Cognitive Digital Twins for Self-Aware Channel Estimation

Artificial intelligence (AI) and machine learning (ML)-based channel estimators silently degrade when propagation conditions drift from their training distributions. This letter proposes a model-agnostic cognitive digital twin (CDT) framework that combines a variational autoencoder (VAE) with latent activation monitoring to detect distribution drift and autonomously execute \textsc{continue}, \textsc{update}, or \textsc{retire} lifecycle actions without requiring ground-truth channel knowledge. The proposed framework is fully compatible with the AI-native lifecycle management envisioned in 3rd Generation Partnership Project (3GPP). Simulations over various channels demonstrate accurate drift detection and robust channel estimation, consistently outperforming conventional offline-trained deep learning estimators under moderate and severe channel drift.

Towards Intelligent Wireless Networks: The Synergy of Generative AI and Digital Twins

This paper proposes a generative AI (GenAI)-enabled digital twin (DT) framework for proactive and energy-aware wireless optimization in future 6G ecosystems. Most existing AI-assisted DT approaches remain fundamentally reactive, adjusting network parameters only after performance degradation occurs or restricting GenAI to isolated signal-level tasks such as channel estimation. This work adopts a proactive approach. Instead of responding to problems after they appear, the proposed framework continuously synchronizes channel states, mobility dynamics, traffic conditions, and energy information within a real-time DT environment, enabling the system to anticipate congestion, interference, and energy demand before they materialize. The result is a closed-loop proactive architecture that operates at the system level, jointly managing communication, mobility, and resource dynamics for autonomous wireless control. Evaluations on a UAV-assisted non-terrestrial network (NTN) scenario show approximately 69.2\% energy savings over reactive baselines while maintaining reliable quality-of-service (QoS) under dense and mobility-intensive conditions. Beyond this specific scenario, the framework offers a scalable foundation for broader AI-native 6G applications, including aerial platforms, autonomous systems, extended reality (XR), industrial automation, and space-air-ground-sea (SAGS) integrated infrastructures.

A Generative AI-Enhanced Digital Twin Framework for Proactive Interference Management in Hybrid Near/Far-Field Wireless Systems

The applications of Digital Twins (DT) and Generative AI (GenAI) have demonstrated their capabilities in modeling and learning-based wireless communications. However, their joint potential for proactive wireless system design remains largely underexplored, particularly in extremely large-scale multiple-input multiple-output (XL-MIMO) networks, characterized by hybrid near-field (NF) and far-field (FF) propagation regimes. In this work, we propose an integrated GenAI-enhanced DT framework for proactive interference management in dynamic indoor scenarios. The DT constructs a high-resolution, site-specific virtual replica of the deployment environment, understanding where and why blockage occurs within a realistic 3D representation of the indoor space. Integration of the GenAI module further assists the framework in anticipating and proactively suppressing blockage, rather than reacting after the disruption occurs. Extensive simulation results based on Sionna ray-tracing datasets demonstrate that the proposed framework achieves significant improvements in interference suppression, signal-to-interference-plus-noise ratio (SINR), and outage probability compared to conventional reactive schemes and purely deterministic DT-based approaches.

SkyTrust: Blockchain-Enhanced UAV Security for NTNs with Dynamic Trust and Energy-Aware Consensus

Non-Terrestrial Networks (NTNs) based on Unmanned Aerial Vehicles (UAVs) as base stations are extremely susceptible to security attacks due to their distributed and dynamic nature, which makes them vulnerable to rogue nodes. In this paper, a new Dynamic Trust Score Adjustment Mechanism with Energy-Aware Consensus (DTSAM-EAC) is proposed to enhance security in UAV-based NTNs. The proposed framework integrates a permissioned Hyperledger Fabric blockchain with Federated Learning (FL) to support privacy-preserving trust evaluation. Trust ratings are updated continuously through weighted aggregation of past trust, present behavior, and energy contribution, thus making the system adaptive to changing network conditions. An energy-aware consensus mechanism prioritizes UAVs with greater available energy for block validation, ensuring efficient use of resources under resource-constrained environments. FL aggregation with trust-weighting further increases the resilience of the global trust model. Simulation results verify the designed framework achieves 94\% trust score prediction accuracy and 96\% rogue UAV detection rate while outperforming centralized and static baselines of trust-based solutions on privacy, energy efficiency, and reliability. It complies with 6G requirements in terms of distributed intelligence and sustainability and is an energy-efficient and scalable solution to secure NTNs.

TinyML-Based Adaptive Pulse Shaping for Edge Intelligence in IoT/IIoT

Edge intelligence in IoT and IIoT demands lightweight algorithms for data processing on resource-constrained devices. This paper introduces a novel adaptive pulse shape filter based on TinyML for PAPR and SER optimization on edge devices used in uplink IoT communication. Implemented on IoT nodes such as sensors, our pruned neural network provides up to 2 dB PAPR saving over root-raised-cosine (RRC) filters. Mass simulations validate its efficacy in DFT-s-OFDM systems and offer an energy-efficient and scalable solution for IoT/IIoT use cases such as smart factories and rural connectivity.

AI-Driven Digital Twins: Optimizing 5G/6G Network Slicing with NTNs

Network slicing in 5G/6G Non-Terrestrial Network (NTN) is confronted with mobility and traffic variability. An artificial intelligence (AI)-based digital twin (DT) architecture with deep reinforcement learning (DRL) using Deep deterministic policy gradient (DDPG) is proposed for dynamic optimization of resource allocation. DT virtualizes network states to enable predictive analysis, while DRL changes bandwidth for eMBB slice. Simulations show a 25\% latency reduction compared to static methods, with enhanced resource utilization. This scalable solution supports 5G/6G NTN applications like disaster recovery and urban blockage.

Spreading the Wave: Low-Complexity PAPR Reduction for AFDM and OCDM in 6G Networks

High Peak-to-Average Power Ratio (PAPR) is still a common issue in multicarrier signal modulation systems such as Orthogonal Chirp Division Multiplexing (OCDM) and Affine Frequency Division Multiplexing (AFDM), which are envisioned to play a central role in 6G networks. To this end, this paper aims to investigate a novel and low-complexity solution towards minimizing the PAPR with the aid of a unified premodulation data spreading paradigm. It analyze four spreading techniques namely, Walsh-Hadamard transform (WHT), Discrete Cosine transform (DCT), Zadoff-Chu transform (ZC), and Interleaved Discrete Fourier transform (IDFT), which assist in preallocating energy prior to OCDM and AFDM modulation. The proposed method takes advantage of the inherent characteristics of chirp-based modulation to achieve a notable reduction in PAPR at minimal computational load and no side information as compared to past solutions, such as Partial Transmit Sequence (PTS) or Selected Mapping (SLM), which suffers with a high computational complexity. The proposed method has an additional benefit of achieving an improvement in phase selectivity by increasing chirp parameters of AFDM and quadratic phase of OCDM, which amplifies the robustness in doubly dispersive channels. It further reduces interference by smoothing the output spread signal. The analytical and simulation results demonstrate an improvement in the overall energy efficiency and scalability of large ioT sensor networks.

Stealth Signals: Multi-Discriminator GANs for Covert Communications Against Diverse Wardens

Covert wireless communications are critical for concealing the existence of any transmission from adversarial wardens, particularly in complex environments with multiple heterogeneous detectors. This paper proposes a novel adversarial AI framework leveraging a multi-discriminator Generative Adversarial Network (GAN) to design signals that evade detection by diverse wardens, while ensuring reliable decoding by the intended receiver. The transmitter is modeled as a generator that produces noise-like signals, while every warden is modeled as an individual discriminator, suggesting varied channel conditions and detection techniques. Unlike traditional methods like spread spectrum or single-discriminator GANs, our approach addresses multi-warden scenarios with moving receiver and wardens, which enhances robustness in urban surveillance, military operations, and 6G networks. Performance evaluation shows encouraging results with improved detection probabilities and bit error rates (BERs), in up to five warden cases, compared to noise injection and single-discriminator baselines. The scalability and flexibility of the system make it a potential candidate for future wireless secure systems, and potential future directions include real-time optimization and synergy with 6G technologies such as intelligent reflecting surfaces.