Improve Communication Performance Research Articles

Performance evaluation of distributed multi-agent IoT monitoring based on intelligent reflecting surface

The advent of intelligent reflecting surface (IRS) technology has revolutionized the landscape of wireless communication systems, offering promising opportunities for enhancing the performance of Internet of Things (IoT) applications. This paper presents a comprehensive performance evaluation of multi-agent IoT monitoring systems leveraging IRS technology. We focus on three criteria for selecting IRS units and assess the impact on system performance. Specifically, we analyze the system performance by deriving an outage probability expression for each criterion. Our study begins by introducing the concept of IRS and its role in IoT monitoring. We then present three IRS unit selection criteria: optimal selection (OS), partial selection (PS), and random selection (RS). For each criterion, we mathematically model and analyze the system outage probability, shedding light on the reliability and connectivity of IoT devices. The outage probability expressions derived in this work offer valuable insights into the trade-offs associated with IRS unit selection criteria in the context of IoT monitoring. Additionally, our findings contribute to the optimization of multi-agent IoT monitoring systems, enabling improved communication performance and enhanced reliability.

Decision-Making Algorithm with Geographic Mobility for Cognitive Radio.

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

Joint trajectory, transmission time and power optimization for multi-UAV data collecting system

Unmanned aerial vehicles (UAVs) have been generally applied in the field of communication due to their small size, flexible mobility, and convenient deployment. As a mobile base station, the UAV node can quickly establish a line-of-sight link with the ground node, thereby improving communication performance. In this paper, we study a multi-UAV assisted data collecting system. Specifically, in the case of limited system energy consumption, UAV flight energy consumption and ground node data transmission energy consumption are considered as an general limitation, and considering the channel interference between nodes, a multi-UAV assisted data collection model is studied. An non-convex problem that maximizes the minimum amount of data collected from ground nodes is further formulated. Since the original optimization problem is non-convex that difficult to solve directly, the problem is first decomposed into four sub-problems, and then the solution of each sub-problem is obtained by using successive convex approximation and block coordinate descent method. Finally, based on the solution of the four subproblems, an iterative algorithm for joint optimization of data transmission planning, transmission power, UAV trajectory and mission time is proposed. Simulation experiments show that the proposed algorithm can obtain more transmission data than the baseline algorithms.

Motor and neurocognitive profiles of children with symptomatic spinal muscular atrophy type 1 with two copies of SMN2 before and after treatment: a longitudinal observational study.

Spinal muscular atrophy (SMA) is a neurodegenerative disease caused by mutations in the survival motor neuron 1 (SMN1) gene. In clinical studies, gene replacement therapy with onasemnogene abeparvovec (formerly AVXS-101, Zolgensma®, Novartis) was efficacious in improving motor functioning in children with SMA. However, its effects on cognitive and language skills are largely unknown. This longitudinal observational study evaluated changes in motor and neurocognitive functioning over a 1-year period after administration of onasemnogene abeparvovec in 12 symptomatic SMA type 1 patients with two copies of SMN2 aged 1.7-52.6 months at administration. Motor functioning was measured using the Children's Hospital of Philadelphia Infant Test for Neuromuscular Disorders (CHOP-INTEND) while neurocognitive assessment was measured using Griffiths III. Motor milestones and language ability were also assessed at each timepoint. Statistically significant increases in median CHOP-INTEND scores from baseline were observed at 1, 3, 6, and 12 months after onasemnogene abeparvovec administration (all p ≤ 0.005). Most (91.7%) patients were able to roll over or sit independently for >1 min at 12 months. Significant increases in the Griffiths III Foundations of Learning, Language and Communication, Eye and Hand Coordination, and Personal-Social-Emotional subscale scores were observed at 12-months, but not in the Gross Motor subscale. Speech and language abilities progressed in most patients. Overall, most patients showed some improvement in cognitive and communication performance after treatment with onasemnogene abeparvovec in addition to significant improvement in motor functioning and motor milestones. Evaluation of neurocognitive function should be considered when assessing the global functioning of patients with SMA.

Theoretical Demonstration of Improved Performance in Multi-Channel Photonic RF Signals Based on Optical Injection Locking of Optical Comb and Array Lasers

Multi-channel radio frequency (RF) signal generation, facilitated by photonic technology, offers significant potential for generating coherent signals with a high frequency and low phase noise, providing multifunctional capabilities across diverse platforms, including RF and photonic systems. Traditional methods for multi-channel photonic RF signal generation typically entail the integration of diverse optical components, such as filters and amplifiers. However, this integration often results in compromises related to power efficiency, cost-effectiveness, and implementation simplicity. To address these challenges, we propose a novel method for generating multi-channel photonic RF signals based on optical injection locking technology. This approach eliminates the necessity for traditional optical components, leading to a substantial enhancement in the performance of photonic RF signals. We present the design of an optical injection locking-based multi-channel photonic RF signal generation schematic and theoretically evaluate its Signal-to-Noise Ratio (SNR) and eye pattern performance for data modulation using the Lumerical INTERCONNECT simulator. Our results reveal a significant 1.3-dB and 3.6-dB enhancement in SNR for 30-GHz and 60-GHz signals, respectively. Furthermore, we observed an improved communication performance, as evidenced by enhanced eye patterns in 3-Gbps data transmission compared to passive photonic RF signal generation methods.

Cluster-based RSU deployment strategy for vehicular ad hoc networks with integration of communication, sensing and computing

The integration of communications, sensing and computing (I-CSC) has significant applications in vehicular ad hoc networks (VANETs). A roadside unit (RSU) plays an important role in I-CSC by performing functions such as information transmission and edge computing in vehicular communication. Due to the constraints of limited resources, RSU cannot achieve full coverage and deploying RSUs at key cluster heads of hierarchical structures of road networks is an effective management method. However, direct extracting the hierarchical structures for the resource allocation in VANETs is an open issue. In this paper, we proposed a network-based renormalization method based on information flow and geographical location to hierarchically deploy the RSU on the road networks. The renormalization method is compared with two deployment schemes: genetic algorithm (GA) and memetic framework-based optimal RSU deployment (MFRD), to verify the improvement of communication performance. Our results show that the renormalization method is superior to other schemes in terms of RSU coverage and information reception rate.

An Information-Theoretic Approach to Joint Sensing and Communication

A communication setup is considered where a single transmitter wishes to convey messages to one or two receivers and simultaneously estimate the states of the receivers through the backscattered signals of the emitted waveform. The scenario at hand is motivated by joint radar and communication, which aims to co-design radar sensing and communication over shared spectrum and hardware. In this paper, we model the communication channel as a simple memoryless channel with independent and identically distributed (i.i.d.) time-varying state sequences and we model the backscattered signals by (strictly causal) generalized feedback. For single-receiver systems of this form, we fully characterize the capacity-distortion tradeoff, defined as the largest rate at which a message can reliably be conveyed to the receiver while simultaneously allowing the transmitter to sense the state sequence with a given allowed distortion. Our results show a tradeoff between the achievable rates and distortions, and that this tradeoff only stems from a common choice of the input distribution (the waveform) but not from other properties of the utilized codes. To better illustrate the capacity-distortion tradeoff, we propose a numerical method to compute the optimal inputs (waveforms) that achieve the desired tradeoff. For two-receiver systems with two states, we characterize the capacity-distortion tradeoff region of physically degraded broadcast channels (BC) as a rather straightforward extension of the single receiver case. Here, a tradeoff not only arises between sensing and communication performances but also between the various rates and the distortions of the different states. Similarly to the single-receiver case, the optimal co-design scheme exploits the generalized feedback signals only for sensing but not for improving communication performance. This is different for general two-receiver BCs, where optimal co-design schemes exploit generalized feedback also to improve capacity. However, as we show, also for BCs the optimal sensing performance only depends on the chosen input distribution (waveform) but not on the code construction used to accomplish the communication task. For general BCs, we provide inner and outer bounds on the capacity-distortion region, as well as a sufficient condition when this capacity-distortion region is equal to the product of the capacity region and the set of achievable distortions, in which case no tradeoff between sensing and communication occurs. A number of illustrative examples demonstrate that the optimal co-design schemes outperform conventional schemes that split the resources between sensing and communication, both for single-receiver and BC systems.

Communication Skills in Students: A Comparison of Reports Before and After Covid-19

Purpose: In order to contrast the trends in the literature regarding the subject of communication skills in higher-level students, main themes were identified that were emphasized before the pandemic and that, during and after it, ceased to be the subject of discussion. Methods: There was a qualitative approach. It used the narrative documentary review method and Boolean search engines in online indexed databases. From 421 identified articles, 20 were selected for analysis in the "before pandemic" condition and 24 for the "during and post-pandemic" ones, classifying them by type of Intervention and diagnosis and by research focus: qualitative and quantitative. Results and Conclusion: The pre-pandemic studies focused on elucidating the aspects associated with communication skills and academic performance, trying to design tools and implement strategies to improve communication performance in students. Finally, it is expected that educational institutions consider the relevance of implementing plans to reinforce and develop these soft skills in young people, maximizing technological resources as allies in improving their development in this globalized social context. Research implications: On the other hand, in the During and post-pandemic stages, they came together to explain the causes linked to the withdrawal of the expressive ability of the students, reconsidering the use of virtuality and highlighting the need to use traditional strategies as the best alternative to promote communicative skills. Originality/value: Study in the face of the overwhelming irruption of remote education as the only option for academic interaction.

Study of a dual RIS-assisted beam assignment algorithm in a high-speed moving scenario

In order to improve the frequent Doppler bias in the high-speed mobile scenario, improve the communication quality on HSR (High-Speed Railway). An algorithm can improve communication performance through RIS (intelligent metasurface) phase optimization. The optimization process is modeled as a reinforcement learning task, where the agent in the channel learns to select the optimal RIS phase by interacting with the environment to reduce the effect of Doppler shift.The model covers characteristic parameters such as signal propagation path and frequency related to RIS, and the agent influences the beam direction by adjusting the phase, and learns the optimal phase configuration in the high-speed moving scene.The RIS phase optimization method implemented based on the deep reinforcement learning algorithm has high convergence and has significant advantages in improving the Doppler frequency shift problem. Compared with the traditional frequency bias estimation method, this method can significantly improve the transmission performance. The effectiveness of the proposed algorithm is verified by performing large-scale training and evaluation in the simulation environment.

Simultaneous multitone microwave emission by dc-driven spintronic nano-element

Current-induced self-sustained magnetization oscillations in spin-torque nano-oscillators (STNOs) are promising candidates for ultra-agile microwave sources or detectors. While usually STNOs behave as a monochromatic source, we report here clear bimodal simultaneous emission of incommensurate microwave oscillations in the frequency range of 6 to 10 gigahertz at femtowatt level power. These two tones correspond to two parametrically coupled eigenmodes with tunable splitting. The emission range is crucially sensitive to the change in hybridization of the eigenmodes of free and fixed layers, for instance, through a slight tilt of the applied magnetic field from the normal of the nanopillar. Our experimental findings are supported both analytically and by micromagnetic simulations, which ascribe the process to four-magnon scattering between a pair of radially symmetric magnon modes and a pair of magnon modes with opposite azimuthal index. Our findings pave the way for enhanced cognitive telecommunications and neuromorphic systems that use frequency multiplexing to improve communication performance.

Minimal channel cost‐based energy‐efficient resource allocation algorithm for task offloading under FoG computing environment

SummaryThe Internet of Things (IoT) and fog computing (FC) can be integrated to manage massive data processing and complex networks. It can improve communication performance and provide robust computation for resource‐constrained IoT devices by deploying more fog nodes (FNs) at the edge/IoT networks. In this article, the optimal energy‐efficient fog resource allocation (oEeFRA) algorithm is proposed with the help of the minimal channel cost resource allocation (MiCCRA) method to ensure the load balancing and task offloading of the IoT‐FoG network under various constraints. Resource blocks (RBs) should be maintained higher than FNs in this proposed model to enhance the task‐offloading processes. The MiCCRA algorithm is proposed to provide a minimum of one FN and RB for each IoT device used in the IoT‐FoG network and also to ensure that weather each FN should be allotted with one or more RBs and IoT devices for efficient task offloading and load balancing activities. The energy efficiency (EE) of the proposed oEeFRA algorithm is computed through the MiCCRA algorithm by varying RB, FN, and IoT devices. The performance analysis shows that the proposed algorithm achieved the maximum EE of 6.12 × 109 bit/J, 3.019 × 1010 bit/J, and 5.69 × 1010 bit/J for varying RBs, FNs, and IoTs.

LDPC-coded cylindrical vector beam multiplexing for improved communication performance

Cylindrical vector beams (CVBs) have attracted significant research interest in mode-division multiplexing communication thanks to their capacity to carry orthogonal vector modes. Because CVB modes are the eigen-solutions of few-mode fiber (FMF), they are well-suited for long-distance transmission. Nevertheless, the application of CVB multiplexing communication is impeded by mode coupling in FMF, which leads to mode crosstalk and signal fading. Herein we propose an LDPC coded strategy to provide a channel equalization solution, which enhances the performance of CVB multiplexing communication. Digital signals are encoded using a sparse parity-check matrix that establishes a specific parity-check relationship between the original data bits and the check bits. This enables the correction of error bits for channel equalization. To demonstrate the concept, we built a 4-channel CVB multiplexing communication system that transmitted 24 × 4 Gbit/s QPSK-OFDM signals over 5 km FMF. After equalization, the bit error rate decreased by ∼ 2.5 orders of magnitude, and the communication sensitivity improved by ∼ 8 dB. This proof of concept demonstrates the potential of the proposed LDPC coded strategy to enhance the performance of CVB multiplexing communication systems.

Development and Analysis of Outage Probability and Achievable Rate for UAV Relay Implementation in Disaster Areas

Unmanned aerial vehicles (UAV) coupled with wireless networks have offered the potential for forthcoming mobile communications networks. The UAV-assisted wireless communication technology approach is an option for maintaining communication services, one of which is for disaster areas. This study aims to investigate the deployment of UAV-assisted communications in disaster areas. The simulation in this work is focused on investigating the relationship between the UAV’s height or radius and the outage probability and achievable rate. We identifies a UAV that behaves as a relay in a disaster area connected to two users, identified several critical parameters, such as UAV height, radius, and SNR value, and investigated the impact of changing each of them on outage probability and achievable rate values. Finally, we simulates the effect of these critical parameters on outage probability and achievable rate to maximize the use of UAV-assisted communication as a disaster management solution. According to the simulation results, as the signal to noise (SNR) increases, the probability of network communication interference decreases, and the achievable level increases. Moreover, increasing the range of UAVs can improve communication performance by reducing the probability of outages and increasing the achievable rate.

Channel reconfiguration for intelligent reflecting surface‐aided vehicular networks

AbstractIntelligent reflecting surface (IRS) has been recognized as one of the core technologies in the sixth generation (6G). Nevertheless, the character of channel state rapid change is a major problem in vehicular networks due to the dynamic vehicle movement and electromagnetic wave random scattering. In order to address the above problem, a strategy that joint phase shifts and channel alignment (PS‐CA) is proposed to reduce Doppler shift impact and improve communication performance in IRS‐aided vehicular networks. In this article, the channels can be divided into two types: the one type is the cascaded channel from the base station (BS) to the IRS and then to the vehicle; the other one is the direct channel from the BS to the vehicle. Thus, the proposed PS‐CA strategy can be divided into two stages. In the first stage, the cascaded channel fast fading state is reduced by designing IRS reflection phase shifts. In the second stage, considering the phase difference between the direct channel and the cascaded channel, we designed a correction function to align the direct channel with the cascaded channel. Then, we combined the designed correction function with IRS reflection phase shifts to reduce the impact of the direct channel fast fading state on overall performance. Simulation results demonstrate that compared to the RPS strategy, the PS‐CA strategy achieves higher spectral efficiency by 31.5.

Pattern-Division Multiplexing for Multi-User Continuous-Aperture MIMO

In recent years, thanks to the advances in meta-materials, the concept of continuous-aperture MIMO (CAP-MIMO) is reinvestigated to achieve improved communication performance with limited antenna apertures. Unlike the classical MIMO composed of discrete antennas, CAP-MIMO has a quasi-continuous antenna surface, which is expected to generate any current distribution (i.e., pattern) and induce controllable spatial electromagnetic (EM) waves. In this way, the information is directly modulated on the EM waves, which makes it promising to approach the ultimate capacity of finite apertures. The pattern design is the key factor to determine the communication performance of CAP-MIMO, but it has not been well studied in the literature. In this paper, we develop pattern-division multiplexing (PDM) to design the patterns for CAP-MIMO. Specifically, we first study and model a typical multi-user CAP-MIMO system, which allows us to formulate the sum-rate maximization problem. Then, we develop a general PDM technique to transform the design of the continuous pattern functions to the design of their projection lengths on finite orthogonal bases, which can overcome the challenge of functional programming. Utilizing PDM, we further propose a block coordinate descent (BCD) based pattern design scheme to solve the formulated sum-rate maximization problem. Simulation results show that, the sum-rate achieved by the proposed scheme is higher than that achieved by benchmark schemes, which demonstrates the effectiveness of the developed PDM for CAP-MIMO.

An Optimal Network-Aware Scheduling Technique for Distributed Deep Learning in Distributed HPC Platforms

Deep learning is a growing technique used to solve complex artificial intelligence (AI) problems. Large-scale deep learning has become a significant issue as a result of the expansion of datasets and the complexity of deep learning models. For training large-scale models, the cloud can be used as a distributed HPC (high-performance computing) tool with benefits in cost and flexibility. However, one of the major performance barriers in distributed deep learning in a distributed HPC environment is the network. The performance is often limited by heavy traffic like many stochastic gradient descent transfers for distributed communication. There are many network studies in distributed deep learning to solve these problems, but most research only focuses on improving communication performance and applying new methods or algorithms like overlapping parameter synchronization to minimize communication delay rather than considering the actual network. In this paper, we are focusing on the actual network, especially in a distributed HPC environment. In such an environment, if cluster nodes are assigned to different zones/regions which means a set of an appropriate number of distributed HPC nodes when performing distributed deep learning tasks, performance degradation due to network delay may occur. The proposed network optimization algorithm ensures that distributed work is placed in the same zone as much as possible to reduce network delay. Furthermore, scoring using network monitoring tools like loss, delay, and throughput is applied to select the optimal node within the zone. Our proposal has been validated on the Kubernetes platform, an open source orchestrator for the automatic management and deployment of micro-services. The performance of distributed deep learning is improved through the proposed scheduler.

Joint Active and Passive Beamforming Optimization for Multi-IRS-Assisted Wireless Communication Systems: A Covariance Matrix Adaptation Evolution Strategy

Intelligent reflecting surface (IRS) is applied in 5 G and 6 G to improve communication performance by reconfiguring signal propagation environments while maintaining low computational complexity and high energy efficiency. This paper proposes a novel methodology of joint active and passive beamforming optimization for wireless communications with the aid of multiple IRSs. In the considered system, the IRSs are deployed to assist the communication link from a multi-antenna access point (AP) to single-antenna users. We maximize the received signal to noise ratio (SNR) and sum rate of the multi-IRS-assisted communication system for single user and multiple users respectively, by solving a joint optimization problem of the active beamforming at the AP and the reflection beamforming at the IRSs. Specifically, we develop a mechanism of joint beamforming optimization for multi-IRS-assisted communications based on covariance matrix adaptation evolution strategy (CMA-ES), namely the BO-CMA algorithm, to jointly consider both the AP active beamforming and IRS reflection beamforming in the dynamic signal propagation environment. Furthermore, simulation results are provided to show that the BO-CMA algorithm is capable of significantly outperforming the benchmark schemes. Consequently, the solution based on the BO-CMA algorithm can dynamically rebuild the signal propagation environment, and improve the received SNR and the sum rate respectively for multi-IRS-assisted one user and multiple users communications.

A Robust Scheme for RIS-Assisted UAV Secure Communication in IoT

Reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicles (UAV) have been extensively studied on the Internet of Things (IoT) systems to improve communication performance. In this paper, we aimed to counter simultaneous jamming and eavesdropping attacks by jointly designing an active beamforming vector at the base station (BS) and reflect phase shifts at the RIS. Specifically, considering imperfect angular channel state information (CSI), the sum secrecy rate maximization problem in the worst case could be formulated, which is NP-hard and non-convex. To address this problem, we improved the robust enhanced signal-to-leakage-and-noise ratio (E-SLNR) beamforming to reduce the computational complexity and mitigate the impact of interference, eavesdropping and jamming. Furthermore, a genetic algorithm with a tabu search (GA-TS) method was proposed to efficiently obtain an approximate optimal solution. The simulation results demonstrated that the proposed GA-TS method converged faster with better results than conventional GA, while the proposed robust scheme could achieve higher sum secrecy rates than the zero-forcing (ZF) and SLNR schemes.

Reconfigurable intelligent surface-enabled integrated sensing and communication: a sensing-assisted communication framework

Integrated sensing and communication (ISAC) is an essential technology in the upcoming 6G network, and its performance can be effectively enhanced by the reconfigurable intelligent surface (RIS). Among the various RIS-enabled ISAC techniques, RIS-enabled sensing-assisted communication has attracted growing attention because it can effectively improve communication performance by focusing the energy on the sensed locations of the users and is easy to be integrated into existing communication systems. However, existing RIS-enabled sensing-assisted communication systems rely on antenna arrays for the acquisition of angular information to localize the users, making the system more complex and expensive. To handle this problem, in this paper, we propose an RIS-enabled multi-user sensing-assisted communication framework where a single antenna access point (AP) first senses the locations of the single-antenna users with the help of the RIS, and then the energy of the communication signal is focused on the sensed locations of the users to provide them with a higher sum-rate by adjusting the phase shifts of the RIS. However, the selection of the RIS phase shifts heavily influences the resource block and power allocation at the AP, which makes the joint design of the RIS phase shifts and the resource allocation very challenging. In order to address this challenge, we formulate an RIS-enabled multi-user communication optimization problem and design a two-stage optimization algorithm based on the genetic and Lagrangian duality methods to jointly optimize the RIS phase shifts and the resource allocation. Simulation and experimental results show that compared with the scheme without RIS, the proposed RIS-enabled multi-user communication system can achieve a higher sum-rate and lower localization error.

From threat to challenge—Improving medical students’ stress response and communication skills performance through the combination of stress arousal reappraisal and preparatory worked example-based learning when breaking bad news to simulated patients: study protocol for a randomized controlled trial

BackgroundBreaking bad news (BBN; e.g., delivering a cancer diagnosis) is perceived as one of the most demanding communication tasks in the medical field and associated with high levels of stress. Physicians’ increased stress in BBN encounters can negatively impact their communication performance, and in the long term, patient-related health outcomes. Although a growing body of literature acknowledges the stressful nature of BBN, little has been done to address this issue. Therefore, there is a need for appropriate tools to help physicians cope with their stress response, so that they can perform BBN at their best. In the present study, we implement the biopsychosocial model of challenge and threat as theoretical framework. According to this model, the balance between perceived situational demands and perceived coping resources determines whether a stressful performance situation, such as BBN, is experienced as challenge (resources > demands) or threat (resources < demands). Using two interventions, we aim to support medical students in shifting towards challenge-oriented stress responses and improved communication performance: (1) stress arousal reappraisal (SAR), which guides individuals to reinterpret their stress arousal as an adaptive and beneficial response for task performance; (2) worked examples (WE), which demonstrate how to BBN in a step-by-step manner, offering structure and promoting skill acquisition.MethodsIn a randomized controlled trial with a 2 (SAR vs. control) x 2 (WE vs. control) between-subjects design, we will determine the effects of both interventions on stress response and BBN skills performance in N = 200 third-year medical students during a simulated BBN encounter. To identify students’ stress responses, we will assess their perceived coping resources and task demands, record their cardiovascular activity, and measure salivary parameters before, during, and after BBN encounters. Three trained raters will independently score students’ BBN skills performances.DiscussionFindings will provide unique insights into the psychophysiology of medical students who are tasked with BBN. Parameters can be understood more comprehensively from the challenge and threat perspective and linked to performance outcomes. If proven effective, the evaluated interventions could be incorporated into the curriculum of medical students and facilitate BBN skills acquisition.Trial registrationClinicalTrials.gov (NCT05037318), September 8, 2021.