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.