we applied machine learning techniques to the classification process. A support vector machine (SVM), random forest, and K-nearest neighbors (KNN) were used to detect the PUE in simulation and emulation experiments implemented on a software-defined radio (SDR) testbed, showing that the SVM technique detected the PUE and increased the probability of detection by 8% above the energy detector in low values of signal-to-noise ratio (SNR), being 5% above the KNN and random forest techniques in the experiments.

Assuntos

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Cognitive radios represent a real alternative to the scarcity of the radio spectrum. One of the primary tasks of these radios is the detection of possible gaps in a given bandwidth used by licensed users (called also primary users). This task, called spectrum sensing, requires high precision in determining these gaps, maximizing the probability of detection. The design of spectrum sensing algorithms also requires innovative hardware and software solutions for real-time implementations. In this work, a technique to determine possible primary users' transmissions in a wide frequency interval (multiband spectrum sensing) from the perspective of cognitive radios is presented. The proposal is implemented in a real wireless communications environment using low-cost hardware considering the sample entropy as a decision rule. To validate its feasibility for real-time implementation, a simulated scenario was first tested. Simulation and real-time implementations results were compared with the Higuchi fractal dimension as a decision rule. The encouraging results show that sample entropy correctly detects noise or a possible primary user transmission, with a probability of success around 0.99, and the number of samples with errors at the start and end of frequency edges of transmissions is, on average, only 12 samples.

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Some bands in the frequency spectrum have become overloaded and others underutilized due to the considerable increase in demand and user allocation policy. Cognitive radio applies detection techniques to dynamically allocate unlicensed users. Cooperative spectrum sensing is currently showing promising results. Therefore, in this work, we propose a cooperative spectrum detection system based on a residual neural network architecture combined with feature extractor and random forest classifier. The objective of this paper is to propose a cooperative spectrum sensing approach that can achieve high accuracy in higher levels of noise power density with less unlicensed users cooperating in the system. Therefore, we propose to extract features of the sensing information of each unlicensed user, then we use a random forest to classify if there is a presence of a licensed user in each band analyzed by the unlicensed user. Then, information from several unlicensed users are shared to a fusion center, where the decision about the presence or absence of a licensed user is accomplished by a model trained by a residual neural network. In our work, we achieved a high level of accuracy even when the noise power density is high, which means that our proposed approach is able to recognize the presence of a licensed user in 98% of the cases when the evaluated channel suffers a high level of noise power density (-134 dBm/Hz). This result was achieved with the cooperation of 10 unlicensed users.

Assuntos

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Cognitive radio networks (CRN) allow for an increase in spectral efficiency and performance of today's wireless networks. Currently, multiple proposals exist in the area of spectral decision-making and mobility; however, very few evaluate the impact of collaboration between secondary users and the performance of spectrum access by many secondary users. Unlike existing works, this article provides a comprehensive quantitative analysis of the performance of CRN taking into account access to the spectrum simultaneously by multiple users and decision making based on collaboration through the exchange of information between nearby secondary users. This proposal is developed through the implementation of four modules: Input Module, Multi-user Module, Collaborative module and Decision-making module, where the results are evaluated comparatively through the handoff rate generated with two multicriteria techniques: Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Criteria Optimization and Compromise Solution (VIKOR). The evaluation is carried out taking into account three levels of collaboration, three multi-user access scenarios, and two multi-criteria techniques for a total of 18 simulation scenarios. The results obtained show the importance of implementing collaboration strategies, as for multi-user access, the number of handoffs increases as the number of serial users increases. TOPSIS presented the best results in 76 % of the analyzed cases where VIKOR generated a smaller number of handoffs; TOPSIS maintained good performance with differences not exceeding 90 handoffs.

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Cognitive radio networks promote better spectral efficiency of the electric radio spectrum. The vast majority of current spectral decision models for cognitive radio networks evaluate their performance based on a single secondary user. In reality, the network can experience multiple requests from spectral opportunities. Based on this, the intent of this article is to present and evaluate a spectral decision model for cognitive radio networks in a multi-user environment taking into account the effect of the decisions of the SU on the usefulness of the other SU. To achieve this, a spectral decision model was developed that allows secondary users to share relevant information before accessing the spectrum so that they can select the most appropriate spectral opportunities. The evaluation and validation of the model was performed using three multicriteria decision-making algorithms under the metric of the number of total handoffs in a conventional scenario and a real scenario, in the conventional scenario, only users that match the input of the multiuser module are included; in the real scenario, in addition to the conventional users, users that enter and leave at random times are included, a feature that alters the models for estimating the behavior of the radio environment. The results show better performance of the TOPSIS algorithm over VIKOR and SAW. The most important contribution of this work is the evaluation of the performance of the spectral decision algorithms implemented in a multi-user environment that allows multiple access and exchange of information between users, with experimental spectral occupation data.

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The radio-frequency spectrum shortage, which is primarily caused by the fixed allocation policy, is one of the main bottlenecks to the deployment of existing wireless communication networks, and to the development of new ones. The dynamic spectrum access policy is foreseen as the solution to this problem, since it allows shared spectrum usage by primary licensed and secondary unlicensed networks. In order to turn this policy into reality, the secondary network must be capable of acquiring reliable, real-time information on available bands within the service area, which can be achieved by means of spectrum sensing, spectrum occupancy databases, or a combination of them. This Review presents guidelines related to the design of a framework that can be adopted to foster dynamic spectrum access policies. The framework applies special-purpose Internet of Things (IoT) devices that perform spectrum sensing, subsequently feeding a spectrum occupancy database, which in turn will be used by the secondary network to gather information on location-dependent spectrum availability. The guidelines address technological enablers capable of making the framework feasible, reliable and secure.

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Unmanned Aerial Vehicles (UAVs) demand technologies so they can not only fly autonomously, but also communicate with base stations, flight controllers, computers, devices, or even other UAVs. Still, UAVs usually operate within unlicensed spectrum bands, competing against the increasing number of mobile devices and other wireless networks. Combining UAVs with Cognitive Radio (CR) may increase their general communication performance, thus allowing them to execute missions where the conventional UAVs face limitations. CR provides a smart wireless communication which, instead of using a transmission frequency defined in the hardware, uses software transmission. CR smartly uses free transmission channels and/or chooses them according to application's requirements. Moreover, CR is considered a key enabler for deploying technologies that require high connectivity, such as Smart Cities, 5G, Internet of Things (IoT), and the Internet of Flying Things (IoFT). This paper presents an overview on the field of CR for UAV communications and its state-of-the-art, testbed alternatives for real data experiments, as well as specifications to build a simple and low-cost testbed, and indicates key opportunities and future challenges in the field.

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A very important task in Mobile Cognitive Radio Networks (MCRN) is to ensure that the system releases a given frequency when a Primary User (PU) is present, by maintaining the principle to not interfere with its activity within a cognitive radio system. Afterwards, a cognitive protocol must be set in order to change to another frequency channel that is available or shut down the service if there are no free channels to be found. The system must sense the frequency spectrum constantly through the energy detection method which is the most commonly used. However, this analysis takes place in the time domain and signals cannot be easily identified due to changes in modulation, power and distance from mobile users. The proposed system works with Gaussian Minimum Shift Keying (GMSK) and Orthogonal Frequency Division Multiplexing (OFDM) for systems from Global System for Mobile Communication (GSM) to 5G systems, the signals are analyzed in the frequency domain and the Rényi-Entropy method is used as a tool to distinguish the noise and the PU signal without prior knowledge of its features. The main contribution of this research is that uses a Software Defined Radio (SDR) system to implement a MCRN in order to measure the behavior of Primary and Secondary signals in both time and frequency using GNURadio and OpenBTS as software tools to allow a phone call service between two Secondary Users (SU). This allows to extract experimental results that are compared with simulations and theory using Rényi-entropy to detect signals from SU in GMSK and OFDM systems. It is concluded that the Rényi-Entropy detector has a higher performance than the conventional energy detector in the Additive White Gaussian Noise (AWGN) and Rayleigh channels. The system increases the detection probability (PD) to over 96% with a Signal to Noise Ratio (SNR) of 10dB and starting 5 dB below energy sensing levels.

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This work presents a novel spectral sensing method for the detection of signals presenting nonlinear phase variation over time. The introduced method is based on the angle-time cyclostationarity theory, which applies transformations to the signal to be sensed in order to mitigate the effects of nonlinear phase variation. The architecture is employed for sensing binary phase shift keying (BPSK) signals, being also compared with time cyclostationarity. The obtained simulation results clearly demonstrate the efficiency of the proposed approach, while presenting improved performance in terms of the detection rate of primary users increased by about 8 dB.

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This paper presents an approach to exploit the superimposed training (ST)-based primary users' (PUs) transmissions in the context of spectrum sensing for cognitive radio. In the low signal-to-noise ratio (SNR), the proposed scheme splits the spectrum sensing phase into two sample processing periods, allowing a secondary user (SU) to carry out a training sequence synchronization (with a small probability of error) before the implementation of a robust spectrum sensing algorithm that enhances the detection, based on the deterministic signal components embedded in the ST PU's signals along with the unknown data signal. The overall sensing performance is improved using a reasonable number of samples to achieve a high probability of detection, resulting in a reduced spectrum sensing duration. Furthermore, a low computational complexity version of the proposed ST combined approach for a reduced phase (SCAR-Phase) of spectrum sensing is presented, which attains the same detection performance with a smaller number of real operations in the low SNR. In the practical consideration of imperfect training sequence synchronizations, the results show the advantages of exploiting the ST sequence to perform spectrum sensing, thus quantifying the significant improvement in detection performance and the maximum SU's achievable throughput.

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This article addresses the impact of forward error correction when applied to the report channel transmissions of a centralized decision fusion cooperative spectrum sensing scheme designed to detect idle ofdma subchannels. The ofdma signal is transmitted over slow frequency-selective multipath Rayleigh fading channels and sensed using the maximum eigenvalue detection test statistic. The decisions on the OFDMA subchannel occupancy are transmitted to a fusion center over report channels represented by a shadowed fading model combining a three-dimensional spatially correlated shadowing with a slow and flat multipath Rayleigh fading. Binary bch and Repetition codes are used to protect these decisions. Results show that shadowing correlation severely deteriorates the overall spectrum sensing performance and that error correction may not be able to protect the report channel transmissions. It can be even worse with respect to the system performance especially at low signal-to-noise regimes. In the situations in which error correction is effective, the Repetition code is capable of outperforming the BCH, meaning that the diversity gain may be more relevant than the coding gain when the spectrum sensing decisions are subjected to correlated shadowing.

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We integrate, for the first time in the literature, the following ingredients to deal with emerging dynamic spectrum management (DSM) problem in heterogeneous wireless sensors and actuators networks (WSANs), Internet of things (IoT) and Wi-Fi: (i) named-based routing to provide provenance and location-independent access to control plane; (ii) temporary storage of control data for efficient and cohesive control dissemination, as well as asynchronous communication between software-controllers and devices; (iii) contract-based control to improve trust-ability of actions; (iv) service-defined configuration of wireless devices, approximating their configurations to real services needs. The work is implemented using NovaGenesis architecture and a proof-of-concept is evaluated in a real scenario, demonstrating our approach to automate radio frequency channel optimization in Wi-Fi and IEEE 802.15.4 networks in the 2.4 GHz bands. An integrated cognitive radio system provides the dual-mode best channel indications for novel DSM services in NovaGenesis. By reconfiguring Wi-Fi/IoT devices to best channels, the proposed solution more than doubles the network throughput, when compared to the case of mutual interference. Therefore, environments equipped with the proposal provide enhanced performance to their users.

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New wireless network paradigms will demand higher spectrum use and availability to cope with emerging data-hungry devices. Traditional static spectrum allocation policies cause spectrum scarcity, and new paradigms such as Cognitive Radio (CR) and new protocols and techniques need to be developed in order to have efficient spectrum usage. Medium Access Control (MAC) protocols are accountable for recognizing free spectrum, scheduling available resources and coordinating the coexistence of heterogeneous systems and users. This paper provides an ample review of the state-of-the-art MAC protocols, which mainly focuses on Cognitive Radio Ad Hoc Networks (CRAHN). First, a description of the cognitive radio fundamental functions is presented. Next, MAC protocols are divided into three groups, which are based on their channel access mechanism, namely time-slotted protocol, random access protocol and hybrid protocol. In each group, a detailed and comprehensive explanation of the latest MAC protocols is presented, as well as the pros and cons of each protocol. A discussion on future challenges for CRAHN MAC protocols is included with a comparison of the protocols from a functional perspective.

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This paper describes a novel scheme for the fusion of spectrum sensing information in cooperative spectrum sensing for cognitive radio applications. The scheme combines a spectrum-efficient, pre-distortion-based fusion strategy with an energy-efficient censoring-based fusion strategy to achieve the combined effect of reduction in bandwidth and power consumption during the transmissions of the local decisions to the fusion center. Expressions for computing the key performance metrics of the spectrum sensing of the proposed scheme are derived and validated by means of computer simulations. An extensive analysis of the overall energy efficiency is made, along with comparisons with reference strategies proposed in the literature. It is demonstrated that the proposed fusion scheme can outperform the energy efficiency attained by these reference strategies. Moreover, it attains approximately the same global decision performance of the best among these strategies.