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1. WiMove: Toward Infrastructure Mobility in mmWave WiFi

   https://doi.org/10.1145/3416012.3424625  

   Jian, Yubing; Agarwal, Mohit; Venkateswaran, Shyam Krishnan; Liu, Yuchen; Blough, Douglas M.; Sivakumar, Raghupathy (November 2020, ACM Symposium on Mobility Management and Wireless Access)

   Line-of-sight (LOS) is a critical requirement for mmWave wireless communications. In this work, we explore the use of access point (AP) infrastructure mobility to optimize indoor mmWave WiFi network performance based on the discovery of LOS connectivity to stations (STAs).We consider a ceiling-mounted mobile (CMM) AP as the infrastructure mobility framework. Within this framework, we present a LOS prediction algorithm based on machine learning (ML) that addresses the LOS discovery problem. The algorithm relies on the available network state information (e.g., LOS connectivity between STAs and the AP) to predict the unknown LOS connectivity status between the reachable AP locations and target STAs. We show that the proposed algorithm can predict LOS connectivity between the AP and target STAs with an accuracy up to 91%. Based on the LOS prediction algorithm, we then propose a systematic solution WiMove, which can decide if and where the AP should move to for optimizing network performance. Using both ns-3 based simulation and experimental prototype implementation, we show that the throughput and fairness performance of WiMove is up to 119% and 15% better compared with single static AP and brute force search. 

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2. AutoCLC: Towards Automated Assessment and Feedback on Closed-Loop Communication in Team-based Healthcare Simulation Training

   https://doi.org/10.1145/3768164  

   Li, Kaylee Yaxuan; Huang, Kunpeng; Harmer, Bryan; Ducharme, Casey; Heasman, Benjamin; Falahee, Eleanor Anne; Cooke, James M; Cole, Michael; Sample, Alanson P; Popov, Vitaliy (September 2025, ACM Transactions on Computing for Healthcare)

   This study introduces AutoCLC, an AI-powered system designed to assess and provide feedback on closed-loop communication (CLC) in professional learning environments. CLC, where a sender’s Call-Out statement is acknowledged by the receiver’s Check-Back statement, is a critical safety protocol in high-reliability domains, including emergency medicine resuscitation teams. Existing methods for evaluating CLC lack quantifiable metrics and depend heavily on human observation. AutoCLC addresses these limitations by leveraging natural language processing and large language models to analyze audio recordings from Advanced Cardiovascular Life Support (ACLS) simulation training. The system identifies CLC instances, measures their frequency and rate per minute, and categorizes communications as effective, incomplete, or missed. Technical evaluations demonstrate AutoCLC achieves 78.9% precision for identifying Call-Outs and 74.3% for Check-Backs, with a performance gap of only 5% compared to human annotations. A user study involving 11 cardiac arrest instructors across three training sites supported the need for automated CLC assessment. Instructors found AutoCLC reports valuable for quantifying CLC frequency and quality, as well as for providing actionable, example-based feedback. Participants rated AutoCLC highly, with a System Usability Scale score of 76.4%, reflecting above-average usability. This work represents a significant step toward developing scalable, data-driven feedback systems that enhance individual skills and team performance in high-reliability settings. 

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3. Centrality-based lane interventions in road networks for improved level of service: the case of downtown Boise, Idaho

   https://doi.org/10.1007/s41109-023-00532-z  

   Ahmed, Md Ashraf; Kays, H. M.; Sadri, Arif Mohaimin (December 2023, Applied Network Science)

   Abstract Recent advancements in network science showed that the topological credentials of the elements (i.e., links) in a network carry important implications. Likewise, roadway segments (i.e., links) in a road network should be assessed based on their network position along with traffic conditions at a given geographic scale. The goal of this study is to present a framework that can identify and select critical links in a road network based on their topological importance such as centrality, and the effects of systematic interventions conducted on such links in improving overall system performance (vehicle delay, travel time) to provide an adequate level of service (LOS). A real-world road network (Boise downtown) is investigated by applying lane interventions on roadways experiencing high congestion. Microscopic traffic simulation and analyses are conducted to estimate the traffic flow parameters hence the performance of the road segments. The findings of this study show that interventions applied to critical and congested road segments improve the serviceability from LOS F to LOS E as well as from LOS D to LOS C. Besides, reduced travel time and vehicular delay (after applying intervention on critical components) are also observed for high demand OD pairs of the road network. As such the proposed framework has the potential to incorporate the topological credentials with traffic flow parameters and improve the performance of the road network. This systematic approach will help traffic managers and practitioners to develop strategies that enhance road network performance. 

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4. Opportunistic Temporal Spectrum Coexistence of Passive Radiometry and Active Wireless Networks

   https://doi.org/10.1109/WNYISPW57858.2022.9983493  

   Koosha, Mohammad; Mastronarde, Nicholas (November 2022, 2022 IEEE Western New York Image and Signal Processing Workshop (WNYISPW))

   There is insufficient wireless frequency spectrum to support the continued growth of active wireless technologies and devices. This has provoked extensive research on spectrum coexistence. One case that has gained limited attention in this course is using currently banned frequency bands for active wireless communications. One such option is the 27 MHz-wide narrowband portion of the L-band from 1.400 to 1.427 GHz, which is exclusively devoted to space-borne passive radiometry for remote sensing and radio astronomy. Radio regulations currently prohibit active wireless communications and radars from operating in this band to avoid radio frequency interference (RFI) on highly noise-sensitive passive radiometry equipment. The National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active Passive (SMAP) satellite is one of the latest space-borne remote sensing missions that evaluates global soil moisture by passive scanning of the thermal emissions of the earth in this frequency band. In this paper, we investigate the opportunistic temporal use of this 27 MHz-wide passive radiometry band for active wireless transmissions when there is no Line of Sight (LoS) between SMAP and a terrestrial wireless network. We use MATLAB simulations to determine the fraction of time that SMAP has LoS (and non-LoS) with a terrestrial wireless cell at different Earth latitudes based on SMAP’s orbital characteristics. We also investigate the severity of RFI induced on SMAP in the presence of a terrestrial cluster of 5G cells with LoS. 

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5. 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. 

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