Next-Generation Wearable Antennas for WBANs: A Comprehensive Survey

2025-10-16T08:13:47+00:00

Wearable antennas have emerged as a critical enabler of Wireless Body Area Networks (WBANs), offering seamless connectivity for applications in healthcare, sports, defense, and consumer electronics. Unlike conventional rigid antennas, wearable antennas must be lightweight, flexible, low-profile, and easily integrated into textiles or other soft substrates. These stringent requirements introduce unique design challenges, such as detuning effects due to proximity with the human body, performance degradation under bending and stretching, and compliance with Specific Absorption Rate (SAR) limits to ensure user safety. This study provides a comprehensive review of recent advances in wearable antenna research, covering fundamental design principles, major challenges, and performance optimization techniques. Special attention is given to emerging innovations, including metamaterials, Electromagnetic Band-Gap (EBG) structures, Artificial Magnetic Conductors (AMCs), and reconfigurable architectures. Furthermore, advanced fabrication methods, such as embroidery, inkjet printing, and 3D printing, are examined for their role in enabling the production of practical, scalable, and cost-effective antennas. Finally, future research directions are discussed, with emphasis on promising developments such as energy-harvesting antennas, AI-assisted antenna design, and integration into next-generation communication paradigms, including 6G networks and Reconfigurable Intelligent Surfaces (RIS).

AI-Optimized Reconfigurable Antennas for 6G Communication Systems

2025-08-30T06:50:43+00:00

The advent of sixth-generation (6G) wireless networks marks a paradigm shift toward ultra-high data rates, seamless connectivity, and near-zero latency. Achieving these targets requires efficient operation in millimeter-wave (mmWave) and terahertz (THz) bands, where severe propagation losses, spectrum scarcity, and hardware constraints impose unprecedented challenges on antenna systems. Reconfigurable antennas (RAs) have emerged as a pivotal solution, offering the ability to dynamically adapt frequency, polarization, and radiation characteristics to ensure robust performance in highly variable environments. In parallel, artificial intelligence (AI) and machine learning (ML) are playing a transformative role in antenna design, optimization, and control. By leveraging algorithms such as deep reinforcement learning and convolutional neural networks, AI enables autonomous beam steering, adaptive resource allocation, and fault detection, and predictive optimization capabilities essential for 6G adaptability. Integrating AI with RA architectures enhances spectral efficiency, energy utilization, and network resilience, while reducing hardware redundancy. Recent advances demonstrate significant performance gains, including improved impedance matching, expanded bandwidth, and real-time beam reconfiguration. This synergy between AI and reconfigurable antenna technology not only addresses the stringent requirements of 6G communication systems but also lays the foundation for scalable, intelligent, and self-optimizing networks. Ultimately, AI-optimized RAs represent a transformative pathway for ensuring efficiency, reliability, and sustainability in next-generation wireless infrastructures.

Journal of RF and Microwave Communication Technologies

Next-Generation Wearable Antennas for WBANs: A Comprehensive Survey

AI-Optimized Reconfigurable Antennas for 6G Communication Systems