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With recent developments, the performance of automotive radar has improved significantly. The next generation of 4D radar can achieve imaging capability in the form of high-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.

Carrier aggregation (CA) was introduced in mobile communication systems in response to the demand for higher network capacity. CA was conceived as a technique to achieve higher data rates by aggregating multiple blocks of spectrum from the same or different frequency bands. This work explores a different point of view, where CA is employed not as a way to increase capacity through using more bandwidth, but as a diversity technique in order to increase the spectral efficiency of the existing spectrum, and therefore, achieve higher capacity without needing additional spectrum. A mathematical model and set of closed-form expressions are provided, which can be used to characterise the performance of CA as a diversity technique (in terms of both ergodic capacity and secrecy capacity) and determine the impact of various relevant configuration parameters. The numerical results obtained by evaluating the mathematical expressions derived in this work are in line with our previous simulation studies and demonstrate that CA can be effectively exploited as a diversity technique to improve the capacity and performance of mobile communication systems compared to the case of single-carrier transmission over the same amount of bandwidth.

In the realm of programming education, enhancing student commitment is pivotal for both academic success and practical application. Traditional methodologies often fall short in catering to the evolving needs of today’s learners. Addressing this gap, this paper introduces a web-based gamified tool designed to transform the pedagogical approach in programming courses. The study’s theoretical contribution lies in its exploration of gamification as a strategy to augment student commitment, bridging the divide between conventional teaching and contemporary learning needs. From a practical standpoint, empirical data encompassing performance metrics, dropout rates, and student feedback demonstrate the tool’s significant positive impact. Notably, students exhibited improved pass rates and reduced dropout tendencies, underscoring the tool’s practical efficacy. Furthermore, feedback highlighted the tool’s usability and the enhanced satisfaction derived from its engagement. Aligned with the existing literature, the findings advocate for the broader adoption of gamification in programming education. This research substantiates the potential of gamification as both a theoretical model and a practical strategy, enriching the discourse on modern programming education.

The field of image analysis with artificial intelligence has grown exponentially thanks to the development of neural networks. One of its most promising areas is medical diagnosis through lung X-rays, which are crucial for diseases like pneumonia, which can be mistaken for other conditions. Despite medical expertise, precise diagnosis is challenging, and this is where well-trained algorithms can assist. However, working with medical images presents challenges, especially when datasets are limited and unbalanced. Strategies to balance these classes have been explored, but understanding their local impact and how they affect model evaluation is still lacking. This work aims to analyze how a class imbalance in a dataset can significantly influence the informativeness of metrics used to evaluate predictions. It demonstrates that class separation in a dataset impacts trained models and is a strategy deserving more attention in future research. To achieve these goals, classification models using artificial and deep neural networks implemented in the R environment are developed. These models are trained using a set of publicly available images related to lung pathologies. All results are validated using metrics obtained from the confusion matrix to verify the impact of data imbalance on the performance of medical diagnostic models. The results raise questions about the procedures used to group classes in many studies, aiming to achieve class balance in imbalanced data and open new avenues for future research to investigate the impact of class separation in datasets with clinical pathologies.

Cooperative navigation is a fundamental enabling technology for unlocking the full potential of Unmanned Underwater Vehicle (UUV) clusters in GNSS-denied environments. However, the severe constraints of the underwater acoustic channel, such as high latency, low bandwidth, and non-Gaussian noise, pose significant challenges to designing robust and efficient state estimation and information fusion algorithms. While numerous surveys have cataloged the available techniques, they have remained largely descriptive, lacking a rigorous, quantitative comparison of their performance trade-offs under realistic conditions. This paper provides a comprehensive and critical review that moves beyond qualitative descriptions to establish a novel quantitative comparison framework. Through a standardized benchmark scenario, we provide the first data-driven, comparative analysis of key frontier algorithms—from recursive filters like the Maximum Correntropy Kalman Filter (MCC-KF) to batch optimization methods like Factor Graph Optimization (FGO)—evaluating them across critical metrics including accuracy, computational complexity, communication load, and robustness. Our results empirically reveal the fundamental performance gaps and trade-offs, offering actionable insights for system design. Furthermore, this paper provides in-depth technical analyses of advanced topics, including distributed fusion architectures, intelligent strategies like Deep Reinforcement Learning (DRL), and the unique challenges of navigating in extreme environments such as the polar regions. Finally, leveraging the insights derived from our quantitative analysis, we propose a structured, data-driven research roadmap to systematically guide future investigations in this critical domain.

Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems access the channel in an opportunistic, non-interfering manner with the primary network. DSA/CR systems utilize spectrum sensing techniques to sense the availability of Primary user (PU). CR users can benefit from the knowledge of PU activity statistics.In this work, comprehensive analysis of estimation of distribution of PU idle and busy periods is carried out using Generalized Pareto and Pareto distributions for long and short time scale models respectively and closed form expression is derived. Moreover, the impact of sensing periods on the accuracy of estimated PU idle/busy periods is studied. Furthermore, the error in proposed estimation of distribution of PU idle and busy periods is quantified using the Kolmogorov–Smirnov test. From this study we conclude that the proposed model is better fit for the real scenarios eliminating practical limitations. Mathematical analysis is substantiated with the simulation results.

The concept of signal area (SA), defined as the rectangular time–frequency region in a spectrogram where a signal is detected, plays an important role in spectrum usage measurements. The need for signal area estimation (SAE) is justified by its role in the process of allocating white space spectrum to secondary users in dynamic spectrum access systems as well as in other interesting applications such as compliance verification and enforcement of spectrum regulations, signal interception, and network planning and optimisation. Existing SAE methods are far from perfect and therefore new solutions capable to provide more accurate estimations are thus required. In this study, a novel approach based on image processing techniques is explored. Concretely, the feasibility of using morphological operations (MOs) is explored to examine its usefulness in the context of SAE. By means of extensive simulations, the performance of different MOs (erosion, dilation, opening, and closing) in the context of SAE is investigated under various configurations, including different shapes and sizes of the structuring element (SE), when used both as standalone SAE methods and in combination with other SAE methods from the literature. Based on the obtained results, an MO-based SAE method is formulated based on the optimum MO and SE configuration for each specific SNR regime, which can improve substantially the performance of other proposed SAE methods when used as a pre- or postprocessing technique. Concretely, the accuracy improvement in terms of F1 score is up to 40% in the low-SNR regime while achieving a perfect accuracy of 100% in the high-SNR regime. This is achieved without having a noticeable impact on the associated computational cost (and even reducing it by up to 15% at high SNR). The performance improvement is thus particularly significant in the low-SNR regime, where most methods’ performances are limited, and as a result the proposed SAE approach can extend the operational SNR range of the existing SAE methods.

Dynamic Spectrum Access/Cognitive Radio systems access the channel in an opportunistic, non-interfering manner with the primary network. These systems utilize spectrum sensing techniques to sense the occupancy of the primary user. In this paper, an artificial neural network based hybrid spectrum sensing technique is proposed, which considers sensing as a binary classification problem to detect whether the primary user is idle or busy. The proposed scheme utilizes energy detection and likelihood ratio test statistic as features to train the neural network. Moreover, we demonstrate the impact of hyperparameter tuning and carry out the detailed study of it, yielding a combination of best-suited hyperparameters. The performance of the proposed sensing scheme is validated on primary signals of various real world radio technologies acquired with an empirical testbed setup. We conclude that the best performing configuration results in an increase of approximately 63% in detection performance compared to classical energy detection and improved energy detection sensing schemes when averaged over all the radio technologies considered in this work.

Link Adaptation is a radio resource management technique that assesses the channel conditions and selects a transport mode, from a set of possible options, which is optimised for these conditions according to a predefined criterion. The optimum transport mode is commonly determined so as to maximise the throughput. Although this approach may be appropriate for best-effort services, its suitability for multimedia services, usually characterised by tight delay and error performance constraints, has been questioned. As a result, a number of alternative algorithms have been proposed in the literature. In this context, this paper presents and evaluates in a dynamic radio environment several Link Adaptation algorithms designed to enhance the provision of delay- and error-sensitive multimedia packet-data services over wireless systems. The obtained results demonstrate that significant improvements in terms of throughput, transmission delay, error performance and operation of Link Adaptation itself can be obtained with the proposed schemes.

IntroductionCognitive radio (CR) has recently become one of the most intensively studied paradigms in wireless communications. In its broadest sense, a CR can be thought of as an enhanced smart software defined radio (SDR). The terms SDR and CRwere introduced by J. Mitola in 1992 [1] and 1999 [2], respectively. SDR, sometimes shortened to software radio, is generally a multi-band radio that supports multiple air interfaces and protocols, and is reconfigurable through software running on a digital signal processor (DSP), field-programmable gate array (FPGA), or general-purpose microprocessor [3]. CR, usually built upon an SDR platform, is a context-aware intelligent radio capable of autonomous reconfiguration by learning from and adapting to the surrounding communication environment [4]. CRs are capable of perceiving and sensing their radio frequency (RF) environment, learning about their radio resources, user equipment (UE), and application requirements, and adapting their configuration and behavior accordingly. From this definition, two main characteristics of CR can be identified: cognitive capability (ability to capture information and learn from the radio environment) and reconfigurability (which enables the transmitter parameters to be dynamically programmed and modified according to the radio environment).An important specific application often associated with CR is dynamic spectrum access (DSA). DSA, despite being a broader concept [5–7], is commonly understood as the reutilization of licensed RF bands by unlicensed UEs provided that the legitimate licensed UEs are not using the reused frequencies at a given time or in a given region of space.