With the rapid growth of IoT and AIoT applications, the demand for customized hardware is surging, yet PCB design for IoT devices remains a heavily manual, GUI-centric process that requires significant expertise in circuit and tools. This creates a widening gap between the accelerated software development and the difficulty of realizing corresponding hardware, making PCB-based hardware a critical bottleneck for system innovation. We present IoTGen, an LLM-driven agentic system that generates IoT PCB designs directly from natural-language demands. IoTGen introduces semantic-rich programming abstractions that capture schematic construction, enabling a domain-specialized language model to synthesize schematics with code. It further integrates a semantic component retrieval algorithm that combines LLM understanding with vector-based search, and an LLM-guided hybrid layout procedure that coordinates auto-routing tools to produce PCB layouts suitable for fabrication. We evaluate IoTGen on a dataset of diverse IoT designs, achieving a high component matching accuracy of 90%, a schematic generation success rate of 82%, and significant layout improvement. Case studies further demonstrate its capability to generate IoT PCB designs while substantially reducing the human effort and domain expertise required for hardware development. We release IoTGen to facilitate further research.

RF hardware design is a complicated, time-consuming, and expertise-bound process, which constrains the development and adoption of hardware innovation. Manual workflows do not scale to emerging wireless applications, while existing design generation strategies, e.g., learning-based approaches, lack training efficiency and generalizability across hardware types, frequency bands, operation modes, and substrate materials. In this paper, we present AutoRF, the first agentic framework for automated RF hardware design, supporting metasurfaces and antennas. It allows users to specify their demands and generate corresponding designs. We introduce extensible design abstractions to enable a modular framework. The core of the framework is an efficient and generalizable algorithm for design search and optimization, which utilizes LLMs to drive both circuit model simulator and EM simulator. To boost reliability and performance, we propose custom programming interfaces and a rule reviewer agent as feedback sources to train a specialized LLM. Evaluation demonstrates high success rate and significant optimization speedup; case studies with fabricated metasurface and antennas, ranging from 2.4 GHz to sub-THz, illustrate an ability to derive novel designs for next-generation wireless infrastructure.

Non-invasive continuous glucose monitoring for diabetes patients remains challenging despite ongoing interest. This paper presents CGMM, a novel non-invasive wireless glucose monitoring system integrated into wearable devices. It features a specially designed metasurface that couples with the wearable's antenna and tissue fluid beneath the skin, amplifying frequency response changes caused by subtle glucose concentration variations. To address individual tissue variability and optimize the passive metasurface design, we develop a tunable metasurface and a one-shot calibration method to obtain the impedance for optimal resonance in glucose sensing environments with unknown parameters. The calibrated impedance is then used for the inverse design and fabrication of an economical passive metasurface. We implement prototypes of CGMM and conduct extensive experimental evaluations. In human experiments involving ten participants using the prototype with LibreVNA, the overall performance is quantified with relative errors ranging from -5.02% to 6.93% and an RMSE of 9.65 mg/dL.

With the growing popularity of VR and AR devices, eye tracking has become a critical user interface and input modality for on-device AI agents. However, a compact, power-efficient, and robust eye tracking solution for AR/smart glasses remains an unsolved challenge. In this paper, we present mmET, the first mmWave radar-based eye tracking system on glasses. Our system, implemented as a pair of prototype glasses, utilizes sub-1cm mmWave radars placed near the eyes. The radars transmit FMCW signals and capture the reflections from the eyes and surrounding skin as the system input. To refine gaze estimation accuracy and data efficiency, we propose several novel methods: (1) concatenating multiple chirps and beamforming with learnable weights to improve resolution, (2) a novel neural network architecture to enhance robustness against remounting, (3) pretraining with contrastive loss to enable fast adaptation for new users. Experiments with 16 participants show that mmET achieves an average angular gaze direction error of 1.49° within sessions and 4.47° across remounting sessions, and reduces the training data needed for new users by 80% using the pretrained model.

Emerging wireless networks increasingly venture beyond over-the-air communication, such as deep-tissue networking for medical sensors, air-water communication for oceanography, and soil sensing for agriculture. They face the fundamental challenge of significant reflection and power loss at medium interfaces. We present RF-Mediator, a transflective metasurface placed near a medium interface to program the behavior of the physical boundary. Our hardware design comprises a single layer of varactor-based surface elements with specific metallic patterns and wiring. With the biasing voltage tuned element-wise, the surface dynamically mediates between the adjacent media to control and utilize both signal reflection and transmission, enhancing connectivity for wireless endpoints on both sides. A multi-stage control algorithm efficiently determines the surface configuration to handle dynamic adaptation needs for joint medium impedance matching and beamforming towards multiple endpoints simultaneously. We implement a lightweight and flexible metasurface prototype and experiment with diverse cross-medium setups. Extensive evaluation shows that RF-Mediator provides a median power gain of 8 dB for air-tissue links, reaching up to 30 dB gain for backscatter links, and up to 18 dB for multiple links concurrently.

Programmable radio environments with metasurfaces introduce signal-level programmability to wireless networks, providing various services such as connectivity enhancement, coverage extension, sensing, security protection, and wireless powering. Next-generation wireless networks are set to widely deploy metasurfaces. However, the current one-system-per-use-case approach cannot scale with wide-ranging hardware designs and surface-aided applications. This paper presents a vision, SurfOS, a metasurface operating system for programmable radio environments. SurfOS aims to orchestrate heterogeneous surface hardware and provide diverse services for user-level applications. We discuss the challenges of building such a system, potential abstraction layers, and open research problems. Our early-stage implementation demonstrates the feasibility and benefits of this approach.

Emerging wireless IoT applications increasingly venture beyond over-the-air communication, such as deep-tissue networking for medical sensors, air-water communication for oceanography, and soil sensing for agriculture. These applications face the fundamental challenge of significant reflection and power loss at medium interfaces. We present RF-Mediator, a programmable metasurface placed near a medium interface to mask the presence of a physical boundary. Our hardware design comprises a single layer of varactor-based surface elements with specific metallic patterns and wiring. With the biasing voltage tuned element-wise, the surface dynamically mediates between the adjacent media to minimize unwanted reflection and boost transmission through the medium interface. A multi-stage control algorithm efficiently determines the surface configuration to handle all dynamic adaptation needs for medium impedance matching and beamforming jointly. We implement a lightweight and flexible metasurface prototype and experiment with diverse cross-medium setups. Extensive evaluation shows that RF-Mediator provides a median power gain of 8 dB for air-tissue links and up to 30 dB for cross-medium backscatter links.

mmWave networks offer wide bandwidth for high-speed wireless communication but suffer from limited range and susceptibility to blockage. Existing coverage provisioning solutions not only incur high costs but also require significant expert knowledge and manual efforts. In this paper, we present AutoMS, an automated service framework to optimize mmWave coverage by strategically designing and placing low-cost passive metasurfaces. Our approach consists of three key components: (1) joint optimization of metasurface phase configurations and placement as well as access point beamforming codebooks. (2) a fast 3D ray-tracing simulator for accelerated large-scale metasurface channel modeling. (3) a metasurface design amenable to ultra-low-cost hot stamping fabrication, featuring high reflectivity, near 2π phase control, and wideband support. Simulation and testbed experiments show that AutoMS can increase the median received signal strength by 11 dB in target rooms and over 20 dB at previous blind spots, and improve the median throughput by over 3× in real-world scenarios.

Global Navigation Satellite System (GNSS) is extensively utilized for outdoor positioning and navigation. However, achieving high-precision indoor positioning is challenging due to the significant attenuation of GNSS signals indoors. To address this issue, we propose an innovative indoor GNSS positioning system called GPMS, which uses passive metasurface technology to redirect GNSS signals from outdoors into indoor spaces. These passive metasurfaces are strategically optimized for indoor coverage by steering and scattering the GNSS signals across a wide range of incident angles. We further develop a novel localization algorithm that can determine which metasurface the signal goes through and localize the user using the set of metasurfaces as anchor points. A distinct advantage of our localization algorithm is that it can be implemented on existing mobile devices without any hardware modifications. We implement the prototype of GPMS, and deploy six metasurfaces in two indoor environments, a 10×50 m2 office floor and a 15×20 m2 lecture room, to evaluate system performance. In terms of coverage, our GPMS increases the C/N0 from 9.1 dB-Hz to 23.2 dB-Hz and increases the number of visible satellites from 3.6 to 21.5 in the office floor. In terms of indoor positioning accuracy, our proposed system decreases the absolute positioning error from 30.6 m to 3.2 m in the office floor, and from 11.2 m to 2.7 m in the lecture room, demonstrating the feasibility and benefits of metasurface-assisted GNSS for indoor positioning.

With new frequency bands opening up, emerging wireless IoT devices are capitalizing on an increasingly divergent range of frequencies. However, existing coverage provisioning practice is often tied to specific standards and frequencies. There is little shareable wireless infrastructure for concurrent links on different frequencies, across networks and standards.

6G is on the horizon. A key paradigm being proposed for 6G involves a shift from device-centric to user-centric services, i.e., multiple devices collaborate to serve user demands. This paradigm shift and the potential applications require handling wireless signals from multiple sources collectively in a 3D space and potentially at close proximity to the end user. Conventional approaches of optimizing individual communication endpoints are ill-suited to collaborative 3D signal shaping. Recent smart surface proposals to program the radio environments appear a better fit, but existing designs implicitly require planar, rigid substrates. Optimizing 3D coverage would require very large surface implementations and incur many scalability and deployment challenges. In this paper, therefore, we propose a notion of metamorphic smart surfaces, i.e., shape-changing smart surfaces to cater to complex 3D propagation environments. Based on early explorations with HFSS simulations and two simple prototypes, we discuss the pros and cons of metamorphic surfaces and potential future directions.