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1. Over-the-air transmission of Zak-OTFS with spread pilots on the sub-THz communication testbed

   Parisi, C; Khammammetti, V; Calderbank, R; Huie, L (December 2025, IEEE)

   Looking towards 6G wireless systems, frequency bands like the sub-terahertz (sub-THz) band (100 GHz - 300 GHz) are gaining traction for their promises of large available swaths of bandwidth to support the ever-growing data demands. However, challenges with harsh channel conditions and hardware non-linearities in the sub-THz band require robust communication techniques with favorable properties, such as good spectral efficiency and low peak-to-average power ratio (PAPR). Recently, OTFS and its variants have garnered significant attention for their performance in severe conditions (like high delay and Doppler), making it a promising candidate for future communications. In this work, we implement Zak-OTFS for the over-the-air experiments with traditional point pilots and the new spread pilots. Notably, we design our spread pilot waveforms with communications and sensing coexisting in the same radio resources. We define the system model and the signal design for integration onto our state-of-the-art sub-THz wireless testbed. We show successful data transmission over-the-air at 140 GHz and 240 GHz in a variety of signal-to-noise ratio (SNR) conditions. In addition, we demonstrate integrated sensing and communications (ISAC) capabilities and show PAPR improvement of over 5 dB with spread pilots compared to point pilots. 

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2. A Deep Reinforcement Learning Approach for Integrated Automotive Radar Sensing and Communication

   https://doi.org/10.1109/SAM53842.2022.9827815  

   Xu, Lifan; Zheng, Ruxin; Sun, Shunqiao (June 2022, IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM))

   We present a deep reinforcement learning approach to design an automotive radar system with integrated sensing and communication. In the proposed system, sparse transmit arrays with quantized phase shifter are used to carry out transmit beamforming to enhance the performance of both radar sensing and communication. Through interaction with environment, the automotive radar learns a reward that reflects the difference between mainlobe peak and the peak sidelobe level in radar sensing mode or communication user feedback in communication mode, and intelligently adjust its beamforming vector. The Wolpertinger policy based action-critic network is introduced for beamforming vector learning, which solves the dimension curse due to huge beamforming action space. 

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3. RISC: Resource-Constrained Urban Sensing Task Scheduling Based on Commercial Fleets

   https://doi.org/10.1145/3397337  

   Xie, Xiaoyang; Fang, Zhihan; Wang, Yang; Zhang, Fan; Zhang, Desheng (June 2020, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   With the trend of vehicles becoming increasingly connected and potentially autonomous, vehicles are being equipped with rich sensing and communication devices. Various vehicular services based on shared real-time sensor data of vehicles from a fleet have been proposed to improve the urban efficiency, e.g., HD-live map, and traffic accident recovery. However, due to the high cost of data uploading (e.g., monthly fees for a cellular network), it would be impractical to make all well-equipped vehicles to upload real-time sensor data constantly. To better utilize these limited uploading resources and achieve an optimal road segment sensing coverage, we present a real-time sensing task scheduling framework, i.e., RISC, for Resource-Constraint modeling for urban sensing by scheduling sensing tasks of commercial vehicles with sensors based on the predictability of vehicles' mobility patterns. In particular, we utilize the commercial vehicles, including taxicabs, buses, and logistics trucks as mobile sensors to sense urban phenomena, e.g., traffic, by using the equipped vehicular sensors, e.g., dash-cam, lidar, automotive radar, etc. We implement RISC on a Chinese city Shenzhen with one-month real-world data from (i) a taxi fleet with 14 thousand vehicles; (ii) a bus fleet with 13 thousand vehicles; (iii) a truck fleet with 4 thousand vehicles. Further, we design an application, i.e., track suspect vehicles (e.g., hit-and-run vehicles), to evaluate the performance of RISC on the urban sensing aspect based on the data from a regular vehicle (i.e., personal car) fleet with 11 thousand vehicles. The evaluation results show that compared to the state-of-the-art solutions, we improved sensing coverage (i.e., the number of road segments covered by sensing vehicles) by 10% on average. 

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4. Exploitation of Symmetrical Non-Convexity for Symbol-Level DFRC Signal Design

   https://doi.org/10.1109/ICC51166.2024.10622712  

   Nguyen, Ly V; Liu, Rang; Swindlehurst, A Lee (June 2024, IEEE)

   Constructive interference exploited by symbol-level (SL) signal processing is a promising solution for addressing the inherent interference problem in dual-functional radar-communication (DFRC) signal designs. This paper considers an SL-DFRC signal design problem which maximizes the radar performance under communication performance constraints. We exploit the symmetrical non-convexity property of the communication-independent radar sensing metric to develop low- complexity yet efficient algorithms. We first propose a radar-to- DFRC (R2DFRC) algorithm that relies on the non-convexity of the radar sensing metric to find a set of radar-only solutions. Based on these solutions, we further exploit the symmetrical property of the radar sensing metric to efficiently design the DFRC signal. Since the radar sensing metric is independent of the communication channel and data symbols, the set of radar-only solutions can be constructed offline, therefore reducing the computational complexity. We then develop an accelerated R2DFRC algorithm that further reduces the complexity. Finally, we demonstrate the superiority of the proposed algorithms compared to existing methods in terms of both radar sensing and communication performance as well as computational complexity. 

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5. Design of a Cyberattack Resilient 77 GHz Automotive Radar Sensor

   https://doi.org/10.3390/electronics9040573  

   Toker, Onur; Alsweiss, Suleiman (April 2020, Electronics)

   In this paper, we propose a novel 77 GHz automotive radar sensor, and demonstrate its cyberattack resilience using real measurements. The proposed system is built upon a standard Frequency Modulated Continuous Wave (FMCW) radar RF-front end, and the novelty is in the DSP algorithm used at the firmware level. All attack scenarios are based on real radar signals generated by Texas Instruments AWR series 77 GHz radars, and all measurements are done using the same radar family. For sensor networks, including interconnected autonomous vehicles sharing radar measurements, cyberattacks at the network/communication layer is a known critical problem, and has been addressed by several different researchers. What is addressed in this paper is cyberattacks at the physical layer, that is, adversarial agents generating 77 GHz electromagnetic waves which may cause a false target detection, false distance/velocity estimation, or not detecting an existing target. The main algorithm proposed in this paper is not a predictive filtering based cyberattack detection scheme where an “unusual” difference between measured and predicted values triggers an alarm. The core idea is based on a kind of physical challenge-response authentication, and its integration into the radar DSP firmware. 

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