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Metasurface: Unlocking the future of wireless sensing and communication

The image features three white icons on a gradient background transitioning from blue on the left to green on the right. The first icon, located on the left, represents a Wi-Fi signal with curved lines radiating from a central point. The middle icon depicts a satellite with solar panels and an antenna emitting waves. The third icon, on the right, shows a bar chart with ascending bars indicating signal strength.

As the demand for faster, more reliable wireless communication continues to grow, but traditional systems face limitations in efficiency and adaptability. To keep up with evolving needs, researchers are investigating new ways to manipulate electromagnetic waves to improve wireless performance. 

To address these challenges, researchers are exploring new approaches, including metasurfaces—engineered materials that can control wave propagation in unprecedented ways. By dynamically shaping and directing electromagnetic waves, metasurfaces offer a promising path to overcoming the constraints of conventional wireless systems. 

Building on these capabilities, we are developing metasurfaces for a wide range of wireless applications, such as enhancing Low Earth Orbit satellite communication, optimizing acoustic sensing, and enabling acoustic and millimeter-wave technologies for 5G and 6G communication systems with commercial devices. More recently, our work has focused on enabling indoor access to the Global Navigation Satellite System (GNSS), improving millimeter-wave coverage in targeted environments, optimizing heat distribution in microwave ovens, and providing directional sound projection without headphones.

These advances, published at leading networking conferences—including MobiCom 2023 and 2024, MobiSys 2024 and 2025, and NSDI 2023—highlight metasurfaces’ potential in wireless communication and sensing. This post explores some of these applications in more detail. 

Microsoft research podcast

NeurIPS 2024: The co-evolution of AI and systems with Lidong Zhou

Just after his NeurIPS 2024 keynote on the co-evolution of systems and AI, Microsoft CVP Lidong Zhou joins the podcast to discuss how rapidly advancing AI impacts the systems supporting it and the opportunities to use AI to enhance systems engineering itself.


While GNSS is widely used for outdoor positioning and navigation, its indoor performance is often hindered by signal blockage, reflection, and attenuation caused by physical obstacles. Additional technologies like Wi-Fi and Bluetooth Low Energy (BLE) are often employed to address these issues. However, these solutions require extra infrastructure, are costly, and are complicated to deploy. Accurate positioning also typically depends on specialized hardware and software on mobile devices. 

Despite these challenges, GNSS signals hold promise for accurate indoor positioning. By leveraging the vast number of available satellites, GNSS-based solutions eliminate the need for base station deployment and maintenance required by Wi-Fi and BLE systems. This approach also allows seamless integration between indoor and outdoor environments, supporting continuous positioning in scenarios like guiding smart vehicles through indoor and outdoor industrial environments. 

To explore this potential, we conducted indoor measurements and found that GNSS satellite signals can penetrate windows at different angles and reflect or diffract from surfaces like floors and ceilings, resulting in uneven signals. Metasurfaces can control structured arrays of electromagnetic signals, allowing them to capture and redirect more GNSS signals. This allows signals to enter buildings in a path parallel to the ground, achieving broader coverage. Using this capability, we developed a GNSS positioning metasurface system (GPMS) based on passive metasurface technology.

One limitation of passive metasurfaces is their lack of programmability. To overcome this and enable them to effectively guide signals from different angles and scatter them in parallel, we designed a two-layer metasurface system. As shown in Figure 1, this design ensures that electromagnetic waves from different angles follow similar emission trajectories.  

A diagram showing the optimization of metasurfaces for enhancing GNSS signals indoors. It includes two GNSS satellites, far-field channels, a near-field channel matrix, a passive metasurface grid, and colorful 3D waveforms. The target radiation matrix is shown with indoor users. The text reads: “Optimization problem: The radiation output of our designed metasurfaces should all be close to the target radiation for GNSS signal input at all incidence angles.”
Figure 1: The GPMS two-layer metasurface structure

To improve positioning accuracy, we developed new algorithms that allow signals to pass through metasurfaces, using them as anchor points. Traditional GPS positioning requires signals from at least four satellites to decode location information. In the GPMS system, illustrated in Figure 2, each deployed metasurface functions as a virtual satellite. By deploying at least three metasurfaces indoors, we achieved high-precision positioning through a triangulation algorithm.

The image depicts a shopping mall indoor environment with three metasurfaces labeled Metasurface 1, Metasurface 2, and Metasurface 3. Each metasurface is associated with a steering and scattering area, labeled Steering and scattering area 1, Steering and scattering area 2, and Steering and scattering area 3 respectively. GNSS satellites are shown outside the building. The image illustrates how GNSS signals interact with metasurfaces within an indoor environment.
Figure 2. Diagram of the GPMS system. Passive metasurfaces guide GNSS signals indoors, while enhanced positioning algorithms provide precise indoor positioning on mobile devices. 

To evaluate the system, we deployed the GPMS with six metasurfaces on a 10×50-meter office floor and a 15×20-meter conference hall. The results show significant improvements in signal quality and availability. C/N₀, a measure of signal-to-noise ratio, increased from 9.1 dB-Hz to 32.2 dB-Hz. The number of visible satellites increased from 3.6 to 21.5. Finally, the absolute positioning error decreased from 30.6 meters to 3.2 meters in the office and from 11.2 meters to 2.7 meters in the conference hall. These findings are promising and highlight the feasibility and advantages of GNSS-based metasurfaces for indoor positioning. 

Millimeter waves enable the high-speed, low-latency performance needed for 5G and 6G communication systems. While commercial products like 60 GHz Wi-Fi routers and mobile devices are becoming popular, their limited coverage and susceptibility to signal obstruction restrict their widespread application. 

Traditional solutions include deploying multiple millimeter-wave access points, such as routers or base stations, or placing reflective metal panels in room corners to reflect electromagnetic waves. However, these approaches are both costly and offer limited performance. Metasurfaces offer a promising alternative for improving millimeter-wave applications. Previous research has shown that programmable metasurfaces can enhance signal coverage in blind spots and significantly improve signal quality and efficiency.  

To maximize the benefits of metasurfaces, we developed the AutoMS automation service framework, shown in Figure 3. This proposed framework can optimize millimeter-wave coverage using low-cost passive metasurface design and strategic placement. 

The three main components of AutoMS can address the limitations of traditional solutions: 

  1. Automated joint optimization: AutoMS determines the optimal network deployment configuration by analyzing phase settings, metasurface placement, and access point positioning. It also refines beam-forming configurations to enhance signal coverage. By iteratively identifying and optimizing the number, size, and placement of metasurfaces, AutoMS adjusts the metasurface phase settings and the access point’s configurations to achieve optimal signal coverage. 
A flowchart diagram illustrating the AutoMS framework, which generates optimized passive metasurface and access point deployment plans for a specific 3D model based on environmental scanning results. The process starts with an environment scan, producing a 3D model and reflection coefficients. This information feeds into wireless channel modeling, which along with deployment configurations, is optimized by a hyper-configuration tuner. The output includes phase maps used by the surface and AP optimizer. The optimized deployment configurations are then used for metasurface fabrication and network deployment.
Figure 3. The AutoMS framework generates optimized deployment plans for passive metasurface and access points based on environment scanning results. 
  1. Fast 3D ray tracing simulator: Using hardware and software acceleration, our simulator efficiently calculates channel matrices resulting from metasurfaces with tens of thousands of elements. This simulator, capable of tracing 1.3 billion rays in just three minutes on an A100 GPU, significantly accelerates calculations for complex environments.
  1. Low-cost passive metasurface design: We designed a high-reflectivity passive metasurface with near-2π phase control and broadband compatibility for the millimeter-wave frequency band. This metasurface is compatible with low-precision, cost-effective thermoforming processes. This process enables users to create metasurfaces at minimal cost, significantly reducing deployment expenses.

    Shown in Figure 4, users can capture the environment using existing 3D scanning apps on mobile devices, generate a 3D layout model, and upload it to the cloud. AutoMS then generates metasurface settings and placement guidelines.  

    Users can print metasurface patterns using hot stamping and customize them without affecting functionality, as millimeter waves penetrate paint and paper. 

A step-by-step process for creating low-cost passive metasurfaces. Step 1: Print patterns on paper with a laser printer. Step 2: Hot stamp aluminum foil on paper with a laminator. Step 3: Tear the aluminum foil off to get the metallic patterns. Step 4: Paste patterns on the plastic sheet and aluminum board.
Figure 4: The low-cost passive metasurface creation process 

Evaluation using publicly available 3D layout datasets and real-world tests shows that AutoMS significantly improves millimeter-wave coverage across various scenarios. Compared to a single router setup, AutoMS increased signal strength by 12.1 dB. Onsite tests further confirmed gains of 11 dB in target areas and over 20 dB in blind spots, with signal throughput increasing from 77 Mbps to 373 Mbps. AutoMS adapts to diverse environments, ensuring reliable and flexible deployment in real-world applications. 

Microwave ovens often heat unevenly, creating cold spots in food. These can allow harmful bacteria and other pathogens to survive, increasing the risk of foodborne illnesses. Uneven heating can cause eggs to burst or create “hot spots” that can scald.

Uneven heating is due to the appliance’s heating mechanism. Microwave ovens generate high-power radio frequency (RF) electromagnetic waves through dielectric heating. These waves create nodes with zero amplitude, which prevents heating. They also create antinodes, where heating occurs more rapidly.  

To address this issue, we developed MicroSurf, a low-cost solution that improves heating by using passive metasurfaces to control electromagnetic energy inside the microwave oven. It uses the resonance effect between the metasurface and electromagnetic waves to modify the standing-wave distribution and achieve more uniform heating. This is shown in Figure 5. 

A diagram illustrating the working principle of MicroSurf in four parts. A shows an uneven electric field distribution inside a microwave oven leading to uneven heating, with images of a microwave and thermal images of food. B depicts accurate modeling of the microwave oven, including geometry refinement, dielectric factor tuning, and frequency tuning. C involves designing and optimizing a metasurface that can function in a high-power environment to change the standing wave distribution, with an image of a high-power phase-tuning metasurface. D demonstrates achieving uniform heating of different foods and selectively heating specific parts of food, with thermal images showing uniform heating results.
Figure 5: MicroSurf’s working principle: Uneven electric field distribution inside the microwave oven leads to uneven heating. B. Modeling the microwave oven. C. Designing and optimizing a metasurface that can function in a high-power environment to change the standing wave distribution. D. Achieving uniform heating of different foods and selectively heating specific parts. 

Tests across four different microwave oven brands demonstrate that MicroSurf effectively optimizes heating for various liquids and solids, uniformly heating water, milk, bread, and meat. It concentrates heat on specific areas and adapts to differently shaped foods. MicroSurf offers a promising solution for even heating in microwave ovens, demonstrating the potential of metasurface technology in everyday applications. This innovation paves the way for smarter, more efficient home appliances.  

Advancing wireless innovation

Wireless sensing and communication technologies are evolving rapidly, driving innovation across a wide range of applications. We are continuing to push the boundaries of these technologies—particularly in metasurface development—while working to create practical solutions for a variety of use cases. 



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