In this abstract, we introduce the concept of a programmable smart radio environment, which can be controlled and tuned by software to reconfigure itself in real time based on application needs. We design programmable smart surface systems and deploy them on buildings and vehicles to physically control radio environments. Our ideas are implemented as hardware-software systems, integrated into city-scale wireless testbeds alongside existing network protocols, and validated through rigorous experimental evaluation.

Tomorrow’s massive-scale Internet-of-Things (IoT) sensor networks are poised to drive uplink traffic demand, especially in areas of dense deployment. To meet this demand, however, network designers leverage tools that often require accurate estimates of Channel State Information (CSI), which incurs a high overhead and thus reduces network throughput. Furthermore, the overhead generally scales with the number of clients, and so is of special concern in such massive IoT sensor networks. While prior work has used transmissions over one frequency band to predict the channel of another frequency band on the same link, this paper takes the next step in the effort to reduce CSI overhead: predict the CSI of a nearby but distinct link. We proposeCross-Link Channel Prediction(CLCP), a technique that leverages multi-view representation learning to predict the channel response of a large number of users, thereby reducing channel estimation overhead further than previously possible. CLCP’s design is highly practical, exploiting existing transmissions rather than dedicated channel sounding or extra pilot signals. We have implemented CLCP for two different Wi-Fi versions, namely 802.11n and 802.11ax, the latter being the leading candidate for future IoT networks. We evaluate CLCP in two large-scale indoor scenarios involving both line-of-sight and non-line-of-sight transmissions with up to 144 different 802.11ax users. Moreover, we measure its performance with four different channel bandwidths, from 20 MHz up to 160 MHz. Our results show that CLCP provides a 2x throughput gain over baseline and a 30% throughput gain over existing prediction algorithms.

We present Wall-Street, a smart surface designed for vehicles to boost 5G mmWave connectivity for passengers. It improves mmWave connections in three ways: (1) it steers outdoor mmWave signals into the vehicle, ensuring all users have coverage; (2) it enables the vehicle to receive data from the current cell while measuring signals from potential handover cells, allowing smooth transitions without interrupting service; (3) during handovers, it combines/splits signals from/to both the current and new cells, creating a make-before-break connection. Our demonstration shows Wall-Street's versatile signal manipulation abilities. These include steering single beams, simultaneously reflecting and transmitting beams for neighboring cell measurements with concurrent communication, and combining or splitting beams for seamless cell transitions.

This paper presents Monolith, a high bitrate, low-power, metamaterials surface-based Orbital Angular Momentum (OAM) MIMO multiplexing design for rank deficient, free space wireless environments. Leveraging ambient signals as the source of power, Monolith backscatters these ambient signals by modulating them into several orthogonal beams, where each beam carries a unique OAM. We provide insights along the design aspects of a low-power and programmable metamaterials-based surface. Our results show that Monolith achieves an order of magnitude higher channel capacity than traditional spatial MIMO backscattering networks.

Tomorrow's massive-scale IoT sensor networks are poised to drive uplink traffic demand, especially in areas of dense deployment. To meet this demand, however, network designers leverage tools that often require accurate estimates of Channel State Information (CSI), which incurs a high overhead and thus reduces network throughput. Furthermore, the overhead generally scales with the number of clients, and so is of special concern in such massive IoT sensor networks. While prior work has used transmissions over one frequency band to predict the channel of another frequency band on the same link, this paper takes the next step in the effort to reduce CSI overhead: predict the CSI of a nearby but distinct link. We propose Cross-Link Channel Prediction (CLCP), a technique that leverages multi-view representation learning to predict the channel response of a large number of users, thereby reducing channel estimation overhead further than previously possible. CLCP's design is highly practical, exploiting existing transmissions rather than dedicated channel sounding or extra pilot signals. We have implemented CLCP for two different Wi-Fi versions, namely 802.11n and 802.11ax, the latter being the leading candidate for future IoT networks. We evaluate CLCP in two large-scale indoor scenarios involving both line-of-sight and non-line-of-sight transmissions with up to 144 different 802.11ax users and four different channel bandwidths, from 20 MHz up to 160 MHz. Our results show that CLCP provides a 2× throughput gain over baseline and a 30% throughput gain over existing prediction algorithms.

The first low earth orbit satellite networks for internet service have recently been deployed and are growing in size, yet will face deployment challenges in many practical circumstances of interest. This paper explores how a dual-band, electronically tunable smart surface can enable dynamic beam alignment between the satellite and mobile users, make service possible in urban canyons, and improve service in rural areas. Our design is the first of its kind to target dual channels in the Ku radio frequency band with a novel dual Huygens resonator design that leverages radio reciprocity to allow our surface to simultaneously steer energy in the satellite uplink and downlink directions, and in both reflective and transmissive modes of operation. Our surface, Wall-E, is designed and evaluated in an electromagnetic simulator and demonstrates 94% transmission efficiency and a 85% reflection efficiency, with at most 6 dB power loss at steering angles over a 150 degree field of view for both transmission and reflection. With 75cm2 surface, our link budget calculations predict 4 dB and 24 dB improvement in the SNR of a link entering the window of a rural home in comparison to the free-space path and brick wall penetration, respectively.

Biometric authentication offers advantages over current security practices. Unlike keys and tokens, biometrics are never lost or stolen. Unlike passwords, biometrics cannot be forgotten. However, existing biometric systems are with controversy: once divulged, they are compromised forever. To this end, this paper explores a truly cancelable brain-based biometric system for the first time. Specifically, we present a new psychophysiological protocol via non-volitional brain response for trustworthy user authentication, with an application example of smart headwear. More specifically, we address the following research challenges in a theoretical and experimental combined manner: (1) how to generate reliable brain responses with sophisticated visual stimuli; (2) how to acquire effective brain response and analyze unique features in them for authentication; and (3) how to reset and change brain biometrics when the current biometric credential is divulged. To evaluate the performance of the proposed system, we conducted a pilot study and achieved an f-score accuracy of 95.46 percent and equal error rate (EER) of 2.503 percent, thereby demonstrating the potential feasibility of neurofeedback based biometrics for smart headwear applications. Further, the cancelability study proves the effectiveness of the reset brain password. To the best of our knowledge, it is the first in-depth research study on truly cancelable brain biometrics.

In recent years, biometric techniques (e.g., fingerprint or iris) are increasingly integrated into mobile devices to offer security advantages over traditional practices (e.g., passwords and PINs) due to their ease of use in user authentication. However, existing biometric systems are with controversy: once divulged, they are compromised forever - no one can grow a new fingerprint or iris. This work explores a truly cancelable brain-based biometric system for mobile platforms (e.g., smart headwear). Specifically, we present a new psychophysiological protocol via non-volitional brain response for trustworthy mobile authentication, with an application example of smart headwear. Particularly, we address the following research challenges in mobile biometrics with a theoretical and empirical combined manner: (1) how to generate reliable brain responses with sophisticated visual stimuli; (2) how to acquire the distinct brain response and analyze unique features in the mobile platform; (3) how to reset and change brain biometrics when the current biometric credential is divulged. To evaluate the proposed solution, we conducted a pilot study and achieved an f -score accuracy of 95.46% and equal error rate (EER) of 2.503%, thereby demonstrating the potential feasibility of neurofeedback based biometrics for smart headwear. Furthermore, we perform the cancelability study and the longitudinal study, respectively, to show the effectiveness and usability of our new proposed mobile biometric system. To the best of our knowledge, it is the first in-depth research study on truly cancelable brain biometrics for secure mobile authentication.