Telecom

This article discusses the fundamental limitations of Light Fidelity (Li-Fi) systems, an emerging visible light communication technology that is constrained by line-of-sight dependency and optical attenuation. Unlike existing adaptive modulation approaches that focus solely on improving signal processing, we present an integrated framework that combines three key contributions: (1) an adaptive modulation optimization algorithm that selects among OOK, PAM, and OFDM schemes based on instantaneous signal-to-noise ratio thresholds, achieving a 30–40% range extension compared to fixed modulation references; (2) a method for spatial optimization of access points (APs) using the L-BFGS-B algorithm to determine the optimal location of APs, taking into account lighting constraints and coverage uniformity; and (3) comprehensive system-level modeling incorporating shot noise, thermal noise, inter-symbol interference, and dynamic shadowing effects for realistic performance evaluation. Through extensive simulations on multiple room geometries (6 m × 5 m to 20 m × 15 m) and AP configurations (one to six APs), we demonstrate that the proposed adaptive system achieves an average throughput 60% higher than that of fixed OOK, while maintaining 98.7% coverage in a 10 m × 8 m environment with two optimally placed APs. The framework provides practical design guidelines for Li-Fi deployment, including an analysis of computational complexity for coverage assessment, for access point optimization) and a characterization of convergence behavior. A comparative analysis with state-of-the-art techniques (optical smart reflective surfaces, machine learning-based blockage prediction, and Li-Fi/RF hybrid configurations) positions our lightweight algorithmic approach as suitable for resource-constrained deployment scenarios, where system-level integration and practical feasibility take precedence over innovation in individual components.

The increasing deployment of humanoid robots in indoor environments such as smart factories, laboratories, offices, and hospitals poses new challenges to millimeter-wave wireless communication systems. Existing human body obstruction models, while effective at characterizing pedestrian-induced signal attenuation, are not designed to directly capture the structural geometry, material composition, and controlled mobility of humanoid robotic platforms. In this work, we first reproduce a well-established human-body-based propagation model under comparable indoor conditions and subsequently extend this hybrid framework to controlled humanoid-based scenarios by combining double knife-edge diffraction (DKED) with a modified street-canyon reflection model operating at 28 GHz. Compared to existing human-based studies, the proposed approach explicitly incorporates the material properties of the humanoid robot’s envelope through a calibrated correction factor and accounts for its controlled lateral movements. An indoor measurement campaign using three programmable humanoid robots was conducted to evaluate the model. Experimental results show that humanoid robots can reproduce attenuation trends and obstruction dynamics consistent with those reported in prior human-body blockage studies, while offering improved repeatability and greater experimental control. The proposed framework provides a practical and reproducible tool for modeling indoor millimeter-wave channels under controlled humanoid-based experimental conditions, in environments involving mobile robotic agents.

This paper presents the design of a highly reconfigurable interdigital bandpass filter (BPF) developed through a three-stage design approach. In the first stage, the influence of four low-loss dielectric substrates on the filter response is systematically analyzed to identify the optimal configuration. The selected substrate demonstrates excellent performance, achieving an input return loss of −38 dB, an insertion loss of −0.9 dB at 4.30 GHz, and a wide passband corresponding to a bandwidth (BW) of 2.20 GHz. In the second stage, two variable capacitors were incorporated into the baseline geometry, enabling manual tuning of the center frequency ($f_0$) from 5.10 to 6.34 GHz, with $\left(S_{11}\right)$ better than −25 dB and ${(S}_{12})$ close to −0.60 dB. In the final stage, the capacitors were replaced by SMV1413 varactor diodes, transforming the design into a fully voltage-controlled tunable filter. This configuration provides continuous frequency agility from 4.70 to 5 GHz without modifying the physical structure, while achieving $\left(S_{11}\right)$ levels down to −40 dB and insertion loss as low as −0.7 dB. The proposed architecture offers a compact, low-loss, and electrically reconfigurable solution, making it a promising solution for next-generation RF front-ends, adaptive wireless systems, and cognitive radio applications. Two independent Electromagnetic solvers (EM) were employed to validate the filter’s performance: an EM based on the Finite Integration Technique and the Advanced Design System 2026 (ADS) solver using the Method of Moments (MoM). The close agreement between the results produced by both platforms confirms the accuracy and robustness of the proposed reconfigurable bandpass filter structure.