Engineering

Abstract: Pneumonia detection in chest X-rays is commonly treated as a binary classification task, although the radiographic burden of disease varies substantially across patients. This study investigates whether the expert bounding-box annotations available in the RSNA 2018 Pneumonia Detection dataset can be reused to derive an interpretable severity grading framework based on lesion extent. For each pneumonia-positive image, total disease burden was computed as the cumulative area of all annotated opacity boxes, and a balanced three-tier split was generated, defining Severity 1 (low/mild), Severity 2 (moderate), and Severity 3 (severe). The resulting severity groups contained 1999, 2006, and 2007 images, respectively. To validate the usefulness of this annotation-driven severity formulation, we trained Vision Transformer models in both a classical binary setting and a severity-specialized setup. The classical binary baseline reached a maximum validation accuracy of 95.05%, while the specialized models achieved 94.98% for Severity 1, 97.88% for Severity 2, and 99.08% for Severity 3. These results indicate that radiographic burden derived from bounding-box extent supports highly separable severity-aware subproblems, particularly for severe pneumonia, while lower-burden categories remain more difficult. The proposed framework offers a transparent and reproducible way to transform RSNA 2018 lesion annotations into ordinal severity labels. Beyond its original use as a detection and localization benchmark, the dataset can therefore also support severity-aware classification, radiographic burden analysis, and future explainable AI studies in chest X-ray interpretation.

Abstract: Next-generation wireless systems are becoming more complex, and the need for intelligent mobility management mechanisms that can ensure service continuity and efficiently utilize network resources has been growing. Frequent handovers, unequal distribution of traffic, variable network conditions, and multiple radio access technologies are some of the challenges that traditional mobility control strategies are likely to face in 5G and future 6G networks with dense deployments of small cells, heterogeneous architectures, and highly mobile users. These constraints frequently lead to sub-optimal user experience, higher overhead in signalling and inefficient use of resources. The introduction of new tools through the advancements of artificial intelligence (AI), specifically machine learning and deep learning techniques have created new opportunities for "predictive" and "adaptive" mobility optimization. The use of data-driven decision-making can enable AI-based solutions to predict user movements, fine-tune the execution of handover and dynamically allocate radio resources to enhance network performance. This survey This paper presents a comprehensive survey of AI-enabled handover strategies for mobility load management 5G, Beyond-5G (B5G), and upcoming 6G networks. This study provides a review of current studies, classifies the framework approaches of mobility management based on AI technologies, and identifies their architecture, learning and optimization goals. Moreover, the survey assesses the performance of intelligent handover schemes to solve the critical issues like load balancing, interference mitigation, connection reliability and quality of service maintenance. Key performance indicators related to mobility robustness, resource efficiency and service continuity are compared between the conventional mobility management methods and the AI based ones. Last but not least, the paper outlines unsolved problems, new trends and potential areas of research that will guide the evolution of autonomous mobility management solutions for the future of wireless communication networks.

Abstract: Large-scale network operations require engineers to work across heterogeneous tools and dashboards, which can lengthen Mean Time to Repair (MTTR) and affect availability when it delays incident resolution. We present an agentic ChatOps system in which a supervisor orchestrates large language model (LLM) agents that route natural-language intents to specialized workers issuing planned tool calls across operations systems, with retrieval-augmented grounding in a network source of truth. We deploy the approach in a major global network—a deployment that has since grown to fourteen specialized workers plus dedicated expert and autonomous agents—and detail seven representative use cases, including closed-loop link-flapping remediation that validates candidate changes in a digital twin before committing version-controlled configuration. Across six recurring tasks, the integrated ChatOps automation system was associated with per-event handling times lower by roughly 10× to 2400×, and the link-flap cycle was shortened from about 30 to 3 minutes. Over a representative 90-day window it handled roughly 7,400 production interactions, with positive feedback on most rated responses; an offline benchmark of 200 questions scored by an LLM-as-a-judge yielded mean relevance and context relevance of 0.85 and 0.79. The results support modeled availability improvement when saved handling time reduces incident MTTR, in a deployment designed for confidentiality and guarded by validation pre-checks.

Abstract: This paper presents a robust and efficient Convolutional Neural Network (CNN)-based channel estimator for fifth-generation (5G) Orthogonal Frequency Division Multiplexing (OFDM) systems. While conventional Least Squares (LS) and Minimum Mean Square Error (MMSE) estimators degrade significantly in high-mobility, non-linear, and frequency-selective fading environments, the proposed framework treats the time-frequency resource grid as a spatial image, enabling implicit learning of complex fading dynamics without explicit statistical modeling. The model is trained on 105 synthetic channel realizations spanning Rayleigh, Rician (K = 5 dB), AWGN, CDL-A, CDL-B, and TDL-A channel profiles and validated through rigorous MATLAB simulations. Key quantitative results demonstrate: (i) a 7-fold BER reduction over MMSE at 20 dB SNR on CDL- A (1.0 × 10⁻³ vs. 7.0 × 10⁻³); (ii) a 3–5 dB NMSE improvement across the full 0–30 dB SNR range; (iii) robust performance under Doppler spreads up to 300 km/h with less than 0.5 dB BER penalty; (iv) a 50% reduction in pilot overhead while maintaining superior MSE performance; and (v) spectral efficiency within 0.35 bits/s/Hz of the perfect-CSI Shannon bound. With a measured inference latency of 0.8 ms and a lightweight design of 2.3 × 10⁶ parameters, the proposed CNN-CE is validated as a practically deployable and resource-efficient technology for 5G and beyond-5G (B5G) networks.

Abstract: This paper presents a comparative analysis of Reinforcement Learning (RL)-based strategies for optimizing Frequency-Hopping Spread Spectrum (FHSS) systems against a first-order Markov jammer in Unmanned Aerial Vehicle (UAV) communications, addressing critical vulnerabilities in electronic warfare scenarios. The jammer model simulate adaptive threats in drone networks. Simulations were conducted within a Markov Decision Process (MDP) framework featuring 16 channels and episodes of 1000 steps. Three approaches were evaluated: Baseline random channel selection, Tabular Q-Learning, and Deep Q-Network (DQN) employing 16-128-128-16 neural architecture. Training spanned 100–500 episodes, with performance assessed via key metrics: Success Rate (%), Bit Error Rate (BER), Signal-to-Noise Ratio (SNR), action Entropy, and Packet Loss Rate (PLR) under Forward Error Correction (FEC).

Abstract: Dirty paper coding (DPC) is applied to linear multi-input multi-output (MIMO) broadcast channels with additive white Gaussian noise and one message per receiver. The method decomposes each receiver channel into parallel scalar channels with known interference, then applies modulo operators, amplitude-shift keying (ASK), and probabilistic shaping. The achievable rate tuples include all points inside the capacity region by choosing truncated Gaussian shaping, large ASK alphabets, and large modulo intervals. Simulations with short polar codes show significant rate gains from DPC compared to conventional linear precoding, while maintaining similar encoder and decoder complexities.

Abstract: This paper presents a novel optimal control-based beamforming framework for phased antenna arrays, targeting advanced wireless communication and radar applications, including 5G systems. Unlike conventional beamforming techniques such as Fourier-based methods and adaptive algorithms (e.g., LMS and RLS), the proposed approach formulates the beam synthesis problem as a discrete-time optimal control problem. The antenna array is modeled using a state-space representation, and a quadratic cost function is introduced to jointly minimize the deviation from a desired radiation pattern and the excitation power. The optimal excitation weights are derived using the Linear Quadratic Regulator (LQR) framework by solving the discrete-time algebraic Riccati equation. This formulation enables an effective trade-off between sidelobe suppression, main lobe accuracy, and power efficiency. Simulation results demonstrate that the proposed method achieves a well-focused main beam, significantly reduced sidelobe levels, and improved directivity compared to conventional approaches. Furthermore, the framework offers robustness and computational efficiency, making it suitable for real-time implementation, particularly on embedded platforms such as FPGA-based systems. Overall, the proposed optimal control-based beamforming approach provides a powerful and flexible solution for next-generation antenna systems in 5G and radar applications.

Abstract: Federated learning (FL) is an attractive learning paradigm for privacy-preserving edge intelligence because it allows distributed devices to train a shared model without moving raw data to a central server. This feature is especially relevant to 5G and emerging 6G networks, where ultra-low latency, dense connectivity, and edge-native computing are expected to support large-scale intelligent services. Nevertheless, practical FL deployment remains difficult in heterogeneous wireless environments because client devices differ in processing capability, battery budget, data volume, and channel quality. These differences create stragglers, increase round latency, and waste scarce communication resources when client participation is scheduled naively. This study develops a deployment-oriented framework for dynamic client selection and resource allocation in heterogeneous edge environments. We formulate each FL round as a latency-constrained optimization problem that jointly captures computation time, uplink transmission time, and minimum participation requirements. On this basis, we propose a Dynamic Client Selection and Resource Allocation (DCS-RA) method that ranks clients using a weighted score combining computational capability, channel quality, and a fairness term, followed by a greedy radio-resource allocation procedure that prioritizes the largest marginal reduction in estimated completion time. Using the reported simulation setting with 100 clients and 20 resource blocks, DCS-RA reduces average round-completion time from 1.92 s to 1.55 s on MNIST and from 2.02 s to 1.57 s on CIFAR-10, corresponding to improvements of 19.39% and 22.47%, respectively. The results indicate that lightweight joint scheduling can substantially improve wall-clock efficiency for FL over heterogeneous 5G/6G edge networks.

Abstract: The combination of ultra-dense network deployments and high mobility results in an unfavorable outcome, rendering the task of handover more difficult than in environments typical of previous generations. 5G and 6G necessitate the deployment of heterogeneous networks and small cells to meet the demand, which at the same time introduces certain challenges. This scenario introduces small cells (such as femtocells, picocells, and microcells) that have very limited coverage areas, which, combined with the high speed of user equipment, create an excessive number of handover triggers, leading to the “ping-pong effect,” which wastes network resources and degrades the overall Quality of Service. Furthermore, high mobility means that a user might enter and exit a cell in less time than the mobile terminal’s dwell time, dropping the connection and resulting in handover failures and radio link failures. The conventional handover methods that rely on thresholds of certain factors such as the received signal strength could be insufficient for these environments. Different criteria should be balanced to avoid the drop, such as the user’s velocity, dwell time, target cell load, available bandwidth, device battery, and application latency requirements. Predictive methods could be a more efficient alternative to the existing reactive ones. This paper presents a decision-tree-based algorithm as one predictive method that learns the patterns among all the criteria mentioned and is particularly useful for avoiding ping-pongs and limiting handover failures. The classifier is trained on real multi-operator drive-test data with ping-pong events excluded from the positive class, and evaluated under Leave-One-Trace-Out cross-validation on 16 traces covering UMTS, HSUPA, HSPA+, and LTE cells. The proposed system achieves F1=0.642 and AUC =0.797 under LOTO, with a +0.052F1 lift over the best threshold-based baseline, while remaining interpretable and deployable in real time. The paper aims to present a solution applicable also to 5G NR and 6G.

Abstract: Channel estimation is important for Orthogonal Frequency-Division Multiplexing (OFDM) in wireless channel communication and requires algorithms that offer the best accuracy while at the same time have very low computational and runtime complexities. Newtonised Orthogonal Matching Pursuit (NOMP) is a promising algorithm for channel estimation; however, it suffers from high computational complexity due to repeated refinement and least-squares updates. In this paper, we propose a low complexity NOMP variant that reduces the dominant computational cost through three modifications: (i) a residual energy-based stopping criterion for NOMP to avoid expensive CFAR evaluation, (ii) a partial cyclic refinement with frozen atoms, and (iii) approximate one-sweep per atom least-squares updates. Complexity analysis shows a reduction from O(K³) to O(KN) in the gain update and from O(K²N) to O(KN) in refinement. Simulation results show that the proposed method achieves ∼87% reduction in runtime, while the symbol error rate (SER) performance is comparable to classical NOMP and outperforms Oversampled OMP at high signal-to-noise ratio (SNR). These results show that NOMP can be computationally efficient for OFDM systems without sacrificing estimation accuracy.

Abstract: The integration of satellite and terrestrial networks within the same spectrum is a key enabler for extending mobile connectivity in future communication systems. In this context, the Direct Connectivity between Mobile Satellite Service and International Mobile Telecommunications user equipment (DC-MSS-IMT) paradigm, currently under study within the International Telecommunication Union [1], foresees the use of terrestrial IMT frequency bands by satellite systems to directly serve conventional mobile devices. This paper presents an experimental study to assess the coexistence between a terrestrial 5G-NR receiver and a co-channel interfering signal representative of a Low Earth Orbit (LEO) satellite downlink. A controlled laboratory setup in conducted configuration was implemented to ensure repeatability and accurate control of interference conditions. Measurements were performed over four carrier frequencies representative of IMT bands (763 MHz, 1482 MHz, 2150 MHz, and 2635 MHz) [2], considering different traffic load conditions (100% and 50%) and Doppler shifts associated with satellite motion. The interference impact was evaluated in terms of receiver desensitization, defined as the increase in the total received power relative to the baseline noise level [3]. The results show that a 1 dB desensitization threshold is consistently reached when the interfering signal power is approximately 5–6 dB below the receiver noise floor, corresponding to an interference-to-noise ratio (I/N) of about −6 dB. This behavior is observed across all tested frequency bands, traffic conditions, and Doppler scenarios, indicating limited sensitivity to frequency offsets within the considered range. The findings confirm the validity of commonly adopted coexistence criteria and provide experimentally derived reference values to support ongoing regulatory and technical studies on spectrum sharing between satellite and terrestrial IMT systems.

Abstract: Digital agriculture employs a wide range of sensing, actuation, and analytics technologies to optimize productivity, sustainability, and decision-making in farming operations. However, rural and remote regions face persistent barriers, including limited network coverage and insufficient support for both low- and high-throughput applications, which hinder the deployment of conventional and broadband-intensive Internet of Things solutions. A central challenge is the lack of adequate field-level network infrastructure, with connectivity often unavailable or unreliable. This article presents a comprehensive survey of Broadband-based IoT as a solution for supporting both low- and high-data-rate digital agriculture applications, including UAVs, computer vision, and extended reality, even in settings without continuous internet connectivity. It examines how technologies such as 5G/6G, dynamic spectrum access, non-terrestrial networks, and edge computing can help address connectivity and infrastructure gaps in underserved agricultural areas. Furthermore, we introduce and analyze the concept of Evolved-Variety Technologies, which combines modified state-of-the-art modules with next-generation networks to create flexible, modular, and scalable system designs adaptable to diverse topographical and operational conditions. Beyond technical evaluations, the article examines economic feasibility, environmental sustainability, and policy implications, emphasizing the need for coordinated roles among governments, telecom providers, and agribusiness stakeholders. Our findings advocate for hybrid telecom architectures that integrate terrestrial and non-terrestrial components, leveraging emerging technologies to reduce the rural–urban digital divide and enable scalable, data-driven agriculture in underserved regions.

Abstract: Large carrier frequency offsets (CFOs) can severely distort the correlation response of the Physical Random Access Channel (PRACH), generating multiple significant peaks even for a single transmitting user equipment (UE), such that CFO-induced pseudo-peaks may exceed the detection threshold and be erroneously identified as valid peaks. This work addresses the problem of peak disambiguation under such conditions by formulating peak selection as a model-consistency validation problem under mismatch. A generalized likelihood ratio test (GLRT) is first formulated to provide a principled statistical validation of each detected candidate peak based on the estimated timing advance (TA) and CFO parameters. While theoretically grounded, this approach is shown to be insufficient under realistic large-CFO conditions, where CFO-induced peak ambiguity is further complicated by multipath-induced model mismatch. To address this limitation, a complementary residual-energy-based criterion is introduced, along with a weighted combination of both metrics, interpreted as a penalized consistency criterion for robust peak selection under model mismatch. The proposed framework enables the selection of a single reliable TA/CFO pair among multiple candidates, improving receiver robustness and reducing spurious updates. Performance is evaluated using precision, recall, and F1-score for both short and long PRACH formats under 3GPP-aligned channel models, including high-CFO and high-Doppler scenarios. Results demonstrate that the proposed weighted strategy generally provides a more robust trade-off than the individual GLRT-only and residual-only criteria.

Abstract: The fast growth of wireless communication systems and the growing need for very high data rates have been the driving force behind the creation of sixth-generation (6G) technologies that operate in the terahertz (THz) frequency region. This research represents the design and analysis of a small compact microstrip patch antenna that works in the terahertz (THz) frequency range for 6G cellular connectivity. The Rogers RT5880 substrate and annealed copper are used in the design of the suggested antenna, which aims for a 593 GHz resonance frequency. A progressive design technique that incorporates slotting and geometric optimization has been used to develop a castle shaped antenna which improve impedance matching and bandwidth to overcome the inherent constraints of traditional microstrip antennas. Excellent impedance matching is shown by the final design's near-ideal voltage standing wave ratio (VSWR) and return loss (S11) of –48.76 dB. It achieves a broad impedance bandwidth of 154.88 GHz, which far outperforms many current systems. The antenna exhibits consistent radiation characteristics in the broadside direction, a gain of 8.005 dBi, a directivity of 8.727 dBi, and an efficiency of around 91.73%. The proposed design performs very well in terms of bandwidth and efficiency, while also preserving compact dimensions and structural simplicity, as shown by a comparative comparison with most current literature. These results validate the suitability of the proposed antenna for high-speed, short-range THz communication systems in future 6G networks.

Abstract: The proliferation of Unmanned Aerial Vehicles (UAVs) in various applications has created a pressing need for robust and efficient communication systems. Fifth generation (5G) networks, with their high bandwidth and low latency, are poised to support the massive connectivity requirements of UAVs. However, the high mobility of drones presents significant challenges for handover management, leading to frequent service interruptions and degraded performance. This paper proposes a novel, first-of-its-kind framework that integrates multi-UAV trajectory prediction with proactive handover optimization in 5G networks. Our approach utilizes a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to predict the future flight path of each UAV. The predicted trajectories are then fed into a Deep Reinforcement Learning (DRL) agent, which makes optimal handover decisions to ensure seamless connectivity and high Quality of Service (QoS). Unlike existing solutions that primarily rely on simulated data, our framework is validated using a real-world drone trajectory dataset. The experimental results demonstrate that our proposed method significantly outperforms traditional and existing machine learning-based handover schemes in terms of handover success rate, average Signal-to-Interference-plus-Noise Ratio (SINR), and handover delay. The proposed framework paves the way for more reliable and efficient drone operations in 5G and beyond networks.

Abstract: We investigate the problem of stable signal estimation under irregular observation conditions characterized by missing samples, heteroscedastic noise, and reportability constraints. In such environments, the primary engineering challenge arises from the severe curvature distortion of the objective landscape, which renders traditional Euclidean gradient methods numerically unstable. To address this, we propose a layered information-geometric framework for stable estimation on a distorted statistical manifold.The architecture consists of three integrated components: (i) a deterministic basin-safe front-end that resolves global navigation on the non-convex information landscape; (ii) a diagonal-Fisher local refiner that performs local metric normalization to correct for geometric scaling under irregular weighting; and (iii) a PT-even gatekeeper that acts as an engineering feasibility constraint by restricting the optimization trajectory to the reportable subspace. As a concrete structured path within this layered estimator, the QFRT branch—a Quaternionic Fourier–Ramanujan representation used here as an arithmetic-periodicity anchor under shared whitening and weighting—serves as a structured source of robustness under gappy observations without altering the bounded scope of the present technical claim.Across a locked stress matrix, the resulting hybrid estimator exhibits a two-layer gain structure. First, a general refinement gain in RMSE_ω is achieved via curvature-aware updates in the classical-tangent regime. Second, a specific PT-sensitive gain emerges when nuisance-coupled sectors become observable, effectively suppressing non-reportable "ghost-mode" leakage (|z| ≈ 0) where unprojected baselines suffer from substantial parameter drift. Mechanism diagnostics support a seed-path shielding interpretation: the classical front-end resolves the ω-dominant basin selection problem, shielding the downstream PT-aware refinement from unfavorable seed geometry. The resulting contribution is a technical methods framework for auditable stable estimation under missing and heteroscedastic observations.

Abstract: The rapid advancement of AI is fundamentally disrupting research and engineering. While much attention is given to how AI may optimize wireless systems, this article explores a different question: how will AI impact the ecosystem and community developing future wireless technology? We trace this transformation across the entire lifecycle, from education and core research to technical publication and production-ready network deployments. As AI increasingly automates routine tasks, the primary value of the human researcher will shift from problem-solving to problem-finding, research orchestration, and oversight of trade-off management. By actively preserving spaces for deep, unplugged thinking and steering AI toward genuine discovery rather than mere recombination, we can navigate this profound shift to ultimately elevate human ingenuity and the future evolution of the researcher.

Abstract: This study investigates the vulnerability of target signals to co-channel Linear Frequency Modulation (LFM) interference in automotive Frequency-Modulated Continuous-Wave (FMCW) radar systems. It analyzes the limitations of conventional adaptive noise cancellation (ANC) techniques, particularly slow convergence and performance degradation under intense interference. To address these issues, an improved ANC algorithm is proposed. The method generates reference signals through single-channel self-delay processing and adopts a joint optimization framework for weight adaptation, which integrates normalized variable-step-size Least Mean Squares (LMS) adaptation with a leakage factor. Notably, the algorithm achieves robust performance in high-interference scenarios without requiring additional hardware or complex signal transformations. Simulation results verify that the proposed algorithm significantly improves the signal-to-interference-plus-noise ratio (SINR) preserves signal fidelity, and enhances detection probability under strong LFM interference.

Abstract: The problem of Reduced Capability (RedCap) User Equipment (UE) positioning within indoor 5G networks is addressed. While conventional approaches rely on time-domain ranging, the limited signal bandwidth associated with RedCap devices often prevents these methods from satisfying stringent accuracy requirements. As an alternative, this paper proposes a positioning framework based on Angle-of-Arrival (AoA) measurements. The framework incorporates a low-complexity AoA estimation algorithm derived from the analysis of the spatial covariance matrix. This procedure inherently generates a link quality metric which, alongside the AoA estimate, is utilized for final UE localization. The proposed localization algorithm belongs to the class of Weighted Least Squares (WLS) estimators and provides a unified approach to UE positioning in both 2D and 3D physical space. Simulation results demonstrate the effectiveness of the proposed framework under the challenging high-multipath conditions inherent to 5G indoor deployments.

Abstract: Mobile network operators are increasingly adopting 5G Fixed Wireless Access (FWA) to meet the growing demand for high performance services in households. This study evaluated the adoption and Quality of Experience (QoE) of 5G FWA through a multi-phase study. First phase, utilized a systematic literature review to develop a structural equation modeling (SEM) framework, identifying Quality of Service (QoS) and User Experience (UX) factors. A questionnaire survey was then conducted with 42 industry experts and 52 end-users. The SEM analysis shows that UX is not transferable between FTTx and 5G FWA, as the correlation (y = - 0.052, t value = -0.100) was statistically insignificant. The technical QoS FTTx does not influence how users perceive the technical QoS 5G FWA (y = - 0.02, t value = -0.122). Bandwidth and Quality are the most critical drivers for 5G FWA success regarding UX, whereas latency, MoS, and throughput are vital for QoS. Exploratory Factor Analysis for the UX and QoS parameters of 5G FWA showed strong internal consistency across all identified factors. The framework with fit indices reflected excellent model QoS (RMSEA = 0.08, CFI = 0.973, TLI = 0.965, CMINDF = 1.254 and GFI = 0.782) and UX (RMSEA = 0.08, CFI = 0.895, TLI = 0.881, CMINDF = 1.377 and GFI = 0.655). The mathematical SEM model provides empirical evidence of the role of the service factor as observed parameters and introduces a validated theoretical framework QoE-SEM. This research contributes to the academic and telecommunications industries, to deliver a fit observe model for upcoming new technology 5G FWA and assist decision makers in formulating strategic QoE models.