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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. 

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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. 

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3. Stabilizing Terahertz MIMO Channel Capacity with Controlled Diffuse Reflections

   https://doi.org/10.1109/ICC45041.2023.10279474  

   Singh, Suresh; Tran, Ha; Le, Thanh (May 2023, IEEE International Conference on Communications)

   Communication at the terahertz band is increasingly seen as vital for future short-range very high datarate channels. However, these channels suffer from significant environmental impairments and, as a result, providing coverage in indoor settings requires the use of directional line of sight (LoS) paths to visible users and reflected paths, using smooth metal reflectors, for users in the shadow of an obstruction. Previous work has shown that these types of reflected paths display similar characteristics to LoS paths and we call them R-LoS (reflected LoS). MIMO for LoS and R-LoS channels is feasible at terahertz frequencies and delivers very high capacity at some distances. Unfortunately, channel capacity varies greatly with small changes in distance (the channel matrix fluctuates between full rank and rank 1) which is undesirable for communication systems. In this paper, we utilize diffusive reflectors to create multiple reflections such that the MIMO channel capacity for R-LoS is better behaved. We conduct experiments at 410 GHz for reflections from different artificially created diffuse surfaces. We use measurements to estimate channel capacity for 2×2 MIMO when the only path is the diffuse reflected one. We show that by creating multiple controlled reflections, it is possible to achieve relatively stable capacity up to 13 - 16 bits/sec/Hz at varying distances. We also analyze the phase of the received signals and the beam profile in detail. Overall, our results indicate that by utilizing artificially created reflections, we can maintain a stable MIMO channel at high capacity. 

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4. Tunable Reflectors Enabled Environment Augmentation for Better mmWave WLANs

   https://doi.org/10.1109/ICCChina.2019.8855899  

   Zhang, Lan; Yan, Li; Lin, Bin; Fang, Yuguang; Fang, Xuming (August 2019, IEEE/CIC International Conference on Communications in China (ICCC))

   With significant commercial potentials, millimeter- wave (mmWave) based wireless local area networks (WLANs) have attracted intensive attention lately. Unfortunately, the susceptible transmission characteristics over mmWave bands, especially the vulnerability to blockages, poses significant design challenges. Although existing solutions, such as beamforming, can overcome some of the problems, they usually focus on enhancing end transceivers to adapt to the transmission environments, and sometimes are still less effective. In this paper, by deploying highly-reflective cheap metallic plates as tunable reflectors without damaging the aesthetic nature of the environments, we propose to augment WLAN transmission environments in a way to create more effective alternative indirect line-of-sight (LOS) links by adjusting the orientations of the reflectors. Based on this idea, we design a novel adaptive mechanism, called mmRef, to effectively tune the angels of the deployed reflectors and develop corresponding operational procedures. Our performance study demonstrates our proposed scheme could achieve significant gain by tuning the angles of deployed reflectors in the augmented transmission environment. 

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5. A Quantitative Exploration of Access Point Mobility for mmWave WiFi Networks

   https://doi.org/10.1109/ICC40277.2020.9148974  

   Jian, Yubing; Liu, Yuchen; Venkateswaran, Shyam Krishnan; Blough, Douglas M; Sivakumar, Raghupathy (June 2020, IEEE International Conference on Communications)

   mmWave is emerging as an essential technology for next-generation wireless networks due to its capability of delivering multi-gigabit throughput performance. To achieve such a promising performance in mmWave communications, Line-of-sight (LOS) connectivity is a critical requirement. In this work, we explore the strategy of infrastructure mobility to alter the location of an access point (AP) in order to provide LOS connectivity to stations (STAs) in indoor mmWave WiFi networks. Through both simulation-based and theoretical analyses, we make a detailed case for infrastructure mobility by identifying the impact of AP mobile platforms configurations on network performance and propose a ceiling-mounted mobile (CMM) AP model. Then, we compare the performance of a CMM AP with multiple static APs, and we identify that the throughput and fairness performance of a CMM AP is better than as many as 5 ceiling-mounted static APs. 

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