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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.
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.
3. Optimal Access Point Placement for Multi-AP mmWave WLANs

   https://doi.org/10.1145/3345768.3355914  

   Liu, Yuchen ; Jian, Yubing ; Sivakumar, Raghupathy ; Blough, Douglas M. ( January 2019 , Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems)

   mmWave communication in 60GHz band has been recognized as an emerging technology to support various bandwidth-hungry applications in indoor scenarios. To maintain ultra-high throughputs while addressing potential blockage problems for mmWave signals, maintaining line-of-sight (LoS) communications between client devices and access points (APs) is critical. To maximize LoS communications, one approach is to deploy multiple APs in the same room. In this paper, we investigate the optimal placement of multiple APs using both analytical methods and simulations. Considering the uncertainty of obstacles and clients, we focus on two typical indoor settings: random-obstacle-random-client (RORC) scenarios and fixed-obstacle-random-client (FORC) scenarios. In the first case, we analytically derive the optimal positions of APs by solving a thinnest covering problem. This analytical result is used to show that deploying up to 5 APs in a specific room brings substantial performance gains. For the FORC scenario, we propose the shadowing-elimination search (SES) algorithm based on an analytic model to efficiently determine the placement of APs. We show, through simulations, that with only a few APs, the network can achieve blockage-free operation in the presence of multiple obstacles and also demonstrate that the algorithm produces near-optimal deployments. Finally, we perform ns-3 simulations based on the IEEEmore »802.11ad protocol at mmWave frequency to validate our analytical results. The ns-3 results show that proposed multi-AP deployments produce significantly higher aggregate performance as compared to other common AP placements in indoor scenarios.« less
4. 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.
5. 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.