This paper presents CARTS, an adaptive 5G uplink sensing scheduling scheme designed to provide Integrated Communication and Localization services. The performance of both communication and localization fundamentally depends on the availability of accurate and up-to-date channel state information (CSI). In modern 5G networks, uplink CSI is derived from two reference signals: the demodulation reference signal (DMRS) and the sounding reference signal (SRS). However, current base station implementations treat these CSI measurements as separate information streams. The key innovation of CARTS is to fuse these two CSI streams to increase the frequency of CSI updates and to extend sensing opportunities to more users. CARTS addresses two key challenges: (i) a novel channel stitching and compensation method that integrates asynchronous CSI estimates from DMRS and SRS, despite their different time and frequency allocations, and (ii) a real-time SRS triggering algorithm that complements the inherently uncontrollable DMRS schedule, ensuring sufficient and non-redundant sensing opportunities for all users. Our trace-driven evaluation shows that CARTS significantly improves scalability, achieving a channel estimation error (NMSE) of 0.167 and UE tracking accuracy of 85 cm while supporting twice the number of users as a periodic SRS-only baseline with similar performance. By opportunistically combining DMRS and SRS, CARTS therefore provides a practical, standard-compliant solution to improve CSI availability for localization and communication without requiring additional radio resources.

The accuracy of traditional localization methods significantly degrades when the direct path between the wireless transmitter and the target is blocked or non-penetrable. This paper proposesN LoS, a novel approach for precise non-line-of-sight (NLoS) localization using a single mmWave radar and a backscatter tag.N LoSleverages multipath reflections from both the tag and surrounding reflectors to accurately estimate the target's position.N LoSintroduces several key innovations. First, we designHFD(Hybrid Frequency-Hopping and Direct Sequence Spread Spectrum) to detect and differentiate reflectors from the target. Second, we enhance signal-to-noise ratio (SNR) by exploiting the correlation properties of the designed signals, improving detection robustness in complex environments. Third, we proposeFS-MUSIC(Frequency-Spatial Multiple Signal Classification), a super-resolution algorithm that extends the traditional MUSIC method by constructing a higher-rank signal matrix, enabling the resolution of additional multipath components. We evaluateN LoSusing a 24 GHz mmWave radar with 250 MHz bandwidth in three diverse environments: a laboratory, an office, and an around-the-corner corridor. Experimental results demonstrate thatN LoSachieves median localization errors of10.69 cm (X)and11.98 cm (Y)at a 5 m range in the laboratory setting, showcasing its effectiveness for real-world NLoS localization.

This poster presents CARTS, an adaptive 5G uplink sensing scheme that jointly uses the estimated CSI from both data channel reference signal (DMRS) and channel sounding reference signal (SRS) to improve the UE sensing capacity with minimal degradation in communication performance. In order to efficiently combine the estimated CSIs from these two reference signals, CARTS features a real-time SRS triggering algorithm to complement the channel estimations from the DMRS. Besides, to address asynchornization issues caused by DMRS and SRS, which are sampled at different time and frequency bands, CARTS applies a new channel stitching and compensation method.

Intelligent Reflective Surfaces (IRS) are an emerging research focus aimed at enhancing non-line-of-sight wireless communications by manipulating radio reflections. However, when embedded within objects, IRS may disrupt mmWave radar object classification by altering reflected features. In this study, we explore the adverse effects of a misconfigured IRS on radar classification. We prototyped an IRS with configurations that can either induce destructive interference with the object's reflected signals or deflect these reflections away from the radar using beamforming techniques. Experiments using a 24 GHz radar to detect four everyday objects revealed a significant drop in classification accuracy due to this interference. These findings underscore a significant vulnerability in the increasingly pervasive deployment of mmWave radar for object classification, highlighting the urgent need for robust countermeasures.

Plant sensing plays an important role in modern smart agriculture and the farming industry. Remote radio sensing allows for monitoring essential indicators of plant health, such as leaf water content (WC). While recent studies have shown the potential of using millimeter-wave (mmWave) radar for plant sensing, many overlook crucial factors, such as leaf structure and surface roughness, which can impact the accuracy of the measurements. In this article, we introduce Leafeon, which leverages mmWave radar to measure leaf WC noninvasively. Utilizing electronic beam steering, multiple leaf perspectives are sent to a custom deep neural network, which discerns unique reflection patterns from subtle antenna variations, ensuring accurate and robust leaf WC estimations. We implement a prototype of Leafeon using a Commercial Off-The-Shelf mmWave radar and evaluate its performance with a variety of different leaf types. Leafeon was trained in-lab using high-resolution destructive leaf measurements, achieving a mean absolute error (MAE) of leaf WC as low as 3.17% for the Avocado leaf, significantly outperforming the state-of-the-art approaches with an MAE reduction of up to 55.7%. Furthermore, we conducted experiments on live plants in both indoor and glasshouse experimental farm environments. Our results showed a strong correlation between predicted leaf WC levels and drought events.

The accuracy of the current localization methods degrades significantly when the direct path between the wireless transmitter and the target is blocked. This paper considers the problem of using a single mmWave radar and a tag to facilitate localization in the non-penetrable non-line-of-sight (NLoS) scenario. We present mN2LoS (short for mmWave based Non-penetrable NLoS LOCalization), which accurately localizes the tag by using the multipath reflections. mN2LoS has a few novel features. First, we design HTRD for detecting reflectors and surroundings while distinguishing them from the tag, using Hybrid utilization of Tag localization code and Reflector localization code based on Direct sequence spread spectrum techniques. Second, we enhance the signal-to-noise ratio by exploiting the correlation features of the designed signal. Evaluation results demonstrate that the developed mN2LoS can achieve median errors (at 5m range) of 12.9cm and 3.8° for distance and AOA estimations for the office configuration, respectively.

Non-line-of-sight (NLoS) sensing represents a significant advancement in sensor technology. Unlike traditional sensing methods that rely on direct line-of-sight, NLoS sensing allows for the detection and localization of objects obscured from the sensor's view. In this paper, we introduce mmMirror, a novel Van Atta Array based millimetre-wave (mmWave) reconfigurable intelligent reflecting surface (IRS) that provides: (i) NLoS localization at a range of approximately 3 meters, (ii) seamless communication between radar and IRS using existing frequency-modulated continuous-wave (FMCW) signals, and (iii) support for multiple targets. The mmMirror system is implemented on commodity 24 GHz radars, and the IRS is prototyped on printed circuit boards (PCBs).