Radio Networks Research Articles

Wideband spectrum sensing technique based on subspace compression for MIMO cognitive radio networks

Wideband spectrum sensing technique based on subspace compression for MIMO cognitive radio networks

Spectrum Sensing and Resource Allocation for Frequency Hopping Based CRN Using Hazelnut Tree Search Algorithm

This paper presents a novel spectrum sensing and resource allocation method for Cognitive Radio Networks (CRNs) using the Hazelnut Tree Search (HTS) algorithm in a frequency-hopping framework. CRNs enable secondary users (SUs) to utilize unused licensed spectrum without causing interference to primary users (PUs). The proposed Spectrum Sensing and Resource Allocation using Hazelnut Tree Search in CRN (SS-RA-HTS-CRN) method detects PU presence, prompting SUs to switch channels to avoid interference, guided by a designated guide node (GN). The HTS algorithm selects optimal sensing nodes and improves spectrum sensing, enhancing resource allocation reliability. Simulations in NS-3.26 show that SS-RA-HTS-CRN achieves delay reductions of 28.93%, 31.9%, and 22.45%, delivery ratio improvements of 28.21%, 17.41%, and 11.565%, and packet drop rate reductions of 35.4%, 44.3%, and 23.12% compared with Energy-Aware Resource Allocation with Backtracking Search Optimization (SS-RA-BSO-CRN), Spectrum Sensing with Hybrid Particle Swarm and Gravitational Search (SS-hybrid PSOGSA-CRN), and Spectrum Sensing using Modified Spider Monkey Optimization (SS-MSMO-CRN). The proposed SS-RA-HTS-CRN method significantly enhances efficiency, reliability, and adaptability in CRNs, making it a promising solution for dynamic spectrum management.

Dynamic multimedia transmission scheduling by unmanned aerial vehicle–mounted base stations for a vehicular ad hoc network

On highways, vehicle speed and topology changes are likely to cause transmission delays. In this study, a multimedia transmission system for highways is proposed; this system combines a next-generation cloud radio access network, which has a centralised architecture, with unmanned aerial vehicle (UAV)-mounted base stations (UAV-BSs), cellular network base stations, and roadside units. The deployment characteristics of the UAV-BSs are examined to improve the service of vehicular ad hoc networks and the quality of experience of users. A UAV location assignment algorithm and a transmission resource allocation algorithm for multiple bit rates, communication channels, and relay stations are proposed to reduce multimedia playback lag. We implemented a simulation programme to evaluate how the proposed algorithms outperformed other relevant methods. The simulation results indicate that the proposed algorithms achieve lower lag time than do existing demand- and transmission-based methods. The findings of this study have implications for the practical application of multimedia transmission decision support on highways.

Effective energy detection using machine learning techniques for cooperative sensing in cognitive radio networks

Effective energy detection using machine learning techniques for cooperative sensing in cognitive radio networks

UAVs Revolutionizing Efficiency: Integrating DRL and Random Walrus for Enhanced Energy and Spectral Resource Management

The improvement of energy efficiency and spectral efficiency in networks is achieved by seamlessly integrating energy harvesting, cognitive radio technologies, and Non-Orthogonal Multiple Access (NOMA) techniques. These complementary strategies optimize resource usage and address challenges related to energy consumption. Additionally, the adaptability and versatility of Unmanned Aerial Vehicles (UAVs) offer innovative solutions for enhancing coverage performance, thereby improving connectivity, efficiency, and reliability. We introduce a novel approach called the Deep Reinforcement Learning-Random Walrus (DRL-RW) algorithm, which combines Deep Reinforcement Learning (DRL) with the Random Walrus optimization (RWO) technique for efficient spectrum resource allocation and energy harvesting management in dynamic environments. The DRL-RW algorithm enables UAVs to learn optimal spectrum-sharing strategies and energy harvesting policies, while the RWO enhances the algorithm's adaptability and speed in exploring diverse solutions. Simulation results demonstrate the effectiveness of the DRL-RW algorithm, showing significant improvements in several performance metrics, including reduced energy consumption, enhanced computation time, improved convergence, increased signal-to-noise ratio, higher throughput, extended network lifetime, and increased harvested energy. These findings underscore the efficacy of the DRL-RW approach in addressing challenges associated with energy management in cognitive radio networks. The integration of UAVs, NOMA networks, and this novel algorithm represents a promising direction for developing energy-efficient communication systems.

Proof of Reputation: A Blockchain-Based Countermeasure to Defend Against Massive SSDF in Cognitive Radio Networks

Proof of Reputation: A Blockchain-Based Countermeasure to Defend Against Massive SSDF in Cognitive Radio Networks

Development of Enriched ROA With Dilated Hybrid Network for Automatic Modulation Classification Framework in CRNs

ABSTRACTAutomatic modulation classification (AMC) is explained as accurately identifying a modulation of a received signal. AMC systems are a significant component of cognitive radio network (CRN) systems. It is difficult to perform modulation classification on an unsettled radio signal without any previous knowledge of the signal's properties. In this work, the deep learning‐aided AMC is suggested to solve the difficulties of the existing models. In the proposed approach, the modulation classification is attained by performing two steps: (a) data collection and (b) classification. Initially, the required data related to the cognitive environment is collected from online resources. Later, the garnered data are passed to the classification phase. The AMC is performed by the adaptive and dilated hybrid network (ADHN), which is the combination of a temporal convolution network (TCN) and a gated recurrent unit (GRU). The ADHN accurately classifies the modulation even in a noisy environment. The classification performance of the ADHN is further boosted by tuning the parameters of this network via the enriched remora optimization algorithm (EROA). This proposed modulation classification model is suitable for various channels. The comparative validation is performed to ensure the usefulness of the designed system via several measures. By experimental analysis, the proposed system acquires the high value of accuracy, precision, and f1‐score by 94.2, 80.2, and 86.7, respectively, when compared with classical approaches. In addition to this, other metrics are considered and obtained with more true value and less false value. Thus, it ensures the effectiveness of classifying the modulation types in CRNs.

Comprehensive survey on security challenges and solutions in cognitive radio networks

Comprehensive survey on security challenges and solutions in cognitive radio networks

Enhancing Cognitive Radio Network Through the Novel Optimization Process and Deep Learning Model in the Transverse System

The dynamic distribution of spectrum, made possible by next-generation networks that utilise Cognitive Radio technology, helps to alleviate spectrum shortages. Additionally, these networks may be dynamically operated to save energy. The innovative cloud-sharing-decision-based method for optimising cognitive radio networks' wireless networks is introduced in this research. Optimisation was achieved via 3 key performance indicators: spectrum utilisation, power utilisation, and human revelation. Researcher find the best key performance indicators for a realistic India’s suburban situation. An optimisation technique in the cloud-based design for architecture simultaneously decreases networks power usage for about 27.6%, mean worldwide revelation by 34.4%, & spectrum utilisation for about 34.6% when related to a standard Cognitive Radio net. For the areas of networks power consumption (4.9%), spectrum utilisation (7.4%), and global exposure (4.4%), method outperforms the old design, even in the worst-case optimisation scenario.

UROLATAM Project: A multidisciplinary comprehensive care project for prostate cancer

Abstract Introduction Prostate Cancer (PCa) represents a health problem in the world and its management is heterogeneous in care, depending on the resources each country has, modifying its effectiveness in prevention, diagnosis, treatment and follow-up. Methods The UROLATAM program was implemented based on the Design Thinking quadruple helix innovation model, consisting of the immersion, design, idea and prototype phases, characterized by being multidisciplinary and comprehensive. Teachers and students from different careers at the Universidad Veracruzana participated. Linkages were made with: Local government, government health institutions, colleges of specialist doctors, private institutions and civil society. This program was broadcast on radio, television and social networks inviting the population to participate in education and prevention activities, in addition to participating in diagnosis, treatment and follow-up actions for men over 40 years of age. The duration of the program was 4 days, including educational sessions for doctors, patients, family members and caregivers, as well as determination of prostate-specific antigen, urological consultation and prostate ultrasound, as well as rehabilitation exercises, diagnostic confirmation and treatment. who needed it. Results During the sessions, 944 patients aged 60.7 (±15.0) years were treated, 443 (47%) had never received a urological consultation, identifying a prevalence of 11.7% of Cap. The human team present with the patient was 25 specialist doctors, 16 medical residents, 38 undergraduate students, 3 rehabilitators and 6 administrative staff. None of the participants received a salary for participating in this project. Conclusions The multidisciplinary UROLATAM linkage project constitutes a useful educational, training and assistance tool for the population, as well as an educational element for the students who are part of the work team. Key messages • The UROLATAM project is replicable in countries that require additional support in the prostate cancer approach. • The structure of the UROLATAM project can be replicated to address other public health problems.

Secrecy performance intelligent prediction for CRNs: An Self-CondenseNet approach

This paper studies the physical layer security (PLS) of cognitive radio networks (CRNs) with Fisher–Snedecor F distribution. To resolve the security issues within CRNs, we derived exact expressions of the security outage probability (SOP) and the probability of strict positive secrecy capacity (SPSC) for the first time, where the SOP and SPSC are uniformly given by Meijer’s G-function. The correctness of the theoretical derivations is proved by Monte Carlo simulations. The results indicate that reducing m and increasing the ratio of (ms,μE) will reduce SOP and increase SPSC. Moreover, we proposed the Self-CondenseNet model to predict the security performance of the system. By comparing with three deep learning algorithms of Transformer, MLP-Mixer and CondenseNet, the results show that the proposed Self-CondenseNet has the best prediction performance. Compared with the CondenseNet, the proposed Self-CondenseNet has a 78.26% higher accuracy and a 12.86% lower time complexity. Compared with the MLP-Mixer, the proposed Self-CondenseNet has a 85.29% higher accuracy. The comparison results show that the proposed algorithm has high prediction accuracy and low time complexity, and can be widely used in complex and changeable scenarios such as 5G, Internet of Vehicles (IoV), and mobile vehicle networking .etc.

Joint passive beamforming and deployment design for active intelligent reflecting surface-assisted energy harvesting-cognitive radio sensor networks.

To prolong the network lifetime of energy harvesting-cognitive radio sensor networks (EH-CRSNs), this paper integrates active intelligent reflecting surface (IRS) to support both downlink EH and uplink data transmissions and formulates a constrained non-convex optimization problem that maximizes the net energy gain. To solve this problem, a joint passive beamforming and IRS deployment mechanism is proposed to determine the optimal deployment location of the active IRS and optimally configure the IRS reflection coefficient matrices. Specifically, the net energy gain maximization problem at a specified location is divided into two sub-problems according to its characteristics: maximizing the cumulative energy harvested by all CRSNs nodes in the downlink and minimizing the energy consumption of cluster heads in uplink data transmissions, with each sub-problem being solved independently. Furthermore, this paper explores how factors such as the number of active reflecting elements and the amplification power budget influence the optimal deployment location of the active IRS. The findings from this investigation can guide the design of active IRS-assisted EH-CRSNs under specified parameters. Simulation results confirm the aforementioned conclusions and highlight the benefits of the proposed joint mechanism in enhancing net energy gain over various benchmark mechanisms.

On Radio k – Chromatic Number for Mycielski of Some Graphs

Objectives: In radio networks, the main difficulty is managing the radio spectrum, assigning radio frequencies to transmitters optimally without any interferences. This study aims to find the smallest span of Mycielski of some graphs, the maximum color assigned to any node is called span. Methods : This study focused on the problem of reducing interference by modeling it with a radio k - coloring problem on graphs. Where transmitters are modeled as nodes in a graph, with edges connecting nodes that represent transmitters in close proximity to one another. For a graph , with node set , edge set and an integer , a radio k - coloring of is a function satisfying the condition for any two nodes and , where is the distance between and in . The radio k - coloring of , is the maximum color assigned to any node of and it is denoted by . The radio k -chromatic number of is the minimum value of taken over all radio k - coloring of and it is denoted by . Findings: This study obtained the radio k-chromatic number for Mycielski of some graphs for and 3 Novelty: To solve the channel assignment problem in radio transmitters, the interference graph is developed, and the channel assignment has been converted into a graph coloring. Reducing the interference by a radio k - coloring problem will motivate many researchers to find the radio k - coloring in various graphs. Keywords: Radio k – Coloring, Mycielski graph, Double Star graph, Triple Star graph, Sunlet graph, Helm graph and Closed Helm graph

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.

Enhanced Cooperative Compressive Spectrum Sensing in Cognitive Radio Networks

ABSTRACTThe demand for bandwidth in cognitive radio networks (CRNs) is growing as applications become increasingly data intensive. Techniques such as compressed spectrum sensing (CSS) and cooperative spectrum sensing (C‐SS) are employed to address this challenge. C‐SS enhances overall detection accuracy and reliability by enabling multiple nodes to share and combine their local sensing data. Conversely, CSS effectively reduces the required information for spectrum usage decision making, thereby improving bandwidth utilization. Integrating these two methods allows CRNs to utilize the spectrum reliably and efficiently, leading to increased spectral efficiency. To further improve reconstruction performance, we leverage the sparsity concept to transcend hardware constraints and merge restrictions from both real and synthesized channels. This approach involves the virtual synthesis of channels, which linearly enhances the signal‐to‐noise ratio (SNR) within the network's size range. Simulation results demonstrate that our proposed method offers significant advantages over single‐node recovery, as validated by simulations and software defined radio (SDR) Implementation. The integration of spectral estimations from various local CR detectors enhances spatial diversity gain and sensing quality, particularly in fading channels. Compared to traditional approaches, our method achieves superior performance, evidenced by an increase in (from 93.97% to 96.52%) with almost the same .

Arithmetic Optimization Algorithm Quantization Optimized With Energy Detection Using Nonparametric Amplitude

ABSTRACTThe spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection‐based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED‐NAQ‐AOA‐SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro‐fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS).

Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio Networks

The exponential growth and proliferation of wireless devices for different wireless applications have led to the emergence of cognitive radio network (CRN) for optimal utilization of scarce spectrum resources. However, these resources have grossly been under-utilized due to the inaccurate spectrum predictions. Existing spectrum occupancy and prediction techniques which rely on 2-state hidden Markov model (HMM) results in false alarm or missed detection caused by noisy or incomplete observable effects. In this paper, a 3-state HMM spectrum occupancy and prediction technique in CRNs is proposed. The transmission, emission and initial state probabilities of the proposed 3-state HMM parameters were derived based on the three canonical problems associated with HMM. The evaluation, decoding and learning problems were solved using Forward algorithm, Viterbi algorithm and the Baum-Welch algorithm, respectively. The performance of the proposed 3-state HMM spectrum prediction technique was evaluated using prediction accuracy, probability of detection and spectrum utilization efficiency. The simulation results obtained revealed that the 3-state HMM outperformed the 2-state HMM spectrum prediction technique by 24.1% in prediction accuracy.

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.

Cross-attention mechanism-based spectrum sensing in generalized Gaussian noise

Spectrum sensing (SS) technology is essential for cognitive radio (CR) networks to effectively identify and utilize idle spectrum resources. Due to the influence of noise characteristics in the channel, providing accurate sensing results is challenging. In order to improve the performance of SS under non-Gaussian noise and overcome the limitations of existing methods that are mostly based on a single feature, we propose a novel time-frequency cross fusion network (TFCFN). Specifically, we utilize gated recurrent units (GRU) to capture long-term dependencies in the time domain on the original signals, meanwhile, we perform a fast Fourier transform (FFT) on the original signals to obtain the frequency domain information, and subsequently use convolutional neural networks (CNN) to extract the local spatial features in the frequency domain. Ultimately, these time-domain and frequency-domain features are dynamically fused through a cross-attention mechanism to construct more comprehensive and robust features for signal classification. We use generalized Gaussian distribution (GGD) as the noise model and reconstruct the RadioML2016.10a dataset to explore the performance under various noise conditions. The experimental results show that compared with the baseline methods, TFCFN exhibits better detection ability and maintains lower complexity in both Gaussian and non-Gaussian noise environments. Notably, when the shape parameter of GGD is set to 0.5 and the signal-to-noise ratio (SNR) of the received signal is -16dB, it can maintain the probability of false alarm (Pf\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_f$$\\end{document}) of 10% while still ensuring the probability of detection (Pd\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_d$$\\end{document}) of over 90%.