With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and Quality-of-Service (QoS) standards. Recent developments in federated learning (FL) have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this article, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unraveling the complexities of designing reliable and scalable FL models. This article outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state of the art and identifying open problems and future research directions.

Artificial Intelligence (AI) is reshaping healthcare through advancements in clinical decision support and diagnostic capabilities. While human expertise remains foundational to medical practice, AI-powered tools are increasingly matching or exceeding specialist-level performance across multiple domains, paving the way for a new era of democratized healthcare access. These systems promise to reduce disparities in care delivery across demographic, racial, and socioeconomic boundaries by providing high-quality diagnostic support at scale. As a result, advanced healthcare services can be affordable to all populations, irrespective of demographics, race, or socioeconomic background. The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care. In contrast to humans, AI can potentially uncover complex relationships in the data from a large set of inputs and generate new evidence-based knowledge in medicine. However, integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability. In this study, we examine recent advances in AI-enabled medical image analysis, current regulatory frameworks, and emerging best practices for clinical integration. We analyze both technical and ethical challenges inherent in deploying AI systems across healthcare institutions, with particular attention to data privacy, algorithmic fairness, and system transparency. Furthermore, we propose practical solutions to address key challenges, including data scarcity, racial bias in training datasets, limited model interpretability, and systematic algorithmic biases. Finally, we outline a conceptual algorithm for responsible AI implementations and identify promising future research and development directions.

Fifth-generation (5G) mobile communication systems are currently implemented worldwide. Moreover, the diversity between different deployments is growing, from operator-deployed macro networks to local private networks. The 5G system is very flexible with a large number of parameters and configurations impacting platform performance. With the availability of private spectrum and open-source software, there is an increasing possibility to get insight into what performance can be achieved in different dimensions, and how it depends on the configuration of various parameters, through experiments and testing. This paper presents a detailed account of a 5G Standalone (SA) platform based on the open5GS and srsRAN software suites installed on a general-purpose workstation. The performance of the platform is measured and compared to theoretical results and information from the 3GPP standardisation documentation. While some aspects of the performance are close to the theoretical expectations, others are further away. The software-defined radio (SDR) platform and the lack of hardware acceleration are probably the main limiting factors on coverage, uplink throughput and latency. The paper goes through several radio parameters and their impact on performance and discusses the performance implications and limitations of the experimental platform to share the lessons learned.

In this paper, we focus on communication from airplanes to air traffic control (ATC) towers at airports, employing multiple antennas at the ATC. The whole air-to-ground communication system can then be modeled as a MIMO MAC channel with mobile users. The main aim is to demonstrate that multiuser MIMO systems can be useful for achieving spectrally efficient aeronautical communications, to quantify the achievable capacity gains when using such systems, and to show that they can be implemented by deploying multiple antennas at reasonable inter-antenna distances at the ATC. In particular, we analyze the issue of antenna separation of a uniform linear array (ULA) at the ATC, for the case of line of sight (LoS) air-to-ground channels. It turns out that at high frequency (1 GHz), the use of large ULAs is indeed possible since the maximum (ergodic) capacity gain is achieved already for antenna separation on the order of a few centimeters. We also observe the system performance in Ricean fading channels. The performance is evaluated with respect to ergodic multiuser capacity throughout.

The electromagnetic spectrum’s scarcity, the tactical edge’s dynamic nature, and the variety of tactical operations impose challenges to military cognitive radio networks. Multi-radio dynamic spectrum management (DSM) and routing promise to increase the spectral efficiency and robustness of tactical ad hoc networks by adding key control plane tools that adapt the network to the varying harsh tactical environments. This paper describes a novel concept where implicit spectrum sensing, a distributed DSM architecture with ontology-based spectrum access policies, modified routing and network time synchronization capabilities interplay to address those challenges. The simulation results verify the designed functionality in harsh electromagnetic environments in a multitude of scenario sizes, in terms of number of radio terminals and number of networks. In particular, the simulation of a large operational scenario shows solid scaling capabilities and that ongoing jamming in several networks are efficiently mitigated and low-loss performance are re-established.

We propose and investigate a bidirectional device-to-device (D2D) transmission scheme that exploits cooperative downlink non-orthogonal multiple access (NOMA) (termed as BCD-NOMA). In BCD-NOMA, two source nodes communicate with their corresponding destination nodes via a relaying node while exchanging bidirectional D2D messages simultaneously. BCD-NOMA is designed for improved outage probability (OP) performance, high ergodic capacity (EC) and high energy efficiency by allowing two sources to share the same relaying node for data transmission to their corresponding destination nodes while also facilitating bidirectional D2D communications exploiting downlink NOMA. Simulation and analytical expressions of the OP, EC and ergodic sum capacity (ESC) under both perfect and imperfect successive interference cancellation (SIC) are used to demonstrate the effectiveness of BCD-NOMA compared to conventional schemes.