Secondary Users Research Articles

Deep learning-based spectrum sensing and modulation categorization for efficient data transmission in Cognitive Radio

Abstract One prominent feature of cognitive radio (CR) involves spectrum sensing (SS), which allows licensed primary users to remain unaffected by secondary users' ability to discover and exploit unoccupied frequency bands. Spectrum sensing enhances the use of spectrum in CR devices, increasing their adaptability and efficiency in wireless communication systems. The rise of wireless equipment and the advent of IoT technologies compound this need for flexibility. Over time, the fixed allocation of frequencies has led to inefficiencies and underutilization as bandwidth needs increase. Deep learning and artificial intelligence have improved spectrum sensing by increasing detection probability of primary users presence under noisy environments, enabling cognitive radio systems to respond intelligently to fluctuations in RF environments. This article is concerned with deep learning techniques for spectrum sensing and modulation categorization with CBRT structure, which combines convolutional neural networks (CNNs), bidirectional recurrent neural networks (BRNNs), and transformer networks (TNs) to improve spectrum sensing. CNNs are responsible for performing spectrum feature extraction; BRNNs are used to capture temporal dependencies; and TNs are good at long range dependencies. Better performance for this model is aimed by integrating the three architectures described. In the proposed work, we considered six digital modulation schemes, for spectrum sensing. The sensing of spectrum in this model is performed using the RadioML2016.10B open-source dataset and performance metrics like the Jaccard Index (JI), Fowlkes’s Mallows Index, and F1 Score. Modulation classification has been performed using MIGOU-MOD open-source dataset. The proposed model exhibits good detection probability and sensing error, unlike other methods at lower SNR.

Experiences from the European Region: case studies and scenarios to inform ways forward in digital health

Abstract Member States in the WHO European Region face similar challenges when it comes to strengthening their HIS and implementing digital health solutions. From WHO/Europe’s work with its Member States, several important preconditions have become apparent for successful and sustainable digital transformation. Firstly, the importance of recognising that implementing digital tools is not a one-off investment, but that this takes a long-term vision including long-term budgeting. Having business continuity plans also is a key element of sustainable implementation of digital tools, that is often not adequately addressed in practice. Secondly, the importance of comprehensive strategic planning. Implementing digital tools is not only about software and hardware, but also about people and processes. Here it is also crucial for policymakers to understand that, to be able to harvest the benefits of data that are gathered by means of digital tools, resources for data processing and analysis also need to be made available. Last but not least, an important precondition for successful digital transformation is stakeholder involvement. Often, new software is developed without adequately involving the end-user, resulting in for example apps that do not meet patients’ needs. Or in electronic health record systems that are not user-friendly for the healthcare staff that need to enter the data, with incomplete and low quality information as a consequence. Stakeholder involvement is essential in strategic planning for digital health as well. Think for example about standardization and accreditation agencies, and institutes that will be secondary users of data for statistics and research. In this presentation, several case studies from across the European Region will be presented, illustrating how digital transformation in health can be successfully and sustainably implemented.

Joint channel allocation and transmit power control for underlay EH‐CRNs: A clustering‐based multi‐agent DDPG approach

AbstractTo address the concerns of energy supply and spectrum scarcity for wireless devices, energy harvesting cognitive radio networks have been proposed. To improve spectrum utilization, secondary users (SUs) access the licensed spectrum in underlay mode, which may cause severe interference to primary users and SUs. The focus is on the underlay energy harvesting cognitive radio networks with multiple pairs of SUs, and formulate the long‐term secondary throughput maximization problem as a mixed‐integer non‐linear programming problem. As traditional approaches could hardly solve the mixed‐integer non‐linear programming problem well, a centralized deep deterministic policy gradient (C‐DDPG) approach is proposed that achieves satisfactory throughput performance. To reduce the computational complexity of C‐DDPG, we further propose a clustering‐based multi‐agent DDPG (CMA‐DDPG) approach that combines the advantages of the centralized deep reinforcement learning approach and the distributed deep reinforcement learning approach. In the CMA‐DDPG, a novel interference‐based clustering algorithm is proposed, which partitions the SUs that cause severe mutual interference into one cluster, and the sizes of state space and action space are smaller than those in C‐DDPG. Numerical results validate the superiority of the proposed approaches in terms of the throughput and outage probability, and validate the clustering performance of the interference‐based clustering algorithm in terms of the outage probability of the secondary network.

Empirical Mode Decomposition Based Amplify and Forward Technique for Cooperative Cognitive Radio System

The rapid growth in the mobile industry due to increase in number of users accessing diverse services causes a high demand for radio spectrum. Nonetheless, the radio spectrum allocated for different wireless communication services is restricted. Cooperative Cognitive Radio (CCR) technique with Amplify and Forward (AF) relaying protocol used to address the problem suffers from noise amplification resulting in poor reception at the destination. Hence, in this paper, Empirical Mode Decomposition (EMD) based AF technique for CCR system was carried out to improve the performance of the existing CCR with AF relaying protocol. The transmitted signal from Primary User (PU) was received at the Secondary User (SU) where SU superimposed its own signal using Exclusive OR (XOR) rule. The combined signal from XOR was made to pass through EMD and amplified using AF by multiplying with the relay gain. The amplified signal was radiated to PU and SU receivers during the second hop transmission. The multiple copies of the receive signal at the SU receiver at different number of path (L = 2, 4) were combined at destination using Maximal Ratio Combiner (MRC). Mathematical expression for Bit Error Rate (BER) and Throughput (TP) were derived using the Probability Density Function (PDF) of the Nakagami-m fading distribution. Extensive simulations using MATLAB R2021a were employed to assess the effectiveness of the proposed technique and evaluated using BER and TP by comparing with the existing AF CCR. The EMD based AF technique proposed gave better performance with 75.2% reduction in BER and 21.86% increase in TP over the existing AF technique for CCR. The proposed technique can be deployed to improve the performance of cooperative cognitive radio system.

Cognitive radio inspired RSMA-MEC under hardware impairments: Performance analysis and optimization

In this paper, we investigate a cognitive radio (CR) inspired rate splitting multiple access (RSMA) aided mobile edge computing (MEC) network, where hardware impairments (HIs) in all transceivers are considered. Given that allowing the secondary user (SU) to share the resource with the primary user (PU) for data offloading does not degrade the PU’s offloading performance, we derive closed-form expressions for the rate-splitting parameter and power allocation coefficient at the SU to maximize its offloading rate. Using the derived parameters, we then derive the successful computation probability (SCP), which is defined as the probability that both the PU and SU can successfully compute their task bits within a given latency budget, into the closed form. To further enhance the performance of the CR inspired RSMA-MEC network with HIs, a SCP maximization-based problem is formulated to jointly optimize the task offloading ratio and task offloading time of both the SU and PU. Leveraging the convex theory, we obtain the optimal solutions in the closed form. Simulation results confirm the following two insights. First, the presence of HIs leads to a decreasing on the SCP and the SCP approaches to a constant which is less than 1 with the increase of the transmit power. Second, with the optimal task offloading ratio and time, the CR inspired RSMA-MEC achieves the highest SCP compared to the existing schemes.

Quantum cryptography in the security of cognitive radio networks

Cognitive radio networks face challenges from malicious users who aim to disrupt decision-making during spectrum sensing and decrease spectrum efficiency. The proposed model seeks to distinguish between the presence and absence of primary user signals using energy detection and also it introduces a quantum-inspired outlier detection mechanism for cognitive radio networks to enhance security and reliability during spectrum sensing by ignoring the identified outliers. It employs BB84 quantum key distribution (QKD) protocol and one time pad for secure transmission of secondary user (SU) local decisions to the fusion center (FC), ensuring data integrity and confidentiality. These decisions are transmitted securely establishing a shared secret key between SU and FC and arrives at a global decision. To make the model more robust, we proposed another novel model with a modified BB84 protocol and compared the results of both the models, offering a robust solution for enhancing the reliability and performance of cognitive radio networks with proven results from simulation.

Analysis of NR and LTE Target Signal Structure Based on Neural Network Approach and Deep Learning Methods

Purpose: the rapid development and consolidation of broadband wireless access networks of the fourth 4G LTE and fifth 5G NR generations determine the need to find ways to reuse frequencies. A well-known solution to this problem is the concept of cognitive radio. The use of artificial intelligence in radio networks in general and the use of a neural network approach with deep learning for recognizing signals of LTE and NR standards in particular have serious potential for the practical implementation of frequency resource reuse according to the concept of cognitive development. The aim of the work: is to analyze models, methods and algorithms that allow recognizing signals of the NR and LTE standards based on recording samples of radio signals obtained using pre-recorded NR and LTE signals in MatLab software. In this paper, the procedures for scanning and recognizing sections of the spectrum are considered. The operations of training a semantic segmentation network for the identification of these signals in a broadband spectrogram and the use of a trained network for subsequent spectrum sensing and recognition of signals of the NR and LTE standards are investigated. Methods: the method used to test the concept of cognitive radio is spectrum sensing, which results in information about the occupancy of frequency bands in a given location by primary users, and the identification of spectrum sections available for use by secondary users without interfering with the work of primary users in real time. The novelty of the work is the use of a neural network approach in the analysis of the target NR and LTE signal. Results: the model, trained on the basis of a neural network approach and deep learning methods, is able to distinguish between signals of the NR and LTE standards. Theoretical / Practical relevance: the software implementation of spectral sensing is implemented using an extension package in the MatLab environment and allows for experimental testing of a cognitive radio receiver when performing procedures for analyzing the structure of the target signal of the NR and LTE standards based on a neural network approach and deep learning methods.

A hybrid binary logical regression -gradient decent approach for dynamic cooperative spectrum sensing in CRN

ABSTRACT This paper proposes hybrid binary logical regression with a gradient decent optimisation (GDO) algorithm for spectrum sensing. From multiple secondary users (SU) systems, all signal vectors are collected with cognitive radios (CRs), and the features associated with the signal vector are considered for decision statistics. The decision statistics are modelled with a hybrid binary logical regression with gradient decent (BLR-GD) algorithm, which improves efficiency. The gradient decent (GD) is accomplished with binary logical regression (BLD), in which the regression coefficients are calculated efficiently. For evaluating the performance of the proposed algorithm, the spectrum sensing is evaluated in low signal-to-noise ratio (SNR) and high SNR scenarios. The detection probability, accuracy, F-measure and ROC curve performance of the proposed approach will outperform other existing approaches. The latency value of the proposed approach has reached 0.009 ms, which shows the efficiency of the proposed approach with 5 G communication features.

Healthy Eating and Active Lifestyles for Diabetes (HEAL-D) Online: a mixed methods evaluation exploring the feasibility of implementing a virtual culturally tailored diabetes self-management programme for African and Caribbean communities

ObjectivesTo assess the feasibility and acceptability of delivering Healthy Eating and Active Lifestyles for Diabetes (HEAL-D) Online.InterventionHEAL-D Online—a 7-week culturally tailored type 2 diabetes educational programme delivered using online platform.SettingProgramme...

Energy consumption optimization in green cognitive radio networks based on collaborative spectrum sensing

In the realm of green communications, the focus is on achieving high spectrum efficiency and low energy consumption. This paper addresses the crucial goal of reducing energy usage in green cognitive radio networks (CRNs) during communication between secondary users (SUs) and primary users (PUs). This paper proposed an energy consumption optimization model (ECOM) for green CRN utilizing collaborative spectrum sensing thereby minimizing the environmental impact and prolonging the operational lifetime of devices. The collaborative spectrum sensing proved its role in optimizing the energy consumption in the green CRN. An energy-efficient scheduling algorithm is implemented in ECOM, in which the SUs can be scheduled to perform their sensing process in a time-division manner to reduce energy consumption. Applied collaborative spectrum sensing serves as a valuable resource for researchers, network operators, and policymakers seeking to balance the increasing demand for wireless communication services with the imperative of sustainability. The simulation results and mathematical proof emphasize that ECOM demonstrates reduced energy usage and increased average effective throughput when compared to other recent models.

Priority-based band sharing for secondary users in cognitive radio networks

To improve wireless spectrum utilization, cognitive radio networks (CRNs) allow secondary users (SUs) opportunistic access to the frequency bands owned by primary users (PUs), when such PUs are inactive. A fully distributed CRN architecture is quite favorable, as it does not require any centralized scheduling, nor does it require coordination between SUs via a common control channel. This can reduce cost and avoid single point-of-failure issues. However, uncoordinated access to the spectrum by competing SUs, if not done judiciously, can cause excessive collisions and limit performance, which can be detrimental to the usefulness of CRNs, especially if some SUs have urgent traffic to send. Hence, we present a novel protocol to support traffic with multiple classes of priority within a fully distributed CRN. In such protocol, competing SUs attempt to access the spectrum autonomously, but rationally, based on their priority level, opting to transmit their packets when it is beneficial to both the SU itself and also to the overall system performance. Simulations confirm that the proposed protocol achieves excellent overall network performance, while ensuring that higher priority packets experience higher throughput and smaller delay compared to lower priority packets.

Monitoring and enhancing the co-operation of IoT network rhrough scheduling function based punishment reward strategy

The Internet of Things (IoT) has revolutionized the connectivity of physical devices, leading to an exponential increase in multimedia wireless traffic and creating substantial demand for radio spectrum. Given the inherent scarcity of available spectrum, Cognitive Radio (CR)-assisted IoT emerges as a promising solution to optimize spectrum utilization through cooperation between cognitive and IoT nodes. Unlicensed IoT nodes can opportunistically access licensed spectrum bands without causing interference to licensed users. However, energy constraints may lead to reduced cooperation from IoT nodes during the search for vacant channels, as they aim to conserve battery life. To address this issue, we propose a Punishment-reward-based Cooperative Sensing and Data Forwarding (PR-CSDF) approach for IoT data transmission. Our method involves two key steps: (1) distributing sensing tasks among IoT nodes and (2) enhancing cooperation through a reward and punishment strategy. Evaluation results demonstrate that both secondary users (SUs) and IoT nodes achieve significant utility gains with the proposed mechanism, providing strong incentives for cooperative behaviour.

Energy‐efficient channel selection algorithm with reduced collision probability in cognitive radio networks

AbstractThe growing demand for radio spectrum, driven by widespread communication devices, has led to spectrum congestion. Cognitive radio networks (CRNs) offer a solution by allowing secondary users (SUs) to access primary users' (PUs) channels without causing interference. This paper presents an energy‐efficient channel selection algorithm aimed at reducing collision probability and energy consumption during spectrum sensing. A prediction‐based model forecasts PU channel availability, enabling selective sensing. The results show a significant reduction in energy consumption, with the proposed method consuming 57.9% energy compared to 63.7% for a recent existing method. The proposed approach enhances spectrum management in next‐generation wireless systems by minimizing interference and optimizing energy use.

Bi-directional Long Short-Term Memory with Bird Mating Optimizer based Spectrum Sensing Technique for Cognitive Radio Networks

Cognitive radio networks (CRN) enable the wireless devices to sense the radio spectrum, determine the frequency state channels, and reconfigure the communication variables for satisfying the QoS needs by reducing the energy utilization. In cognitive radio, the detection of principal user signals is crucial for secondary users in order to make the best use of available spectrum (CR). The problem with conventional spectrum sensing approaches is that they have a high rate of missed detections and false alarms, which makes it difficult to make effective use of the spectrum. In instruction to recover the correctness of the detection of free spectrum, deep learning-based spectrum sensing is employed. For resolving the drawbacks of traditional energy detection models, this paper presents a new spectral sensing technique for cognitive radio networks (SST-CRN). Recently published research in spectrum sensing has placed a high value on deep learning that is model-agnostic as a result of this. In particular, long-short term memory (LSTM) networks perform exceptionally well at extracting spatial and temporal information from input data in deep learning. The proposed model, Bidirectional Long Short-Term Memory (Bi-LSTM) with Bird Mating Optimization (BMO), makes it possible to create nonlinear threshold-based systems more quickly and easily than previously possible. The proposed Bi-LSTM with BMO technique involves two stages of operations namely offline and online. The offline stage creates the non-linear threshold value to detect energy. In addition, the online stage automated selects a decision function saved in offline stage for determining the existence of primary user. The experiments were carried out and the results were analyzed using the help of the RadioML2016.10b dataset. A greater spectrum detection performance over previous sensing models has been obtained using a combination of the B-LSTM model and the BMO model, it has been discovered.

Blockchain-Based Spectrum Sharing Algorithm for UAV-Assisted Relay System

Unmanned aerial vehicles (UAVs) are promising tools in mobile communication due to their flexibility. However, the rapid development of mobile communications further intensifies the challenge of spectrum scarcity, necessitating spectrum sharing with other systems. We suggest a Spectrum Sharing Algorithm for a UAV-Assisted Relay System. The utility function of secondary users (SUs) is defined by their communication rate, rewards from relay primary users (PUs), and spectrum leasing expenses. The utility function of PUs consists of their communication rate and revenue from spectrum leasing. We propose a joint optimization algorithm for the positioning and power allocation of UAVs, maximizing the frequency spectrum utilization rate of users. Spectrum trading between PUs and SUs is modeled as a Stackelberg game, and the problem is solved by using Lagrange multipliers and KKT conditions. To enhance the security of spectrum trading, a reputation-based spectrum sharing blockchain consensus algorithm is designed. We utilize Shamir’s secret sharing method to reduce computational complexity. Additionally, we design a smart contract to optimize the functionality of transaction transfers. The findings demonstrate that the proposed algorithm enhances the benefits for both participants in spectrum sharing, while safeguarding the security of spectrum transactions.

Blockchain-enabled cooperative spectrum sensing in 5G and B5G cognitive radio via massive multiple-input multiple-output nonorthogonal multiple access

One of the greatest ways to guarantee that networks designed for fifth generation (5G) and beyond reach the required levels of spectrum efficiency (SE) is through nonorthogonal multiple access (NOMA). This work presents two new blockchain-based techniques that take advantage of massive multiple input multiple output in a single-cell network to improve performance. NOMA power domain is used in the 5G cooperative cognitive radio network (CCRN) to improve the SE of the downlink. This study investigates a novel cooperative NOMA-based CCRN for underlay spectrum sharing. A cooperative NOMA technique is proposed by considering the access modes of relays and secondary users (SUs) on the secondary network. The proposed system's performance is assessed with respect to random channel characteristics, frequency-selective Rayleigh fading and perfect successive interference cancellation (SIC). The first signal decoded by SU identifies the relay states with the best channel quality between users and the destination users to offset the bit error rate. The throughput dropped during the period because of the perfect SIC. The precise closed-form expressions for the system throughput of the secondary network are derived under the interference constraint of the primary network to evaluate the efficacy of the suggested cooperative strategy. The suggested approaches are assessed under various conditions using the MATLAB application by considering varying transmit power levels, power location coefficients and lengths. In every case, four users are assumed to be using a 90 MHz bandwidth and M-ary Quadrature Amplitude Modulation technology.

A hybrid deep learning based approach for spectrum sensing in cognitive radio

The primary user (PU) transmission is sporadic in nature, which explains why the PU is inactive during some time slots, geographic directions or frequency bands. The frequency bands where the PU is not active are called "spectrum holes". Secondary users (SUs) periodically perform sensing to detect the spectrum holes and monitor primary spectrum. For the best possible spectrum utilization, PU signal detection is very crucial. For measuring the spectrum sensing performance, two main metrics are applied, like, probability of false alarm (PFA) and probability of detection (PD). Due to PFA and PD, the conventional sensing techniques have to face issues. These two constraints used to hinder spectrum utilization. Traditional sensing strategies are mostly based on feature extraction of received signal. Advancement of artificial intelligence (AI) has reduced the inaccuracy in detection of spectrum hole. Deep learning (DL) based approaches have shown a remarkable improvement in this aspect. Hence, the present research work was undertaken to address the problem of spectrum sensing in low SNR and improves accuracy. This research penetrates into the use of deep neural network (DNN) for sensing the vacant spectrum accurately. In this article, RadioML2016.10b dataset was used for the experiments. The results are also studied. The proposed approach shows betterment in sensing than other existing spectrum detection models. DeepSenseNet model was validated through simulation results and showed that it has achieved 98.84% prediction accuracy (Pa) with 97.53% precision and 97.62% recall.

Performance analysis of shared relay CR-NOMA network based on SWIPT

With the development of wireless communication technology, the number of devices accessing the communication network is increasing. This paper addresses critical issues such as low spectrum resource utilization and the energy constraints of devices. The investigation focuses on the system performance of the shared relay Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) network based on Simultaneous Wireless Information and Power Transfer (SWIPT) network model. Unlike the existing CR-NOMA model in which the secondary network user do not participate in the transmission of information from the primary network, we consider the secondary network near user as shared relay. The shared relay utilizes SWIPT technology to harvest energy using a nonlinear energy harvesting model. Additionally, the shared relay assists in transmitting information from the primary network base station to primary user, as well as from the secondary network base station to far user of the secondary network. Subsequently, we conduct a series of simulations to analyze the effects of Signal-to-Noise Ratio (SNR), power distribution factor, interference threshold, and time-switching (TS) factor on system performance. Furthermore, we compare and analyze the performance of our proposed network model against CR-NOMA network across three dimensions: outage probability, throughput, and energy efficiency. Our results demonstrate that the proposed network model exhibits superior outage performance and enhances user throughput compared to the CR-NOMA network. Additionally, it demonstrates improved energy efficiency compared to the shared relay CR-NOMA network, leading to an overall improvement in network performance.

Cognisseum: Cognitive radios on Colosseum facing adversaries

Cognitive radio technology brings a lot of interesting features which affect the transmission and reception properties of modern communication devices. Dynamic spectrum sensing, channel hopping and allocation, and software-based control are among the many. The new features allow strategic defense mechanisms while also enabling more capable adversarial attacks. In this work, we study coalitions of secondary users (SUs) against adversaries. In the presence of primary users (PUs), we inspect the behavior of SU pairs in cognitive radio networks, before and after adversarial attacks. We propose algorithms for forming coalitions among SU pairs. We consider two attack strategies for the adversaries: smart or naïve. We study how the channels are allocated if there is an attack and how the payoffs of those SU pairs vary with varying number of channels. We also show the effects of attack from the attackers’ point-of-view and how the attack strategy changes if the adversaries act smart vs. naïve. Using Colosseum, a large-scale wireless channel emulator, we construct a functional cognitive radio network and use its software-defined radio (SDR) hardware as SU and adversarial nodes. Using this setup, we run experiments and record data by running network performance measurement tool iPerf3 for various coalitional setups.

Performance analysis of CDRT based cognitive NOMA vehicular networks with ambient backscatter relays

In order to improve the increasing spectrum requirements and the unguaranteed quality of service (QoS) for edge users in the internet of vehicles (IoV), many techniques such as cognitive radio (CR), non-orthogonal multiple access (NOMA) and coordinated direct and relay transmission (CDRT) have been proposed. To further enhance the spectrum utilization, this paper combines the above techniques with ambient backscatter communications (AmBC), which replaces the traditional relay in the network with a backscatter device (BD). When BD meets certain energy constraints (EC), an optimal dynamic reflect coefficient (RC) is enabled to promote the reflection effect. In this work, firstly, establish a CDRT-based CR-NOMA vehicular network with an ambient backscatter relay. Secondly, the closed expression for the outage probability of each secondary user is obtained with the help of Gauss Laguerre quadrature and Gauss Chebyshev quadrature. Then the system throughput analysis and the asymptotic analysis of each user when the maximum transmitting power as well as the interference temperature constrains (ITC) tends to infinity are performed. Finally, we performed simulations to reveal the impact of AmBC and ITC on edge users. Comparisons are also made with the system using fixed RC, the conventional system and its orthogonal multiple access (OMA) counterpart, demonstrating the superiority of our proposed system in improving the performance of edge users.