Fifth generation (5G) mobile communication technology can enable novel healthcare applications and augment existing ones. However, 5G-enabled healthcare applications demand diverse technical requirements for radio communication. Knowledge of these requirements is important for developers, network providers, and regulatory authorities in the healthcare sector to facilitate safe and effective healthcare. In this paper, we review, identify, describe, and compare the requirements for communication key performance indicators in relevant healthcare use cases, including remote robotic-assisted surgery, connected ambulance, wearable and implantable devices, and service robotics for assisted living, with a focus on quantitative requirements. We also compare 5G-healthcare requirements with the current state of 5G capabilities. Finally, we identify gaps in the existing literature and highlight considerations for this space.

Cough-based diagnosis for respiratory diseases (RDs) using artificial intelligence (AI) has attracted considerable attention, yet many existing studies overlook confounding variables in their predictive models. These variables can distort the relationship between cough recordings (input data) and RD status (output variable), leading to biased associations and unrealistic model performance. To address this gap, we propose the Bias-Free Network (RBF-Net), an end-to-end solution that effectively mitigates the impact of confounders in the training data distribution. RBF-Net ensures accurate and unbiased RD diagnosis features, emphasizing its relevance by incorporating a COVID-19 dataset in this study. This approach aims to enhance the reliability of AI-based RD diagnosis models by navigating the challenges posed by confounding variables. A hybrid of a Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed for the feature encoder module of RBF-Net. An additional bias predictor is incorporated in the classification scheme to formulate a conditional Generative Adversarial Network (c-GAN) that helps in decorrelating the impact of confounding variables from RD prediction. The merit of RBF-Net is demonstrated by comparing classification performance with a State-of-The-Art (SoTA) Deep Learning (DL) model (CNN-LSTM) after training on different unbalanced COVID-19 data sets, created by using a large-scale proprietary cough data set. RBF-Net proved its robustness against extremely biased training scenarios by achieving test set accuracies of 84.1%, 84.6%, and 80.5% for the following confounding variables—gender, age, and smoking status, respectively. RBF-Net outperforms the CNN-LSTM model test set accuracies by 5.5%, 7.7%, and 8.2%, respectively.

According to the World Health Organization, cardiovascular disease adversely impacts the quality of life, can lead to death and imposes a substantial burden on global healthcare systems. Addressing modifiable risk factors for cardiovascular disease through healthy lifestyle changes and early screening via continuous monitoring can help reduce both the disease burden and associated treatment costs. Smartphones and smartwatches have gained popularity due to their non-intrusive nature, making them suitable for daily health tracking. Modern smartwatches integrate sensors like accelerometer, electrocardiogram and photoplethysmogram, enabling continuous monitoring of lifestyle habits and cardiovascular health indicators. This survey reviews studies from January 2019 to December 2024 that utilized wrist-worn sensors for assessing cardiovascular health aspects, including hypertension, abnormal cardiac rhythms, sleep tracking, activity monitoring and post-stroke rehabilitation. Each study was evaluated based on the United Kingdom Research and Innovation’s Technology Readiness Level (TRL) 9-point scale to determine its applicability for real-world clinical use. Studies were categorized by TRL level, disease focus and sensing modalities used in clinical settings. This structured analysis helps identify mature technologies that are closer to clinical deployment, while highlighting gaps in other technologies that require further research. Additionally, the survey provides insights for overcoming limitations and advancing wearable health technologies toward practical medical applications. By analysing the progress in the capabilities of wrist-worn sensors for cardiovascular health monitoring, this survey aims to bridge the gap between research and real-world application, thereby supporting the development of effective and clinically viable solutions.

Prior to standardization, new features, algorithms, and solutions have to be rigorously evaluated and verified using different methods. In this regard, testbeds are considered as one of the most important and effective experimental platforms for performing tests, thus paving the way towards the real-world implementation of many solutions, from the most basic to the most disruptive and innovative ones. Compared to computer simulators, testbeds provide near-realistic implementation and can obtain practical results while posing less risk to live networks. They are also cheaper compared to field trials. Given the crucial role of testbeds in the cellular network ecosystem, it is imperative to create awareness regarding the design challenges associated with constructing an effective and efficient testbed deployment. In this tutorial, we discuss the design considerations and challenges we experienced while deploying an outdoor testbed that we call TurboRAN—a 5G and beyond network testbed facility. It is designed to facilitate the evaluation of early real-world use case algorithm implementations and deployments. We present the process and methodology used to select components, including antennas, amplifiers, base stations, and cabinets, to name a few, and describe the integration of the components to construct a fully operational testbed. Finally, we discuss a use case that demonstrates the TurboRAN’s ability to conduct real-world experiments. The use case demonstrates the detrimental impact of sub-optimal handover parameter configuration on network performance—a big challenge in modern cellular communication. This work intends to inspire and guide the development of a cellular testbed by providing a generalized framework that describes step-by-step the efforts made for its construction.

Deep automation provided by self-organizing network (SON) features and their emerging variants such as zero touch automation solutions is a key enabler for increasingly dense wireless networks and pervasive Internet of Things (IoT). To realize their objectives, most automation functionalities rely on the Minimization of Drive Test (MDT) reports. The MDT reports are used to generate inferences about network state and performance, thus dynamically change network parameters accordingly. However, the collection of MDT reports from commodity user devices, particularly low cost IoT devices, make them a vulnerable entry point to launch an adversarial attack on emerging deeply automated wireless networks. This adds a new dimension to the security threats in the IoT and cellular networks. Existing literature on IoT, SON, or zero touch automation does not address this important problem. In this paper, we investigate an impactful, first of its kind adversarial attack that can be launched by exploiting the malicious MDT reports from the compromised user equipment (UE). We highlight the detrimental repercussions of this attack on the performance of common network automation functions. We also propose a novel Malicious MDT Reports Identification framework (MRIF) as a countermeasure to detect and eliminate the malicious MDT reports using Machine Learning and verify it through a use-case. Thus, the defense mechanism can provide the resilience and robustness for zero touch automation SON engines against the adversarial MDT attacks.