More Like this

1. Learning-based mmWave V2I environment augmentation through tunable reflectors

   https://doi.org/10.1109/GLOBECOM38437.2019.9013979  

   Zhang, Lan; Chen, Xianhao; Fang, Yuguang; Huang, Xiaoxia; Fang, Xuming (December 2019, IEEE Global Communications Conference)

   To support the demand of multi-Gbps sensory data exchanges for enhancing (semi)-autonomous driving, millimeter-wave bands (mmWave) vehicular-to-infrastructure (V2I) communications have attracted intensive attention. Unfortunately, the vulnerability to blockages over mmWave bands poses significant design challenges, which can be hardly addressed by manipulating end transceivers, such as beamforming techniques. In this paper, we propose to enhance mmWave V2I communications by augmenting the transmission environments through reflection, where highly-reflective cheap metallic plates are deployed as tunable reflectors without damaging the aesthetic nature of the environments. In this way, alternative indirect line-of-sight (LOS) links are established by adjusting the angle of reflectors. Our fundamental challenge is to adapt the time-consuming reflector angle tuning to the highly dynamic vehicular environment. By using deep reinforcement learning, we propose the learning-based Fast Reflection (LFR) algorithm, which autonomously learns from the observable traffic pattern to select desirable reflector angles in advance for probably blocked vehicles in near future. Simulation results demonstrate our proposal could effectively augment mmWave V2I transmission environments with significant performance gain. 

   more » « less
2. Argus: Predictable Millimeter-Wave Picocells with Vision and Learning Augmentation

   https://doi.org/10.1145/3508022  

   Regmi, Hem; Sur, Sanjib (February 2022, Proceedings of the ACM on Measurement and Analysis of Computing Systems)

   We propose Argus, a system to enable millimeter-wave (mmWave) deployers to quickly complete site-surveys without sacrificing the accuracy and effectiveness of thorough network deployment surveys. Argus first models the mmWave reflection profile of an environment, considering dominant reflectors, and then use this model to find locations that maximize the usability of the reflectors. The key component in Argus is an efficient machine learning model that can map the visual data to the mmWave signal reflections of an environment and can accurately predict mmWave signal profile at any unobserved locations. It allows Argus to find the best picocell locations to provide maximum coverage and also lets users self-localize accurately anywhere in the environment. Furthermore, Argus allows mmWave picocells to predict device's orientation accurately and enables object tagging and retrieval for VR/AR applications. Currently, we implement and test Argus on two different buildings consisting of multiple different indoor environments. However, the generalization capability of Argus can easily update the model for unseen environments, and thus, Argus can be deployed to any indoor environment with little or no model fine-tuning. 

   more » « less
3. Maximizing Coverage for mmWave WLANs with Dedicated Reflectors

   https://doi.org/10.1109/ICC42927.2021.9500547  

   Deng, Ang; Liu, Yuchen; Blough, Douglas M. (June 2021, IEEE International Conference on Communications)

   To accommodate increasingly intensive application bandwidth demands, mmWave WLAN at 60 GHz has been identified as a promising technology with the potential to achieve Gbps throughput. However, mmWave performance is highly dependent on the signal's line-of-sight (LoS) condition due to its high penetration loss when obstructed. We study the use of dedicated flat passive reflectors to improve coverage in indoor mmWave WLANs through a reflector placement scheme that accommodates any general indoor scenario with pre-deployed ceiling-mounted access points (APs). The reflector locations are efficiently selected among all available vertical surfaces within the indoor environment. Through simulations, we show that deployment of intelligently placed reflectors can improve LoS coverage by up to 10%, which is more than deploying one additional AP. Results are provided to illustrate how different factors affect coverage and insights about preferred reflector placements are provided. 

   more » « less
4. mmPalm: Unlocking Ubiquitous User Authentication through Palm Recognition with mmWave Signals

   Xie, Yucheng Xie; Guo, Xiaonan; Wang, Yan; Cheng, Jerry Q; Zheng, Tianfang; Chen, Yingying; Wei, Yi; Ge, Yuan (September 2024, IEEE Conference on Communications and Network Security (CNS),)

   Biometric authentication systems are increasingly needed across a broad range of applications including in smart city environments (e.g., entering hotels), and in smart home environments (e.g., controlling smart devices). Traditional methods, such as face-based and fingerprint-based authentication, usually incur high costs to be installed in all this kind of environments. In this paper, we develop a ubiquitous low-effort user authentication approach, mmPalm, based on palm recognition using millimeter wave (mmWave) signals. mmWave technology has been adopted by WiGig and 5G, making mmPalm a low-cost solution that can be widely adopted in public places. In addition, the high resolution of mmWave signals allows mmPalm to extract detailed palm characteristics (e.g., palm geometry, skin thickness, and texture) that can assemble distinctive palmprints for user authentication. Our innovative virtual antennas design further increases the spatial resolution of a commercial mmWave device, enabling it to fully capture the comprehensive palmprint features. Moreover, to address the challenge of small-scale environmental changes (e.g., variations in palm-device distances and palm orientations), we design a novel palm profile augmentation method, utilizing a Conditional Generative Adversarial Network (cGAN) to generate synthetic palm profiles for mitigating palm instability. Furthermore, we design a cross-environment adaptation framework based on transfer learning to address the challenge of large-scale environmental changes, including multipath variations introduced by human bodies and nearby furniture. Extensive experiments with 30 participants through 6 months demonstrate that mmPalm achieves 99% authentication accuracy with resilience against different types of attacks, including random, impersonation, and counterfeit. 

   more » « less
5. Programmable and Open-Access Millimeter-Wave Radios in the PAWR COSMOS Testbed

   https://doi.org/10.1145/3477086.3480834  

   Chen, Tingjun; Maddala, Prasanthi; Skrimponis, Panagiotis; Kolodziejski, Jakub; Gu, Xiaoxiong; Paidimarri, Arun; Rangan, Sundeep; Zussman, Gil; Seskar, Ivan (October 2021, Proc. ACM MobiCom’21 Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization (WiNTECH’21) (to appear))

   null (Ed.)

   While millimeter-wave (mmWave) wireless has recently gained tremendous attention with the transition to 5G, developing a broadly accessible experimental infrastructure will allow the research community to make significant progress in this area. Hence, in this paper, we present the design and implementation of various programmable and open-access 28/60 GHz software-defined radios (SDRs), deployed in the PAWR COSMOS advanced wireless testbed. These programmable mmWave radios are based on the IBM 28 GHz 64-element dual-polarized phased array antenna module (PAAM) subsystem board and the Sivers IMA 60 GHz WiGig transceiver. These front ends are integrated with USRP SDRs or Xilinx RF-SoC boards, which provide baseband signal processing capabilities. Moreover, we present measurements of the TX/RX beamforming performance and example experiments (e.g., real-time channel sounding and RFNoC-based 802.11ad preamble detection), using the mmWave radios. Finally, we discuss ongoing enhancement and development efforts focusing on these radios. 

   more » « less