Cognitive Networks Research Articles

ED-SS based Cognitive Radio (CR) over Various Fading Channels for Modern Wireless Communications

Cognitive Radio (CR) has already been proved to provide the better solution for the problem of spectrum insufficiency in which every wireless system occupies the limited bandwidth spectrum (LBS) allotting to the licensed users. Sometimes, this LBS is empty in a certain place at a certain time that is called as spectrum holes estimated by spectrum sensing (SS). The spectrum sensing is the main technique used in cognitive radio and various spectrum sensing techniques are available in the literature such as: energy detection (ED), matched filter detection (MFD) and cyclo-stationary feature detection (CFD). In the present work, energy detection-spectrum sensing (ED-SS) based cognitive radio system has been implemented which can analyze the energy of the spectrum. The various fading channels such as additive white gaussian noise (AWGN), Rayleigh, and Nakagami have been incorporated in the present work over which the performance of cognitive radio has been analyzed and compared using the ED-SS technique. The experimental results show thatt the detection of the presence of primary user signal using proposed ED-SS technique is easier with low computational complexities in Cognitive Radio (CR) networks. The present work also demonstrates when the performance of proposed ED-SS technique over aforesaid three fading channels are compared, Nakagami-m fading channels exhibits superior improvement in Pd compared to Rayleigh and AWGN fading channels

Uncovering the origins of mental images about the concept of light: the difference between novice and expert mediation levels profile

This study investigates the mental imagery formed by two Physics undergraduates following their interaction with a mobile application analyzing emission and absorption light spectra. The tool, an instance of extracerebral cognitive processing, served to illuminate the internal cognitive mechanisms the students employed to understand novel knowledge. Guided by the Cognitive Networks Mediation Theory (CNMT), which underscores the significance of external tools in facilitating cognition, and drawing upon Bachelard's Epistemological Profile and Mortimer's Conceptual Profile, a novel 'Mediation Level' Profile is proposed. This profile represents the preferred type of mediation each student employed to elucidate their observations and understandings. The research utilized the Report Aloud Protocol to analyze students' verbal and gestural communications, revealing a complex pattern of various mental images developed through interaction with external objects via different levels of mediation. Notably, the psychophysical and hypercultural mediation levels stood out among the students. The implications of these findings for Physics education and future research directions are discussed, underscoring the utility of mobile applications as a supplementary tool in laboratory settings and the complexity of mental imagery in understanding physical phenomena.

Short communication: Lifetime musical activity and resting-state functional connectivity in cognitive networks.

Participation in multimodal leisure activities, such as playing a musical instrument, may be protective against brain aging and dementia in older adults (OA). Potential neuroprotective correlates underlying musical activity remain unclear. This cross-sectional study investigated the association between lifetime musical activity and resting-state functional connectivity (RSFC) in three higher-order brain networks: the Default Mode, Fronto-Parietal, and Salience networks. We assessed 130 cognitively unimpaired participants (≥ 60 years) from the baseline cohort of the DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE) study. Lifetime musical activity was operationalized by the self-reported participation in musical instrument playing across early, middle, and late life stages using the Lifetime of Experiences Questionnaire (LEQ). Participants who reported musical activity during all life stages (n = 65) were compared to controls who were matched on demographic and reserve characteristics (including education, intelligence, socioeconomic status, self-reported physical activity, age, and sex) and never played a musical instrument (n = 65) in local (seed-to-voxel) and global (within-network and between-network) RSFC patterns using pre-specified network seeds. Older participants with lifetime musical activity showed significantly higher local RSFC between the medial prefrontal cortex (Default Mode Network seed) and temporal as well as frontal regions, namely the right temporal pole and the right precentral gyrus extending into the superior frontal gyrus, compared to matched controls. There were no significant group differences in global RSFC within or between the three networks. We show that playing a musical instrument during life relates to higher RSFC of the medial prefrontal cortex with distant brain regions involved in higher-order cognitive and motor processes. Preserved or enhanced functional connectivity could potentially contribute to better brain health and resilience in OA with a history in musical activity. German Clinical Trials Register (DRKS00007966, 04/05/2015).

Resource allocation of cognitive relay networks under continuous transmission mechanism

This paper considers the joint power allocation, path selection and subcarrier pairing design of cognitive radio cooperative networks, where the transmission power of secondary users is limited by its own power budget and the tolerable interference threshold of primary users. In particular, a continuous transmission mechanism rather than intermittent emission, leading to severe signal interference between primary and secondary users, as well as mutual interference between secondary users, is taken into account. This cooperative cognitive radio network is seen as an improvement in the sense that the source transmitter can continuously transmit symbols without silence. Although traditional cooperative network technology has long been applied to the cognitive radio field and it has been proposed to allow continuous transmission signals by source transmitter, the deployment of such improved cooperative behavior in cognitive radio networks has not been reported yet. This improvement assumption may render serious mutual interference between the relay station and the destination receiver, and the signal interference borne by the primary user will also exceed the threshold value, if there is no elaborate signal design and resource scheduling. In order to make full use of the available resources, this paper presents the closed-form solution of the power allocation, the decision criteria of the path selection and the subcarrier pairing technique for the problem of maximization of the sum rate of secondary users in such networks under the dual constraints of the power budget and the interference threshold. Simulation results show that the continuous transmission behavior at the source significantly outperforms the traditional switching back and forth between on and off.

CRTrack: Learning Correlation-Refine network for visual object tracking

Conducting reliable feature interaction plays a critical role in the visual tracking community, especially in recent dominated Siamese-based tracking paradigm. In general, there are two primary approaches for fusing representations from template and search area in the Siamese setting, i.e., cross-correlation and transformer modeling. The former provides a straightforward interaction solution, which may have limitations in handling complex scenarios, such as appearance variations and occlusion. While the latter offers an effective interaction mechanism, albeit with higher computation complexity and model cost. In contrast to traditional Siamese-based trackers which rely on two mentioned feature cross-correlation operators, this paper proposes a novel Correlation-Refine network to address the issue of lacking semantic information caused by local linear matching in correlation, from both spatial and channel perspectives. Correlation-Refine network (named CR) is solely built on top of fully convolutional layers, without employing intricate transformer mechanisms or complex methods to fuse features from multiple scales. Moreover, we present a concise yet effective convolutional tracking framework based on the correlation-refine network. CR network can increase the discriminative ability of semantic information in a coarse-to-fine manner: it gradually learns the semantic features of the target to be tracked and suppresses interference from similar objects by stacking multiple CR layers. Extensive experiments and comparisons with recent competitive trackers in challenging large-scale benchmarks demonstrate that, our tracker outperforms all previous convolutional trackers and has competitive results with transformer-based method. The code will be made available.

A cautionary tale on the effects of different covariance structures in linear mixed effects modeling of fMRI data.

With the steadily increasing abundance of longitudinal neuroimaging studies with large sample sizes and multiple repeated measures, questions arise regarding the appropriate modeling of variance and covariance. The current study examined the influence of standard classes of variance-covariance structures in linear mixed effects (LME) modeling of fMRI data from patients with pediatric mild traumatic brain injury (pmTBI; N = 181) and healthy controls (N = 162). During two visits, participants performed a cognitive control fMRI paradigm that compared congruent and incongruent stimuli. The hemodynamic response function was parsed into peak and late peak phases. Data were analyzed with a 4-way (GROUP×VISIT×CONGRUENCY×PHASE) LME using AFNI's 3dLME and compound symmetry (CS), autoregressive process of order 1 (AR1), and unstructured (UN) variance-covariance matrices. Voxel-wise results dramatically varied both within the cognitive control network (UN>CS for CONGRUENCY effect) and broader brain regions (CS>UN for GROUP:VISIT) depending on the variance-covariance matrix that was selected. Additional testing indicated that both model fit and estimated standard error were superior for the UN matrix, likely as a result of the modeling of individual terms. In summary, current findings suggest that the interpretation of results from complex designs is highly dependent on the selection of the variance-covariance structure using LME modeling.

308. Pandemic Effects: Stress and Food Insecurity in Relation to Maternal Cognitive Control Networks and Children’s Self-Regulation at 24 months

308. Pandemic Effects: Stress and Food Insecurity in Relation to Maternal Cognitive Control Networks and Children’s Self-Regulation at 24 months

Improved Spectrum Prediction Model for Cognitive Radio Networks Using Hybrid Deep Learning Technique

Improved Spectrum Prediction Model for Cognitive Radio Networks Using Hybrid Deep Learning Technique

Many-to-one matching based cooperative spectrum sharing in overlay cognitive radio networks

Cooperative Spectrum Sharing (CSS) is a Dynamic Spectrum Access mechanism aimed at providing incentives for both primary users (PUs) and secondary users (SUs) through cooperative communication. This paper addresses the pairing problem among multiple PUs and SUs to allocate optimal fractions of spectrum resources in terms of access time, using the CSS technique. Inspired by Matching Theory, we model the CSS between PUs and SUs as a Two-sided Matching Market and explore different market equilibria. Cooperative strategies among SUs are employed to maximize the gross utility of cooperative SUs and overall utility of the secondary network, in exchange for relay services rendered to PUs. We propose a polynomial-time based many-to-one matching scheme that favors groups of collaborative SUs to attract favorable PUs from the opposite side, leading to optimal matching for SUs. On the other hand, each PU achieves stable matching when paired with a group of cooperative SUs. We provide necessary proofs for the achieved matching equilibrium. Furthermore, we formulate an optimization framework for the optimal usage of PU access time among stable PU-SU pairs and propose a near-optimal solution, achieving 96% accuracy compared to the optimal allocation yield from the benchmark method (analytical). Our proposed many-to-one matching scheme outperforms similar existing schemes and the one-to-one matching scheme in terms of SU utility, satisfaction, participation, and throughput fairness index.

Brain Networks Modulation during Simple and Complex Gait: A "Mobile Brain/Body Imaging" Study.

Walking encompasses a complex interplay of neuromuscular coordination and cognitive processes. Disruptions in gait can impact personal independence and quality of life, especially among the elderly and neurodegenerative patients. While traditional biomechanical analyses and neuroimaging techniques have contributed to understanding gait control, they often lack the temporal resolution needed for rapid neural dynamics. This study employs a mobile brain/body imaging (MoBI) platform with high-density electroencephalography (hd-EEG) to explore event-related desynchronization and synchronization (ERD/ERS) during overground walking. Simultaneous to hdEEG, we recorded gait spatiotemporal parameters. Participants were asked to walk under usual walking and dual-task walking conditions. For data analysis, we extracted ERD/ERS in α, β, and γ bands from 17 selected regions of interest encompassing not only the sensorimotor cerebral network but also the cognitive and affective networks. A correlation analysis was performed between gait parameters and ERD/ERS intensities in different networks in the different phases of gait. Results showed that ERD/ERS modulations across gait phases in the α and β bands extended beyond the sensorimotor network, over the cognitive and limbic networks, and were more prominent in all networks during dual tasks with respect to usual walking. Correlation analyses showed that a stronger α ERS in the initial double-support phases correlates with shorter step length, emphasizing the role of attention in motor control. Additionally, β ERD/ERS in affective and cognitive networks during dual-task walking correlated with dual-task gait performance, suggesting compensatory mechanisms in complex tasks. This study advances our understanding of neural dynamics during overground walking, emphasizing the multidimensional nature of gait control involving cognitive and affective networks.

Resource Optimization in H-CRN with Supervised Learning Based Spectrum Prediction Technique

Cognitive radio network shows potential means of granting intensifying demand for wireless applications. In this model, an efficient resource optimization scheme with Priority Pricing Technique (PPT) is proposed with supervised learning-based SVM to tackle limited spectrum availability and underutilization in Hybrid-Cognitive Radio Networks (H-CRN). H-CRN works under the principle of detection of PUs states (active/inactive). If spectrum sensing is made in favor of active PUs, then the CSI (Channel State Information) is estimated and works in underlay principle. If it is made in favor of inactive PUs, then the transmission is performed in overlay manner. In the proposed PPT the PUs and SUs with highest channel gain have the highest priority to use the spectral resources. SVM is used as an effective technique of spectrum sensing to provide higher probability of detection of PUs as soon as possible. The proposed method faces following challenges such as in order to enhance the CRN transmission performance, the PUs have to withstand more interference power and transmit power control is needed in improving the sum rates when the interference is severe in H-CRN. With Simulation outcomes, the assessment of the proposed (PPT) model among (Fixed Pricing Technique and Without Pricing Technique) indicates the proposed method's improved efficiency. The results reveal significant effectiveness in obtaining better classification accuracy with less computation complexity, increased throughput, spectral efficiency and energy efficiency of the network.

Deep Reinforcement Learning based channel allocation (DRLCA) in Cognitive Radio Networks

The Spectrum is a scarce resource in wireless networks, hence these resources need to be perfectly channelized for their better utilization. By the study of the previous decades, it is found that the spectrum is colossally underutilized and the main reason for the underutilization found out to be the policies that are fixed and not dynamic. The dynamic spectrum allocation of frequency bands may overcome this problem. Cognitive radio provides an important concept that can used to solve the problem of underutilization of spectrum. Reinforcement learning is a key technique that is widely used to learn the spectrum allocation behaviour and maximize the system’s efficiency. Therefore, in this work, we have designed and developed a Reinforcement learning-based model to allocate the channels among secondary users. Also, a Deep Reinforcement learning-based channel allocation algorithm (DRLCA) has been proposed. The proposed DRLCA is compared with existing JPCRL [1]. In our algorithm, the Python libraries were used for simulation. From the simulation results and analysis, it is found that the DRLCA outperforms the JPCRL in terms of channel utilization by 5%.

CSL-SFNet for Cooperative Spectrum Sensing in Cognitive Satellite Network with GEO and LEO Satellites

In a cognitive satellite network (CSN) with GEO and LEO satellites, there is a large propagation losses between the sensing satellite and the ground station. The results of spectrum sensing from a single satellite may be inaccurate, which will create serious interference in the primary satellite system. Cooperative spectrum sensing (CSS) has become the key technology for solving the above problems in recent years. However, most of the current CSS techniques are model-driven. They are difficult to model and implement in CSNs since their detection performance is strongly dependent on an assumed statistical model. Thus, we propose a novel CSS scheme, which uses convolutional neural networks (CNNs), self-attention (SA) modules, long short-term memory networks (LSTMs), and soft fusion networks, called CSL-SFNet. This scheme combines the advantages of CNNs, SA modules, and LSTMs to extract the features of the input signals from the spatial and temporal domains. Additionally, the CSL-SFNet makes use of a novel soft fusion technique that improves detection performance while also considerably reducing communication overhead. The simulation results demonstrate that the proposed algorithm can achieve a detection probability of 90% when the signal-to-noise ratio is −20 dB; it has a shorter running time and always outperforms the other CSS algorithms.

Accelerated epigenetic age is associated with whole-brain functional connectivity and impaired cognitive performance in older adults

While chronological age is a strong predictor for health-related risk factors, it is an incomplete metric that fails to fully characterize the unique aging process of individuals with different genetic makeup, neurodevelopment, and environmental experiences. Recent advances in epigenomic array technologies have made it possible to generate DNA methylation-based biomarkers of biological aging, which may be useful in predicting a myriad of cognitive abilities and functional brain network organization across older individuals. It is currently unclear which cognitive domains are negatively correlated with epigenetic age above and beyond chronological age, and it is unknown if functional brain organization is an important mechanism for explaining these associations. In this study, individuals with accelerated epigenetic age (i.e. AgeAccelGrim) performed worse on tasks that spanned a wide variety of cognitive faculties including both fluid and crystallized intelligence (N = 103, average age = 68.98 years, 73 females, 30 males). Additionally, fMRI connectome-based predictive models suggested a mediating mechanism of functional connectivity on epigenetic age acceleration-cognition associations primarily in medial temporal lobe and limbic structures. This research highlights the important role of epigenetic aging processes on the development and maintenance of healthy cognitive capacities and function of the aging brain.

Backpropagation through time learning for recurrence-aware long-term cognitive networks

Fuzzy Cognitive Mapping (FCM) and the extensive family of models derived from it have firmly established their strong position in the landscape of machine learning algorithms. Specifically designed for pattern classification and multi-output regression, the recently introduced Recurrence-aware Long-term Cognitive Network (r-LTCN) model is one of these FCM-inspired extensions. On the one hand, this recurrent neural network connects all temporal states generated during the reasoning process with the decision-making layer. On the other hand, it uses a quasi-nonlinear reasoning rule devoted to avoiding convergence issues caused by unique fixed points, which typically emerge in other FCM models. In the original paper, the authors employed a combination of unsupervised and supervised learning to compute the r-LTCNs’ learnable parameters. Despite r-LTCNs’ astounding performance for a wide variety of pattern classification problems, the literature reports no attempt to train these recurrent neural systems in a fully supervised manner nor provide insights into their performance in other machine learning settings. This paper brings forward a modified Backpropagation Through Time learning (BPTT) algorithm devoted to training r-LTCN models used for multi-output regressions tasks rather than pattern classification. The proposed BPPT includes a simple yet effective mechanism to deal with the vanishing gradient within the recurrent layer that operates as a closed system while being tailored to the quasi-nonlinear reasoning mechanism. Empirical evaluation of the proposed BPTT algorithm using 20 multi-output regression problems reveals that it produces lower prediction errors compared with other state-of-the-art learning approaches.

Adaptive coalition with Kullback‐Leibler divergence for cooperative spectrum sensing (KLDCSS) in cognitive radio networks

SummaryEffective, reliable, and rapid spectrum sensing in concordance with maximum throughput efficiency is necessary at the cognitive radio network to maximize the network's utilization. An adaptive coalition using Kullback–Leibler divergence (KLD)‐based cooperative spectrum sensing scheme (KLDCSS) with a nominal sensing time to optimize the spectral efficiency is presented in this paper. In a coalition‐based spectrum sensing scheme, cooperating secondary users (SUs) are allocated to different coalitions to sense different Primary User (PU) channels. The same cognitive users allocated to sense the primary user channels are allotted to detect in different sensing phases. In the proposed sensing scheme, the cooperating SUs are adaptively allocated to different coalitions to maximize the throughput efficiency. Collaborating cognitive users with a standard signal‐to‐noise ratio and distinct energy levels that are characterized by the localized probabilities of detection and false alarm is considered. An adaptive coalition and user allocation algorithm are proposed to optimize the average opportunistic throughput and diminish the sensing overhead. The expression for throughput efficiency in terms of average throughput and sensing accuracy is derived. In this scheme, the SU computes the log‐likelihood ratio (LLR) from the acquired sample and forwards the same to the fusion center. The fusion center then accumulates the LLR values computed by the SU to estimate the likelihood of spectrum availability. Simulation results highlighting the competitive edge of the proposed scheme, with higher throughput efficiency over various numbers of SUs, coalitions, and signal‐to‐noise ratios, are presented.

Mobile edge computing based cognitive network security analysis using multi agent machine learning techniques in B5G

The proliferation of wireless applications at an exponential rate has made spectrum problems worse. Saturation in the unlicensed frequency spectrum is rapidly increasing as a result of the increasing data rates required by new wireless devices. A proposed solution to this problem is cognitive radio, which allows for the opportunistic use of licenced spectrum in less crowded areas. Cognitive network-based security evaluations using mobile edge computing and a Beyond 5G' (B5G) machine learning (ML) model are the focus of this research. In this case, the security study was carried out using cognitive network data transfer and multi-agent reinforcement encoder neural network and mobile edge computing (MRENN-MEC), a multi-agent reinforcement encoder neural network with mobile edge computing. Scalability, quality of service, throughput, and forecast accuracy are some of the network properties that undergo experimental analysis.

Interleaved TMS/fMRI shows that threat decreases dlPFC-mediated top-down regulation of emotion processing

The dorsolateral prefrontal cortex (dlPFC) is thought to be a key site in the brain’s cognitive control network, supporting cognitive processes like attention and working memory [1–7]. There is also evidence that the dlPFC is engaged during anxiety regulation tasks, suggesting that anxiety regulation may be mediated in part by dlPFC activity [8–15]. However, the degree to which these two domains of processing overlap is unclear. Therefore, in the current study, we tested the hypothesis that the dlPFC regulates brain regions critical for the expression of anxiety. To do so, we used interleaved TMS/fMRI to record TMS-evoked BOLD responses during periods of threat compared to periods of safety. We hypothesized that TMS pulses would reduce activity in anxiety expression regions during threat. Forty-four healthy controls (no current or history of psychiatric disorders) were recruited to take part in a broader study. Participants completed the neutral, predictable, and unpredictable (NPU) threat task while receiving TMS pulses to either the right dlPFC or a control region. A whole brain analysis identified regions showing significant BOLD responses evoked by dlPFC stimulation. We then extracted these responses and compared those evoked during safe blocks to those evoked during unpredictable threat. We found that responses in the left insula (LI), right sensory/motor cortex (RSM), and a region encompassing the bilateral SMA regions (BSMA) showed significantly different responses during the safe blocks compare to the threat. During the safe periods, these regions showed significant BOLD deactivations. These deactivations were reduced during the threat blocks. Overall, these findings are largely consistent with the hypothesis that the dlPFC plays a role in the top-down control of emotion and suggest that dlPFC activity reduces downstream activity in emotional expression regions, but that this effect is reduced under threat.

EFFICIENTLY UTILIZE THE SPECTRUM AND LOAD BALANCING IN COGNITIVE RADIO NETWORK USING GENETIC ALGORITHM WITH COVERSET PREDICTION

Wireless communication is one of the most in-demand fields of communication nowadays. Due to people's desire to transfer data as fast and broadly as possible, most communication processes are impacted by overloading and spectrum shortage when there is significant network usage. Make use of a cognitive radio network to keep an eye on things automatically, and use the spectrum dynamically to avoid overloading. Instead of evaluating the node's fitness, the CRN will alert the secondary user (SU) of the unused spectrum. To choose the fittest node in an environment with a coverage zone, a cognitive radio network in the proposed work uses a genetic algorithm plus coverset prediction. To address these issues, this paper offers a CRN calculation strategy.

Correction: Transient analysis of enhanced hybrid spectrum access for QoS provisioning in multi-class cognitive radio networks

Correction: Transient analysis of enhanced hybrid spectrum access for QoS provisioning in multi-class cognitive radio networks