Journal of AI-Driven Communication Engineering
The Journal of AI-Driven Communication Engineering is an international, peer-reviewed journal focused on integrating artificial intelligence, machine learning, and data-driven techniques into communication system engineering. It provides a platform for innovative research that enhances the design, optimization, security, and performance of modern communication networks. The journal covers topics such as intelligent signal processing, AI-powered network management for 5G/6G, cognitive radio, IoT, deep learning for signal analysis, communication security, and AI-driven virtualization technologies, aiming to bridge AI innovations with real-world communication challenges.
Conversational AI in Public Service Delivery: Trust, Accessibility, Accountability, and the Communication Imperative in Citizen–Institution Interaction
Public institutions are among the most consequential contexts for the deployment of conversational artificial intelligence. When AI systems mediate communication between citizens and government employment agencies, healthcare providers, social welfare offices, and administrative bodies, the stakes of miscalibrated trust, inadequate interpretability, and poor interaction efficiency extend beyond individual user experience to include access to rights, benefits, and services. This paper examines the specific challenges and opportunities of deploying conversational AI in public service communication contexts and develops the Public Service Conversational AI (PSCAI) framework as both a conceptual and an implementation-oriented model. The revised framework links citizen-facing dialogue interfaces to authoritative knowledge bases, case-management systems, human escalation pathways, audit logs, and continuous feedback mechanisms. It further provides practical guidance for applying the framework in real government service environments such as employment services, welfare benefit guidance, healthcare administration, and municipal service portals. We argue that public sector conversational AI requires a fundamentally different design philosophy from commercial AI: one centered on institutional accountability, citizen dignity, legal accuracy, equitable access, and appealable human oversight rather than pure efficiency optimization.
Special Issue: Artificial Intelligence Across the Communication Stack: Engineering, Human Interaction, and Governance in the 6G Era
Generative AI as Mediator of Interpersonal Communication: Effects on Social Dynamics, Linguistic Norms, and Relational Authenticity
The integration of generative artificial intelligence into everyday interpersonal communication - through AI-assisted writing, real-time translation, automated summarization, and conversational suggestion tools - represents a structural transformation in the mediation of human-human communication. Unlike earlier communication technologies that mainly transmitted or stored human-generated content, generative AI actively co-produces communicative output through a system architecture that includes user interfaces, prompt and context management, large language model inference, personalization modules, disclosure controls, and post-generation human editing. This paper examines AI-mediated interpersonal communication (AIMIC) as both a social phenomenon and an implementable communication system. It analyzes three interconnected effects: linguistic convergence, redistribution of communicative labor, and challenges to relational authenticity. The revised AIMIC framework links these effects to concrete implementation layers in email assistants, messaging platforms, translation tools, collaborative writing systems, and organizational communication software. The paper argues that responsible AIMIC design requires preserving human communicative agency, making AI involvement contextually transparent, monitoring linguistic and relational impacts, and embedding governance mechanisms into the communication pipeline. The paper concludes with practical examples, evaluation recommendations, and design principles for platform developers, educators, organizations, and policymakers.
Affective Dimensions of Trust in AI-Mediated Human Communication: Toward an Emotionally Intelligent Design Framework for Conversational Agents
As large language model (LLM)-based conversational agents become embedded in everyday communication contexts, the affective dimensions of user-AI interaction have emerged as a critical engineering and governance concern. While cognitive trust is usually associated with perceived accuracy, reliability and transparency, affective trust refers to the emotionally mediated sense of comfort, warmth and relational security that users experience when interacting with an AI system. This paper examines the theoretical foundations of affective trust in human-AI communication and proposes the Affective Trust Engineering (ATE) framework as a structured design approach for emotionally intelligent conversational agents. In response to implementation-oriented gaps in the literature, the revised framework links affective trust dimensions to concrete AI communication system components, including affect detection, context modelling, prompt and policy orchestration, response generation, safety gating, audit logging and continuous evaluation. The paper also introduces a conceptual implementation diagram, a short research approach section, real-world application examples, and a completed reference base grounded in recent human-AI interaction, affective computing and AI governance literature. The central argument is that affective trust should be designed as a calibrated, bounded and measurable system output rather than treated as a diffuse by-product of pleasant interaction.
Human–Machine Communication in the Age of Generative AI: Engineering Trust, Interpretability, and Interaction Efficiency in Intelligent Systems
The proliferation of generative artificial intelligence (GenAI) systems has fundamentally transformed the landscape of human–machine communication (HMC). This paper examines three critical engineering dimensions of this transformation: trust calibration, interpretability mechanisms, and interaction efficiency. Drawing on recent advances in large language models (LLMs), conversational agents, and human–computer interaction (HCI) research, we analyze how these dimensions interrelate and affect overall system usability. We propose a conceptual framework—the TIE (Trust–Interpretability–Efficiency) model—that explicitly builds upon and extends established HCI usability models (ISO 9241-11; Nielsen, 1994) and AI trust taxonomies (Lee & See, 2004; Hoff & Bashir, 2015) to provide actionable design principles for GenAI communication systems. Novelty lies in the tripartite synthesis and the explicit modeling of interdependencies among cognitive trust, affective trust, behavioral reliance, multi-level interpretability (model-, system-, and interface-level), and operationally defined efficiency metrics. Our analysis indicates that trust and interpretability share a bidirectional dependency, while efficiency gains are contingent on users developing accurate mental models of AI capabilities. Ethical and governance dimensions—including accountability and misuse prevention—are integrated as co-equal design concerns. Applicability is primarily scoped to LLM-based conversational systems, with acknowledged limitations for multimodal and embodied AI. The paper concludes with a structured evaluation checklist and metric table to support practitioner utility.
Score-Based Diffusion Models for Wireless Channel Synthesis and Data Augmentation in Low-Pilot MIMO Systems
High-quality channel estimation in massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surface (RIS)-assisted systems depends critically on the availability of accurate channel state information (CSI), but dense pilot acquisition competes directly with spectral efficiency and latency targets in fifth- and sixth-generation (5G/6G) networks. Score-based diffusion models can synthesize physically plausible wireless channel samples and can also be conditioned on sparse pilot observations to support low-pilot channel estimation. This paper reviews diffusion-based channel synthesis from both a modelling and a receiver-deployment perspective. In addition to denoising diffusion probabilistic models (DDPM), score matching with Langevin dynamics (SMLD), and consistency models, the revised discussion explicitly addresses practical constraints: integration with DM-RS/CSI-RS based receiver pipelines, offline versus online deployment modes, latency budgets imposed by channel coherence time, and hardware limitations at the base-station edge and user equipment. Synthesis fidelity is discussed using normalized mean square error (NMSE), power delay profile (PDP) preservation, spatial correlation preservation, and downstream channel-estimation performance across three types of evidence: stochastic simulation with QuaDRiGa Urban Macro, ray-tracing simulation with DeepMIMO at 28 GHz, and over-the-air measurements from the DICHASUS testbed. The paper further clarifies that results obtained on synthetic, ray-tracing, and real-world datasets are complementary but not directly interchangeable. Computational complexity, inference latency, and robustness risks are therefore treated as central deployment criteria rather than secondary implementation details.
Federated Deep Learning for Telecom-Orchestrated Anomaly Detection in Industrial IoT Critical Infrastructure Networks
Industrial Internet of Things (IIoT) critical infrastructure is increasingly connected through private 5G/6G networks, software-defined transport, network slicing and O-RAN-compatible orchestration stacks. In such environments, anomaly detection is not only an application-layer cybersecurity task; it is also a communication-engineering signal that can trigger changes in RAN, core and transport control loops. Centralizing sensor, radio and network telemetry for high-accuracy deep anomaly detection violates data-sovereignty and regulatory requirements, while isolated local models fail to generalize across heterogeneous plants and private-network deployments. Federated deep learning (FDL) offers a privacy-preserving architecture for collaborative anomaly detection, but its practical value depends on how anomaly scores are mapped to telecom control elements such as network slices, QoS flows, RIC xApps/rApps, SDN/NFV routing and isolation functions. This paper reviews FDL architectures for IIoT anomaly detection with explicit grounding in telecom system design. Beyond F1-score comparison, the analysis emphasizes radio and network KPIs including SINR degradation, PRB utilization, handover failure rate, link latency, throughput, QoS/QoE, control overhead and spectrum constraints. The paper further explains how anomaly outputs can support automated control actions such as rerouting, slice-profile adjustment, QoS enforcement, edge isolation and SDN/NFV remediation, while respecting radio latency budgets, closed-loop stability and safety requirements. Security threats, communication overhead, model compression and regulatory constraints are analyzed as deployment factors for private 5G/6G critical-infrastructure networks.
Large Language Models as Cognitive Network Agents: Intent-Based Management and Self-Optimization in 6G Communication Systems
The management of sixth-generation (6G) communication networks demands a level of autonomy, contextual reasoning, and cross-layer decision-making that exceeds the capacity of conventional rule-based network management systems. This paper positions large language models (LLMs) not merely as text-generating systems or benchmark-oriented assistants, but as supervisory cognitive agents within AI-driven communication engineering. The core contribution is a system-level analysis of how LLM agents can translate operator intent, telemetry, alarms, and standards knowledge into verifiable network-control actions affecting network slices, QoS profiles, RAN Intelligent Controller (RIC) xApps/rApps, scheduling policies, and fault-management workflows. The paper reviews telecom-specific LLM adaptation methods, intent-based networking, zero-touch configuration, resource management, and security risks, while evaluating practical relevance through communication engineering metrics such as latency, throughput, spectral efficiency, SLA violation rate, control overhead, and closed-loop stability. Deployment constraints are discussed with explicit attention to O-RAN and 6G control-loop timescales, showing that near-term feasibility lies in management-plane and policy-supervision roles rather than direct sub-millisecond PHY/MAC control.
Artificial Neural Networks in Next-Generation Communication Systems: Architectures, Applications, and Deployment Challenges
The proliferation of intelligent wireless systems and the advent of sixth-generation (6G) networks have rendered traditional model-based signal processing approaches increasingly inadequate for managing the complexity, heterogeneity, and dynamism of modern communication infrastructures. Artificial neural networks (ANNs) have emerged as a transformative paradigm, enabling data-driven solutions for tasks ranging from channel estimation and modulation recognition to resource allocation and anomaly detection. This review synthesizes significant developments in ANN architectures applied to communication engineering, spanning multi-layer perceptrons, convolutional and recurrent networks, transformer-based models, graph neural networks, and spiking neural networks, and critically evaluates their applicability within real-world deployment constraints including hardware budgets, latency requirements, and standards compliance. We systematically analyse performance gains across key application domains including adaptive beamforming, end-to-end autoencoder design, federated network management, and energy-efficient edge inference. Furthermore, we examine persistent challenges such as catastrophic forgetting, adversarial vulnerability, data poisoning, model confidentiality risks, interpretability deficits, and the tension between model complexity and real-time deployment. The review concludes by delineating open research directions with emphasis on neuromorphic computing, physics-informed neural networks, and privacy-preserving collaborative learning frameworks.
Artificial Intelligence and Quantum Computing in Data-Driven Industrial Systems
Modern industrial environments are evolving into data-intensive cyber-physical systems that require robust computational frameworks for performance prediction and optimization. While existing literature has addressed developments in statistical methods, artificial intelligence, and quantum computing individually, there remains a lack of systematic reviews examining the integrated evolution and data processing capabilities of these three paradigms. This review addresses the need to clarify the capabilities, limitations, and application domains of each approach to enable engineers to select appropriate data-driven methodologies for specific optimization challenges. In this review, we traced the historical development of optimization methodologies from design of experiments and response surface methodology through neural networks and generative models to variational quantum algorithms, presented chronological development tables documenting key milestones in each paradigm, and analyzed industrial implementation cases including conversion rate increases and emission reductions. The analysis reveals that statistical methods exhibit unique strengths in systematic data analysis, AI in complex pattern recognition, and quantum computing in high-complexity simulation, with their hybrid integration providing optimal performance. This study provides significance in offering a comprehensive framework necessary for connected industries to strategically deploy multi-paradigm optimization strategies within integrated network environments to achieve sustainability goals while maintaining global competitiveness.
Artificial Intelligence and Quantum Computing in Criminal Justice Systems: A Communication Engineering Perspective
Modern communication systems face unprecedented challenges in ensuring secure data transmission and real-time processing across distributed networks. The convergence of artificial intelligence (AI) and quantum computing (QC) fundamentally transforms communication engineering by introducing both enhanced capabilities for intelligent network management and critical threats to cryptographic security protocols that underpin global communications. These challenges are particularly acute in high-security domains such as criminal justice systems, where communication infrastructure must balance stringent security requirements with evidentiary reliability. The administration of criminal justice has historically relied on the epistemological reliability of evidence and the ontological security of information. However, the legal profession currently faces a radical discontinuity driven by the simultaneous maturation of Generative Artificial Intelligence (AI) and the accelerating development of Quantum Computing (QC). Generative AI has introduced a regime of "probabilistic truth," leading to the proliferation of hallucinated legal texts and synthetic media that threaten evidentiary standards. Parallel to this, the looming reality of QC poses a fundamental threat to the cryptographic locks securing sensitive criminal justice data, notably through strategies that target current encrypted data for future decryption. As the integration of these advanced technologies becomes an irreversible trend, there is a critical need to synthesize these divergent yet interconnected threats to understand their collective impact on judicial integrity. This review analyzes the epistemological crisis precipitated by the integration of algorithmic text generation into legal workflows and the challenges posed to digital forensics by the potential compromise of encryption standards. Furthermore, it explores the transformative potential of Quantum Machine Learning (QML) in unraveling sophisticated modern criminal schemes, particularly for identifying complex patterns in financial crimes and criminal networks, while also addressing the technical hurdles limiting the practical deployment of these models. This study underscores the critical necessity for the legal system to fortify procedural defenses against AI-generated misinformation and to accelerate the migration to quantum-resistant security infrastructures. Ultimately, this review highlights that preserving the validity of the justice system requires commitment to technological literacy and the establishment of rigorous verification frameworks to navigate the dual disruption of algorithmic probabilities and quantum insecurity.
A Detailed Analysis of Backup and Recovery Techniques in Database Management Systems (DBMS)
In this exponential growth age and frequent disruption, data has to be really secure, integral, and accessible. DBMS plays a keynote in all these aspects. This report reviews the backup and recovery techniques of four DBMSs: Cassandra, MongoDB, Neo4j, and Oracle. It discusses their efficacy in restoring data loss and maintaining schema integrity. This study throws light on the challenge of complete data and schema recovery considering operational nuances and system-specific requirements. The recovery processes were recreated in the controlled environments by using hands-on experimentation. Recovery percentages for each DBMS were computed from recovered data. The results of the study indicate that 100% success rate for all systems, which means these systems do not fail in case of a disaster. These results give critical insight into the reliability and application-specific advantages of each DBMS in guiding database administrators in the design of resilient systems.
AI-Driven Engineering for Urban Problem Solving: Recent Advances and Case Studies
The integration of Artificial Intelligence (AI) techniques within engineering processes has significantly reshaped the design and operation of urban environments. This paper investigates recent applications of AI-driven methods in engineering, with particular attention to urban-scale problems, including mobility optimization, infrastructure monitoring, and adaptive systems for resource management. We survey multiple case studies, illustrating how AI systems have been employed not merely as analytical tools but as active agents in autonomous decision-making pipelines. The review highlights emerging patterns, challenges, and opportunities in the deployment of AI within city-scale engineering ecosystems.
AI-Driven Signal Processing and Network Management for Next-Generation Communications
The acceleration of wireless technologies from 5G toward 6G has intensified demands for intelligent, adaptive, and secure communication infrastructures. This paper provides a comprehensive synthesis of artificial intelligence (AI) methodologies that advance signal processing and network management across next-generation communication systems. First, we examine AI-driven intelligent signal processing, including deep learning techniques for modulation recognition, waveform generation, channel estimation, and interference mitigation, emphasizing their applicability under heterogeneous, high-dimensional, and noisy environments. Cognitive radio and dynamic spectrum access mechanisms are analysed with a focus on learning-based spectrum sensing and adaptive policy selection, enabling efficient exploitation of scarce spectral resources. In parallel, we investigate IoT communication constraints—energy, latency, jamming resilience—and how AI optimizes scheduling, traffic adaptation, and resource usage within ultra-low-power environments. At the network level, we discuss the integration of AI with Open RAN architectures, virtualization frameworks (NFV/SDN), network slicing, and distributed orchestration for 5G/6G. Special emphasis is placed on AI-supported resource allocation, multi-agent reinforcement learning, edge intelligence, and federated learning for collaborative, privacy-preserving management across large-scale, heterogeneous networks. Security challenges—including anomaly detection, adversarial robustness, and physical-layer protection—are evaluated within the broader context of AI-enabled network defence. Finally, we highlight benchmarking limitations and the need for standardized datasets, reproducibility protocols, and deployment-ready evaluation criteria. Collectively, the findings underscore AI as a foundational enabler of resilient, efficient, and scalable communication systems for emerging 6G and beyond.
Modern Cybersecurity Tools: A Comprehensive Technical and Academic Study to Classify and Analyze
The process of securing our digital assets—such as connections, systems, programs, and networks—from any potential issues is referred to as cybersecurity. The goal is to safeguard information in order to maintain its confidentiality, accuracy, and availability when it is needed. In order to do this, it is necessary to safeguard computers and networks from a variety of threats, including but not limited to malicious software and unauthorized intrusions. The purpose of this research is to investigate and classify the most recent cybersecurity technologies from an all-encompassing, academic, and technological perspective, with a particular focus on the following four key dimensions: tools for network analysis, application security, cloud security, and digital incident management systems. As the rate of digital transformation accelerates and a greater number of people rely on cloud systems, we are in need of more advanced and intelligent technologies for the purpose of identifying potential dangers, monitoring traffic, and detecting breaches at an earlier stage. This study employs a descriptive-analytical methodology that integrates technical evaluations of widely used tools, including Wireshark, Nmap, Burp Suite, Snort, Prisma Cloud, and TheHive, with an academic comparison of findings from various prior studies conducted by organizations such as NIST, ENISA, and OWASP. The research further analyzes technical performance parameters (efficiency, accuracy, reaction time) and reliability indicators (resilience, integration, scalability) across various operational contexts. The findings demonstrate that the amalgamation of cloud tools and network analysis tools attains the furthest levels of holistic security, particularly when artificial intelligence is integrated into the predictive monitoring layers. The report also suggests using hybrid security architectures that can learn on their own and adapt to new threats in real time. A descriptive-analytical methodology was used, underpinned by a literature analysis of studies published from 2018 to 2025. The findings indicated that the amalgamation of network and application security solutions is the primary protection against advanced threats, whilst cloud security and incident reporting tools augment reaction velocity and operational resilience. The study ends with suggestions on how to use artificial intelligence in security monitoring systems to make self-defense and predictive protection more effective.
Behavioural Insights into Online Shoppers’ Purchase Intention Using Machine Learning Models
With evolving dynamics in e-commerce, user behavior analysis on e-commerce websites has grown more crucial in customer experience improvement and conversion rate optimization. Predictive analytics is instrumental in deriving hidden patterns from user behavior and enabling data-informed decisions. However, amidst vast amounts of web traffic data, the majority of online retailers struggle to identify actionable behavioral cues that accurately predict buying intent on a consistent basis. This study addresses the issue of accurately predicting purchasing intention based on session-based user behavior and demographic data. By examining the "Online Shoppers Purchasing Intention" dataset available at the UCI Machine Learning Repository, this project aims to predict whether or not a user will make a purchase during a session. Using Python as the primary tool, the study employs data preprocessing, exploratory data analysis, feature selection, and machine learning algorithms like Logistic Regression, Random Forest, and Support Vector Machines. The performance of these algorithms is evaluated using accuracy, precision, recall, and F1-score. Preliminary results show that page value, bounce rate, and visit month have a significant influence on purchase likelihood. The results highlight the importance of behavioral data in predicting e-commerce outcomes. The results can be utilized to inform strategic planning in UX design, online marketing, and inventory management.
Enhancing Data Security in Smart Cities: A Trust-Based Approach Leveraging Elliptic Curve Cryptography
Background and Objectives: Smart cities rely on interconnected Internet of Things (IoT) devices, which face significant data security and privacy challenges due to their resource-constrained nature. While traditional cryptographic methods are often too computationally heavy for such environments, a scalable and efficient security solution that dynamically adapts to device trustworthiness remains lacking. This paper aims to address this gap by proposing a lightweight, trust-based security framework leveraging Elliptic Curve Cryptography (ECC). Methods: The study introduces a decentralized trust management framework that integrates ECC for efficient key exchange, data encryption, and secure communication with minimal computational overhead. A reputation-based system continuously evaluates device trustworthiness based on behavioral patterns and interaction history. This trust metric dynamically adjusts cryptographic strength—applying stricter security measures for interactions involving untrusted or suspicious entities. Additionally, a lightweight ECC-based authentication protocol is implemented to support secure device onboarding and access control within the smart city ecosystem. The framework was rigorously evaluated through extensive simulations and empirical testing against common IoT threats. Results: The proposed framework demonstrated robust resilience against critical security threats, including man-in-the-middle attacks, eavesdropping, and unauthorized access attempts. Simulation results showed that the ECC-based approach significantly reduces computational overhead and energy consumption compared to traditional cryptographic techniques, while maintaining high security standards. The dynamic trust mechanism effectively identified and isolated compromised or malicious devices, enhancing overall network integrity. Furthermore, the authentication protocol enabled seamless yet secure integration of new devices into the IoT infrastructure without degrading system performance. Conclusion: This study presents a highly effective and scalable security solution tailored for resource-constrained IoT environments in smart cities. The primary contribution is a decentralized, trust-aware ECC-based framework that balances security and efficiency. Additional findings highlight its adaptability to evolving threat landscapes and its practical viability for real-world deployment. These results underscore the potential of integrating cryptographic agility with behavioral trust models to future-proof smart city infrastructures, offering both theoretical advancement and practical value for IoT security.
Enhancing IRS Localization via Deep Learning-Based AOA and Distance Estimation
In Intelligent Reflecting Surface (IRS)-assisted communication systems, accurate user localization, particularly Angle-Of-Arrival (AoA) and range estimation are challenging due to the computational complexity and limited resolution of traditional Multiple Signal Classification (MUSIC) algorithms. This paper introduces a hybrid IRS framework that combines machine learning with a modified MUSIC algorithm to achieve high-precision localization and enhanced security. The system integrates two Convolutional Neural Networks (CNNs): RefineNet, which refines AoA and range estimates from MUSIC pseudo-spectra, and ElementNet, which optimizes the number and placement of active IRS elements to balance accuracy with resource efficiency. Notably, ElementNet shows that only eight active elements are sufficient to obtain 90% of the best achievable localization accuracy, highlighting the efficiency of the proposed design. Validation on the DeepMIMO dataset demonstrates superior range accuracy and AoA precision. This work sheds light on the secure and high-precision localization for diverse wireless applications.
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Exploring Corporate Social Responsibility Enhancement with AI Strategies into Business Development: Literature Review
This a qualitative research approach investigates to explore how corporate social responsibility enhancement with artificial intelligence strategies into business development and discover the strategies that improve the CSR initiatives by assisting AI tools. The literature on AI-driven CSR that has emerged between 2018-2025 is reviewed in this study with a focus on how it affects customer satisfaction, business development, and digital leadership. AI technologies also provide businesses with cutting-edge capabilities for transparent reporting, real-time environmental monitoring, and predictive analytics to foresee environmental and social issues through rely on AI processes and adopting strategies that enhance and develop the businesses. So that, through this research which recommended to integrate AI into CSR initiatives can boost a company’s reputation, data driven decision making, and positioning in the market. Also, conducting more future research to examine how AI strategies help to enhance CSR initiatives in various industries and sectors which to develop the businesses.
AI and Quantum Computing for Advanced Materials Design
The AI-driven inverse design paradigm is fundamentally transforming materials discovery research by enabling the computational exploration of novel materials with predefined target properties. This review comprehensively synthesizes recent progress in applying AI methodologies, such as generative models, reinforcement learning, and diffusion models, to diverse material classes including metals, polymers, and proteins. It particularly highlights key advancements, such as the AI-guided discovery of high-entropy alloys with superior mechanical properties and the de novo design of functional polymers and protein-based biomaterials. Furthermore, major remaining challenges are discussed, including the computational-to-experimental validation gap, data scarcity, and the need for physically constrained models. Furthermore, this review explores the emerging frontier of Quantum Machine Learning (QML), which holds the promise of overcoming the limitations of classical computing for particularly complex problems in materials simulation. Finally, the integration of these methodologies into fully autonomous laboratories for closed-loop design, synthesis, and characterization is presented as a transformative route to accelerate the materials discovery cycle.