The sub-THz band offers an attractive solution to future wireless networks, thanks to its ultra-low latency as well as its large available bandwidth. However, link blockage remains a major setback towards reliable sub-THz end-to-end communication systems, due to narrow beamwidth and inherently high penetration losses. To achieve blockage mitigation in sub-THz communication, this paper takes advantage of unique near-field properties and manipulates curved wavefront trajectories. Unfortunately, finding the best curved beam configuration is non-trivial due to the lack of a closed-form equation for received power calculation under blockage scenarios, even if the wireless environment is precisely known. To address this, we present a physics-informed learning-based framework that optimizes the phase profile of the transmitting array, such that the resulting wavefront could curve around obstacles and adapt to dynamic environments in real time. Through extensive near-field simulations, we evaluate the performance of our AI-generated curved beams as opposed to optimal Airy beams achieved via impractical exhaustive scans with prohibitively large time and complexity overheads. Importantly, simulated results show that our AI-generated curved wavefront provides an average SNR gain of 19.83 dB compared with conventional beam steering and 2.13 dB compared with near-field beam focusing, across ~400 random and independent test scenarios.

We present Eye-Beam, a programmable platform for integrated communication and sensing. Eye-Beam leverages the hardware and processing required for standard millimeter-wave (mmWave) 5G directional communications to enable sensing functions. Specifically, our platform (1) receives and synchronizes to the data frame of broadcast 5G signals, (2) extracts directional communication features, creating a tensor of spatial information, and (3) utilizes this data as input to a DNN that infers the presence of specific objects in the propagation environment. Eye-Beam includes a programmable 28 GHz 64-element phased array, an SDR, and custom FPGA-based firmware. Eye-Beam’s key capabilities and metrics include (i) synchronization of I/Q data (up to 200 MSPS) with beam steering (among 9,601 beams) with 10 ns accuracy; (ii) a signal processing pipeline that extracts communication features such as the SNR and channel response from received 5G waveforms; and (iii) system orchestration that synchronizes the receiver (RX) to the 5G frame structure of the base station (gNodeB) and maintains it within a worst-case OFDM cyclic prefix of$0.29~\mu $s. Eye-Beam is also able to emulate gNodeB transmissions. We demonstrate Eye-Beam’s performance by showcasing its communication capability (decoding up to 64-QAM), as well as its performance as a channel sounder (extracting detailed directional 5G features in 2,401 beam directions within just 20 ms). We then, for the first time, demonstrate AI-based object classification only using the directional communication features derived by Eye-Beam from ambient mmWave 5G signals transmitted by a gNodeB. Six object classes, including 4 distinct objects concealed in a backpack, are classified with 98% accuracy in an indoor environment.

The ability to assess the quality of fruit and vegetables at scale can revolutionize the agriculture sector and significantly reduce food waste. In this paper, we present AgriTera, a novel solution for accurate non-invasive, and contract-free fruit ripeness sensing via sub-terahertz wireless signals. The key idea is that sugar and water concentrations in fruit (that are associated with fruit ripening) leave unique non-uniform footprints in the wide band spectrum of the reflected signal off of the fruits. AgriTera utilizes the sub-THz bands for its wide bandwidth, sensitivity to water, mm-scale penetration depth, and non-ionizing features that offer high-resolution inferences from the peel as well as the pulp underneath the peel. We develop a chemometric model that translates the reflection spectra to well-known ripeness metrics, namely Dry Matter and Brix. We conduct extensive over-the-air experiments with commercially available sub-THz transceivers. We compare our results with ground truth values captured by a specialized quality sensor and a vision-based scheme that infers ripeness based on changes in the appearance of the fruit. We demonstrate that AgriTera can accurately estimate Brix and Dry Matter in three different types of fruit with an average Normalized RMSE value of 0.55%, an error that yields a negligible impact on taste and is imperceivable by the consumer.

Food waste is one of the greatest economic and ethical challenges in the world. Empowering consumers with the ability to assess the quality of fruit can be a game changer for reducing waste and motivating a healthier diet. In this paper, we present Meta-Sticker a novel low-cost non-invasive solution for accurate fruit sensing via a sub-THz metamaterial sticker. The key idea is to exploit fruit as a substrate for resonating meta-atoms. Changes in the chemical composite of the fruit over time (e.g., sugar and water concentration) yield variation in the dielectric properties of the fruit (here substrate). Interestingly, this would affect the resonant frequency of the Meta-Sticker. We design Meta-Sticker to resonate in the sub-THz bands for their sensitivity to water, mm-scale penetration depth, and non-ionizing features that offer high-resolution inferences from the inner pulp of the fruit. We develop a model that translates the resonance of Meta-Sticker to well-known ripeness metrics, namely Dry Matter and Brix. We fabricate Meta-Sticker on paper and conduct extensive over-the-air experiments. We demonstrate that our system can estimate Brix and Dry Matter in three different types of fruits with an average Normalized RMSE value of 1.24%, an error that yields a negligible impact on taste and is imperceptible by the consumer. Our design is non-invasive, low-cost (less than a cent), passive, biodegradable, and conformal.

The spectrum above 100 GHz has the potential to enable accurate 3D wireless localization due to the large swath of available spectrum. Yet, existing wide-band localization systems utilize the time of arrival measurements requiring strict time synchronization. In this paper, we present 123-LOC, a novel non-coherent system for one-shot dual-polarized 3D localization above 100 GHz. Our key idea is to create unique asymmetric THz fingerprints in 3D so that a wireless node can jointly infer its angular position and distance by taking hints from the measured power-spectrum profile. We introduce a dual-polarized dual-slit waveguide structure that emits out signals into free-space with a key feature that the beam pattern depends on the frequency of the signal and the geometry of the slit. To distinguish the emissions from the two slits, we use polarization diversity and manipulate the aperture geometry of the two slits so that they transmit slightly different angular-spectral signatures. Our over-the-air experiments demonstrate that 123-LOC achieves an average angle estimation error of 1° together with millimeter-scale ranging resolution, solely through non-coherent power measurements.