Cognitive Users Research Articles

NEAT Based Approach for Cognitive Covert Communication Assisted by UAV-IRS

Abstract To address the security vulnerabilities inherent in cognitive radio technology amidst extensive control information and data exchanges, this paper introduces the use of unmanned aerial vehicle equipped with intelligent reflecting surfaces (UAV-IRS) as relay nodes to facilitate covert communications for cognitive users. Given the complexities and dynamics of the electromagnetic environment, which involve multiple interconnected and constantly changing communication resources, as well as high-dimensional data, we propose the NeuroEvolution of Augmenting Topologies based Resource Optimisation Algorithm Method (NEAT-ROAM). This advanced algorithm continuously optimizes and adapts the network structure in response to the external environment, ensuring robust and secure communication in dynamic and challenging scenarios. The algorithm not only provides superior covert communication performance but also exhibits fast convergence, which can be applied to the problem of assisting covert communication under mobile conditions using UAV-IRS.

Measuring User Response to Attention Guidance Using the Integrated Cognitive User Assistance System

Humans are limited in their attentional capacity and miss salient cues when multitasking. We built a platform, ICUa (Integrated Cognitive User assistance) that simulates parts of MATBII, a computer-based multitasking testbed, adding gaze-based attention guidance in an attempt to improve multitasking performance. ICUa uses software agents for monitoring the interface, human behavior and eye gaze in order to guide attention (displaying highlights and arrows). We varied the difficulty of our testbed and found effects on human performance (based on error times) and characterized the types of errors that occurred with higher difficulty and how agent response changed. There was no clear effect of ICUa guidance on gaze patterns or human performance. The visual guidance we deployed may not be sufficient to measurably influence behavior. Future work will develop the detection eye gaze patterns and effective cues to redirect attention using this human-agent interface for experiments on attention guidance.

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.

Burst traffic: Congestion management and performance optimization strategies in heterogeneous cognitive radio networks

AbstractIn order to solve the network congestion problem caused by burst service flows in heterogeneous cognitive radio networks (Het‐CRN), a two‐stage dual‐threshold random early detection (TD‐RED) control mechanism is proposed here. Meanwhile, a three‐state Markov modulated Poisson process (MMPP‐3) is used to model the burst service to analyze its arrival in depth. Moreover, a performance evaluation system (PES) is proposed to quantify the interference of burst services to different classes of services and their quality of service, which can comprehensively evaluate the performance enhancement of the TD‐RED control mechanism for each service. The system examines performance metrics such as packet loss rate, average delay, average captain etc. PES simulation results show that TD‐RED significantly reduces the packet rejection rate and transmission delay compared to the no congestion control (NCC) and random early detection (RED) methods, thus providing superior quality of service to all services. thereby providing a superior quality of service to cognitive users. Its lower average captain fully proves that TD‐RED can effectively alleviate congestion.

Analysis of Wideband spectrum sensing using Anderson darling test statistics in underlay cognitive microcell

The most widely used sensing technique for detecting energy is due to its ease of use and lack of need for signal information beforehand. Traditional methods often employ a two-threshold strategy in low signal-to-noise ratio scenarios, which can lead to temporary detection outcomes when the signal's energy is between the low and high threshold values. This subsequently results in inadequate detection accuracy, characterized by a reduced probability of detection, which ultimately results in an extended duration of the spectrum sensing. The paper suggests enhancing the efficiency of spectrum sensing through the utilization of a modified version of the Anderson-Darling test statistic, as opposed to other test statistics.To detect the presence of licensed users in each subchannel from the base station, the cognitive user implements the Anderson Darling test statistics as a sensing technique. The proposed work involves the mathematical derivation of the Anderson-Darling test statistics under a bandwidth of 6MHz.The simulation outcomes show that the sensing technique presented in this research study achieved a detection probability of 0.963 at a false alarm probability of 1%, using a small sample size of 20 at a signal-to-noise ratio of -20dB. This performance outperformed the other three detection algorithms.

Improved spectrum prediction model for cognitive radio networks using hybrid deep learning technique

Cognitive Radio (CR) technology has been highlighted as one of the most likely answers to the issue of spectrum shortage with the rise of fifth generation and beyond communication. Secondary users (SUs) in cognitive radio networks (CRN) must continuously monitor the spectrum to forecast channel occupancy by primary users (PUs) based on fundamental factors, such as location, time, and RF band. A hybrid deep learning model called LSTM-MLP (Long Short-Term Memory-Multilayer Perceptron) is proposed to improve idle channel prediction probability thus reducing the overall sensing time by cognitive users during spectrum sensing. Performance evaluation for the proposed model is done in terms of prediction error and efficiency, the GSM-900 spectrum dataset demonstrates that LSTM-MLP performs better in terms of improved prediction accuracy compared to existing state-of-art prediction techniques.

Smart sensing enabled dynamic spectrum management for cognitive radio networks

Cognitive Radio Networks (CRNs) have ushered in a transformative era in wireless communication, reshaping the landscape of radio spectrum utilization and management. At the core of CRNs lies the pivotal capability to sense the radio frequency spectrum dynamically and adapt transmission parameters to preemptively address interference and optimize spectrum utilization. This article addresses the escalating challenges associated with Quality of Service (QoS) management in CRNs, exacerbated by their dynamic nature, especially in scenarios characterized by high mobility. Concurrently, the article underscores the critical significance of energy efficiency, given its direct implications on network operational costs and sustainability. To effectively navigate the intricate interplay between QoS and energy management in CRNs, we propose a Smart Sensing Enabled Dynamic Spectrum Management scheme (SSDSM). Within the SSDSM framework, cognitive user energy undergoes intelligent sensing, while QoS is governed through dynamic spectrum management. The proposed scheme optimizes service response time by refining fuzzy-based controllers and curtails energy consumption through periodic sensing triggered by predefined rules. Operationalizing within a centralized paradigm, the entire network is overseen by a central controlling node, tasked with formulating an optimal channel list using the SSDSM scheme and allocating it to cognitive users. The efficacy of the proposed scheme is evaluated and validated through rigorous testing using MATLAB. Results reveal tangible enhancements in system efficiency, encompassing maximized throughput, reduced handoff ratio, and minimized service response delay. This research contributes to the ongoing discourse on advancing the performance metrics of cognitive radio networks in the pursuit of reliable and sustainable wireless communication services.

Joint detection algorithm for multiple cognitive users in spectrum sensing

Spectrum sensing technology is a crucial aspect of modern communication technology, serving as one of the essential techniques for efficiently utilizing scarce information resources in tight frequency bands. This paper first introduces three common logical circuit decision criteria in hard decisions and analyzes their decision rigor. Building upon hard decisions, the paper further introduces a method for multi-user spectrum sensing based on soft decisions. Then the paper simulates the false alarm probability and detection probability curves corresponding to the three criteria. The simulated results of multi-user collaborative sensing indicate that the simulation process significantly reduces false alarm probability and enhances detection probability. This approach effectively detects spectrum resources unoccupied during idle periods, leveraging the concept of time-division multiplexing and rationalizing the redistribution of information resources. The entire computation process relies on the calculation principles of power spectral density in communication theory, involving threshold decision detection for noise power and the sum of noise and signal power. It provides a secondary decision detection, reflecting the perceptual decision performance of logical detection methods with relative accuracy.

Distributed user pairing and effective computation offloading in aerial edge networks

Future Sixth-Generation (6G) mobile communication networks extremely require the global connectivity and the ground Base Stations (BSs) are difficult to develop in some specific areas, such as mountainous areas. Therefore, the aerial networks are the key framework for the future communications, in which the aerial vehicle could act as the access point. Additionally, Mobile Edge Computing (MEC) is also essential to enhance the data processing capabilities of aerial networks. In this paper, we study a comprehensive communication-computation resource management problem for jointly optimizing user pairing, power and time allocation in the MEC aided Cognitive Radio (CR) aerial networks, namely CR-MEC aerial networks. Explicitly, this multilevel optimization problem could be decomposed into the user pairing and power allocation as well as time allocation sub-problems. In the conceived CR-MEC aerial networks, we propose a User Pairing and Computation Offloading Management Algorithm (UPCOMA) based on three-sided matching theory, aiming to minimize the system’s energy consumption. At the first step of UPCOMA, multiple Primary Users (PUs) and Cognitive Users (CUs) on the ground negotiate to each other with the suitable power allocation coefficients and construct the stable user pairs. Moreover, the stable user pairs would match to a high altitude platform who act as the base station, which is for appropriately allocating Transmission Time Slots (TSs) at the second step of UPCOMA. Additionally, a hybrid offloading mode is proposed in our conceived networks in order to support ground users to offload their tasks adaptively according to their individual deadlines. Furthermore, the simulation results reveal that the relative performance of UPCOMA is close to that of the Brute-Force Search Algorithm (BFSA) with low complexity. Meanwhile, the hybrid offloading mode with less energy consumption supports much more ground user pairs to offload tasks compared to the binary mode.

NGMA‐based intergrated communication and computing for 6G‐enabled cognitive radio networks

AbstractAccording to the urgent low latency and the heavy computation tasks demands required for sixth‐generation (6G) wireless networks, the authors introduce the conventional resource allocation algorithms, including the game theory, artificial‐intelligence (AI) methods, and matching theory enabled framework, in which the multi‐access edge computing (MEC) scheme collaborative with the cloud platform to serve the primary users (PUs) and cognitive users (CUs) for next generation multiple access (NGMA). The proposed framework allows both the PUs and CUs to offload their computation tasks in a 6G‐enabled cognitive radio (CR) networks, so called cloud‐assisted CR‐MEC networks. In particular, the fundamentals of this conceived networks based on NGMA are first introduced. Hence, a number of methods based on the resource allocation algorithms are proposed in order to improve the quality of service for the mobile users, and reduce their transmission latency as well as the energy consumptions. Moreover, the motivations, challenges, and representative models for these conventional algorithms are described for integrated‐intelligent communication and computing aided NGMA networks. Furthermore, the open issues and future research directions for this conceived networks are summarised.

Joint Power Control and Resource Allocation with Rate Fairness Consideration for SWIPT-Based Cognitive Two-Way Relay Networks.

This paper investigates the power control and resource allocation problem in a simultaneously wireless information and power transfer (SWIPT)-based cognitive two-way relay network, in which two secondary users exchange information through a power splitting (PS) energy harvesting (EH) cognitive relay node underlay in a primary network. To enhance the secondary networks's transmission ability without detriment to the primary network, we formulate an optimization to maximize the minimum transmission rates of the cognitive users by jointly optimizing power allocation at the sources, the time allocation of transmission frames and power splitting at the relay, under the constraint that the transmission power of the cognitive network is set not to exceed the primary user interference threshold to ensure primary work performance. To efficiently solve this problem, a sub-optimal algorithm named the joint power control and resource allocation (JPCRA) scheme is proposed, in which we decouple the non-convex problem into convex problems and use alternative steps in the optimization algorithm to get final solutions. Numerical results reveal that the proposed scheme enhances transmission fairness and outperforms three traditional schemes.

Automatic Error Model (AEM) for User Interface Design: A new approach to Include Errors and Error Corrections in a Cognitive User Model

Replacing users with models that simulate users’ behaviors has been a long-standing vision in interface design. Cognitive models can simulate users’ cognitive processes and behaviors, but they cannot fully interact with user interfaces and simulate all behaviors. VisiTor (Vision+Motor) is a tool that provides cognitive models with methods to interact with the interface that users interact with. However, currently, cognitive models can only simulate experts and error-free users. Previous attempts at using cognitive models were inadequate due to hard-coded errors, which led to predictions that were not applicable to all users and tasks. Therefore, a general approach is required to include error generations and error corrections in cognitive models. VisiTor has been extended to generate and correct errors, leading to the development of an Automatic Error Cognitive Model that simulates more complete users’ behaviors in an Excel spreadsheet. This model helps to get closer to developing complete cognitive models that can replace users.

Secrecy Outage Performance Analysis for Uplink CR-NOMA Systems With Hybrid SIC

In the uplink cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) systems, the successive interference cancellation (SIC) and power control (PC) schemes were designed to enhance the outage performance but they lack the capability of protecting signals from being eavesdropped. This work investigates the secrecy performance of uplink CR-NOMA systems with the hybrid SIC and PC (HSIC-PC) scheme in the presence of a passive eavesdropper. Furthermore, through transmitting artificial noise (AN), a new scheme is proposed to improve the security of the cognitive user with HSIC-PC without deteriorating the primary user’s quality of service. Specifically, the primary and secondary users transmit AN cooperatively to enhance the security of the secondary user. The secrecy performance of the proposed scheme is analyzed based on the relationship between the maximum interference power that the primary user can tolerate and the secondary user’s channel gain. The analytical expressions for the secrecy outage probability (SOP) are derived, and the effect of parameters on the SOP is discussed. We also derive the analytical expressions of the asymptotic SOP in the high-power region to obtain further insights. Monte Carlo simulation results are performed to verify the analytical results. The results demonstrate that the proposed scheme performs better than the benchmarks.

Inclusive Design Requirement in Designing Accessibility for Low Cognitive Users

Inclusive Design Requirement in Designing Accessibility for Low Cognitive Users

Comparative analysis of blind detectors in a cluster-based cooperative Spectrum Hole detection

Prevention of authorized users from interference determine the accurate detection of Spectrum Hole (SH) is of great importance in a Spectrum Shearing Network (SSN). However, multipath fading and shadowing affect the accurate detection of SH resulting in interference. Cluster-Based Cooperative Spectrum Hole Detection (CBCSHD) used to address this problem depends on detector and number of clusters. Hence, comparative analysis of blind detectors in CBCSHD is carried out to evaluate its performance with various blind detectors and number of clusters. The CBCSHD is carried out using six Cognitive Users (CUs) that jointly carry out detection of SH and each of the CUs performs local sensing using Eigenvalue Detector (EVD), Energy Detector (ED) and Cyclostationary Detector (CD). The CUs form clusters to reduce reporting overhead between CUs. The local sensing results from individual user are combined at the Cluster Head (CH) using majority fusion rule. The performance of each of the detectors in CBCSHD is evaluated using Probability of Detection (PD) and Sensing Time (ST). PD values of 0.7661, 0.7160 and 0.6229 are obtained at SNR of 4 dB for ED, CD and EVD, respectively, while ST values of 3.0707, 3.7163 and 4.0907 s are obtained for ED, CD and EVD, respectively. The results obtained show that ED has the highest detection rate, followed by CD, while EVD shows the worst detection rate.

Primary user supported routing protocol for cognitive radio ad hoc networks in search of higher throughput

SummaryA routing protocol for cognitive radio ad hoc networks (CRAHNs), namely, primary user supported routing (PSR) is demonstrated in this research. Unlike existing routing protocols for CRAHN, where routing of cognitive users (CUs) is accomplished within CUs, in this proposed protocol, some of the primary users (PUs) support CUs to communicate, by forwarding CU packets. This service provided by PU is of voluntary nature. However, such assistance shall be provided by the PUs, only when they are idle, thereby, preserving the principles of cognitive radio networks (CRNs), which indicates that the operation of PU should not be disturbed by the CU activities. The proposed work is compared with cognitive ad hoc on‐demand distance vector (CAODV) protocol. The performance parameters considered are routing overhead, rate of loss of packets, and e2e packet delay. PSR outperforms CAODV in all these performance parameters. There has been on average 26.25% improvement in routing overhead, 34.12% decrease in loss, and 27.01% improvement in e2e delivery in the proposed PSR.

Performance Analysis of Wirelessly Powered Cognitive Radio Network with Statistical CSI and Random Mobility.

The relentless expansion of communications services and applications in 5G networks and their further projected growth bring the challenge of necessary spectrum scarcity, a challenge which might be overcome using the concept of cognitive radio. Furthermore, an extremely high number of low-power devices are introduced by the concept of the Internet of Things (IoT), which also requires efficient energy usage and practically applicable device powering. Motivated by these facts, in this paper, we analyze a wirelessly powered underlay cognitive system based on a realistic case in which statistical channel state information (CSI) is available. In the system considered, the primary and the cognitive networks share the same spectrum band under the constraint of an interference threshold and a maximal tolerable outage permitted by the primary user. To adopt the system model in realistic IoT application scenarios in which network nodes are mobile, we consider the randomly moving cognitive user receiver. For the analyzed system, we derive the closed-form expressions for the outage probability, the outage capacity, and the ergodic capacity. The obtained analytical results are corroborated by an independent simulation method.

Covariance absolute values spectrum sensing method based on two adaptive thresholds

Cognitive radio is a modern wireless communication methodology that deals with the issue of spectrum untapped. Cognitive users can continually perceive the licensed spectrum to hunt for unoccupied spots. The essential technology in cognitive radio (CR) for primary user detection is spectrum sensing. Due to signal to noise ratio (SNR), noise uncertainty in spectrum sensing would make the detection unreliable. In this paper, the two adaptive thresholds based on_covariance absolute values (TATCAV) are proposed to increase detection performance in the presence of noise uncertainty. According to the computer simulations using MATLAB 2021b, the value of the probability of detection is Pd=98.1% Compared with the results of two thresholds based on_covariance absolute values (TTCAV) Pd= 95.3% at SNR=-18, noise uncertainty Nu=1.761 dB, and using quadrature amplitude modulation (QAM). And the error rate for the proposed approach is Pe=12.1% under the same circumstances. The proposed approach results, according to the simulations, are considerably better than the results of the fixed two-threshold approach.

A Robust Adaptive Objective Power Allocation in Cognitive NOMA Networks.

Cognitive radio (CR) is a candidate for opportunistic spectrum implementation in wireless communications, allowing secondary users (SUs) to share the spectrum with primary users (PUs). In this paper, a robust adaptive target power allocation strategy for cognitive nonorthogonal multiple access (NOMA) networks is proposed, which involves the maximum transmission power of each SU and interference power threshold under PU constraints. By introducing the signal-to-interference-plus-noise ratio (SINR) adjustment factor, the strategy enables single-station communication to achieve energy efficiency (EE) or high throughput (HT), thus making the target function more flexible. In the same communication scenario, different cognitive users can choose different communication targets that meet their needs. Different QoS can be selected by the same cognitive user at different times. In the case of imperfect channel state information (CSI), semi-infinite (SI) constraints with bounded uncertainty sets are transformed into an optimization problem under the worst case, which is solved by the dual decomposition method. Simulation results show that this strategy has good adaptive selectivity and robustness.

Using Clinical Data Visualizations in Electronic Health Record User Interfaces to Enhance Medical Student Diagnostic Reasoning: Randomized Experiment.

In medicine, the clinical decision-making process can be described using the dual-process theory consisting of the fast, intuitive "System 1," commonly seen in seasoned physicians, and the slow, deliberative "System 2," associated with medical students. System-1-type diagnostic reasoning is thought to be less cognitively burdensome, thereby reducing physician error. To date, limited literature exists on inducing System-1-type diagnosis in medical students through cognitive heuristics, particularly while using modern electronic health record (EHR) interfaces. In this experimental pilot study, we aimed to (1) attempt to induce System-1-type diagnostic reasoning in inexperienced medical students through the acquisition of cognitive user interface heuristics and (2) understand the impact of clinical patient data visualizations on students' cognitive load and medical education. The participants were third- and fourth-year medical students recruited from the University of Pittsburgh School of Medicine who had completed 1+ clinical rotations. The students were presented 8 patient cases on a novel EHR, featuring a prominent data visualization designed to foster at-a-glance rapid case assessment, and asked to diagnose the patient. Half of the participants were shown 4 of the 8 cases repeatedly, up to 4 times with 30 seconds per case (Group A), and the other half of the participants were shown cases twice with 2 minutes per case (Group B). All participants were then asked to provide full diagnoses of all 8 cases. Finally, the participants were asked to evaluate and elaborate on their experience with the system; content analysis was subsequently performed on these user experience interviews. A total of 15 students participated. The participants in Group A scored slightly higher on average than those in Group B, with a mean percentage correct of 76% (95% CI 0.68-0.84) versus 69% (95% CI 0.58-0.80), and spent on average 50% less time per question than Group B diagnosing patients (13.98 seconds vs 19.13 seconds, P=.03, respectively). When comparing the novel EHR design to previously used EHRs, 73% (n=11) of participants rated the new version on par or higher (3+/5). Ease of use and intuitiveness of this new system rated similarly high (mean score 3.73/5 and 4.2/5, respectively). In qualitative thematic analysis of poststudy interviews, most participants (n=11, 73%) spoke to "pattern-recognition" cognitive heuristic strategies consistent with System 1 decision-making. These results support the possibility of inducing type-1 diagnostics in learners and the potential for data visualization and user design heuristics to reduce cognitive burden in clinical settings. Clinical data presentation in the diagnostic reasoning process is ripe for innovation, and further research is needed to explore the benefit of using such visualizations in medical education.