The evolution of 5G and forthcoming 6G networks enables Ultra-Reliable Low-Latency Communication (URLLC) for critical healthcare applications, including robotic surgery, AR/VR-assisted telepresence, and emergency diagnostics. However, existing telemedicine systems struggle to meet sub-millisecond latency, deterministic reliability, and high-accuracy indoor localization requirements, particularly in dynamic hospital environments with energy-constrained Internet of Medical Things (IoMT) devices. This study proposes a scalable, privacy-aware, and robust 6G-enabled telemedicine framework that jointly ensures URLLC performance, precise indoor positioning, and optimized network and reconfigurable intelligent surface (RIS) resources. The proposed architecture uniquely integrates edge intelligence, federated learning, and AI-driven RIS control, optimized through the Enhanced Dragonfly Optimization Algorithm (EDOA) for multi-objective decision-making. The framework consists of four layers: physical (RIS-assisted communication and positioning), edge intelligence (AI-based controllers with federated learning), optimization (EDOA-based resource and RIS phase allocation), and network (distributed telemedicine traffic management). IoMT devices locally train models and exchange encrypted updates with edge servers, preserving data privacy while improving global intelligence. Extensive simulations demonstrate superior performance, achieving an average end-to-end latency of 0.82 ms, 99.8% reliability, 21.6 dB SINR, 38.9 Mbps throughput, and a localization error of 0.48 m, outperforming conventional 5G baselines, static RIS, and edge AI-only approaches. The proposed framework establishes a reliable foundation for next-generation digital healthcare systems.
Space–time-modulated metasurfaces enable versatile manipulation of electromagnetic waves across both spatial and spectral domains. However, most existing approaches rely on dynamic phase modulation, whose implementation becomes increasingly challenging at frequencies above 100 GHz due to limited phase-tuning range and higher implementation complexity of active phase-control components. In addition, conventional 1-bit amplitude-coded metasurfaces suffer from a fundamental limitation in independent harmonic power allocation, since the harmonic spectrum is governed by a single degree of freedom associated with the modulation duty cycle. To address these challenges, a wideband graphene-based 2-bit amplitude-coded space–time-modulated metasurface is proposed for simultaneous harmonic power engineering and beam steering in the sub-THz regime. Four discrete amplitude states are introduced within each modulation period and physically realized through tunable graphene reflection-amplitude states, providing additional temporal degrees of freedom for harmonic spectrum synthesis. The temporal coding sequence is optimized using a simulated annealing (SA) algorithm to realize the prescribed harmonic power distributions. Numerical results demonstrate the accurate realization of prescribed harmonic power distributions with allocation errors below 3% while preserving beam-steering functionality. Furthermore, the practical implementation aspects, including finite switching and fabrication tolerance effects, are considered to evaluate the robustness of the proposed graphene-based space–time modulation scheme. Additionally, selective harmonic suppression and deep spectral notches are achieved through the proposed multi-level amplitude coding strategy. The proposed framework provides a practical route toward programmable harmonic power engineering for future sub-THz and THz communication systems, harmonic multiplexing, and intelligent electromagnetic environments.
Mobile wireless nanonetworks are emerging communication systems composed of nanoscale devices capable of sensing, processing, and exchanging information in highly constrained environments. Electromagnetic communication in the terahertz (THz) band is considered a promising solution for such networks because it enables very high data rates and fast signal propagation. However, routing in THz-based mobile wireless nanonetworks remains a major challenge due to the limited energy, memory, and processing capabilities of nanonodes, the very short communication range, and the dynamic topology caused by node mobility. This paper presents a systematic survey of routing protocols for THz-based mobile wireless nanonetworks. The selected protocols are organized into several categories, namely thermal-aware, flow-guided, mobile nanorouter-based, energy-aware, learning-based, adaptive, and heuristic-based routing approaches. The analysis shows that only few studies explicitly address mobile THz-based routing, and this limited corpus highlights the emerging nature of the field. The survey also identifies key open challenges, including reliable relay selection under dynamic topology, energy-efficient forwarding, realistic mobility modeling, scalability, and the need for stronger validation frameworks. Finally, the paper outlines future research directions for designing lightweight, robust, and mobility-aware routing protocols for THz-based mobile wireless nanonetworks.
Monitoring cell signaling pathways will be an essential function of intrabody nanonetworks in the Internet of BioNano Things (IoBNT). Many human diseases result from dysregulation of these pathways, caused by irregularities in receptor function and expression. Detecting receptor functionality and expression is therefore important. This paper proposes detecting receptors' functionality and expression using backscatter communication. Molecules are released in an extracellular environment using a point transmitter. Tissue/cellular lining with absorbing receptors is modeled as a planar, infinite wall that acts as a backscatter transmitter. The wall influences the concentration of molecules in the extracellular environment, which is then finally detected by the nearby spherical absorbing receivers. Detected output at the spherical receivers confers information about the health and existence of receptors. Assuming channel memory, the performance of the communication scheme is evaluated by using a) the Viterbi algorithm and b) spatial diversity schemes utilizing majority decision and equal gain combining decision rules. The proposed scheme has diagnostic applications and can be used to detect a disease.
