An official website of the United States government
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
Argus: Predictable Millimeter-Wave Picocells with Vision and Learning Augmentation
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
- PAR ID:
- 10358453
- Date Published:
- Journal Name:
- Proceedings of the ACM on Measurement and Analysis of Computing Systems
- Volume:
- 6
- Issue:
- 1
- ISSN:
- 2476-1249
- Page Range / eLocation ID:
- 1 to 26
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Design and standardization of future millimeter-wave (mmWave) wireless communications systems require accurate models of wireless propagation channels. In particular, comprehensive statistical models describing the effect of human bodies moving randomly in the surrounding environment, acting as reflectors or absorbers, on the received power and delay spread are urgently needed. To enable these, new measurements campaigns are required based on channel sounders designed specifically to capture the realtime dynamics of the channel responses. This paper proposes a new methodology to enable fully dynamic measurements with a pseudonoise (PN)-sequence channel sounder by means of quasi-perfect transmitter-receiver (Tx-Rx) synchronization and suppression of probing signal effects in the post-processed channel impulse responses (CIRs). This approach allows the identification of the weak multipath components (MPCs) originated by reflections on the human body. The approach is validated by analysing CIRs collected in an indoor environment with one person moving close to the 60 GHz link. The results also demonstrate that future mmWave systems could exploit these additional MPCs and benefit from human interactions.more » « less
-
Abstract—Millimeter-wave (mmWave) and sub-Terahertz (THz) frequencies are expected to play a vital role in 6G wireless systems and beyond due to the vast available bandwidth of many tens of GHz. This paper presents an indoor 3-D spatial statistical channel model for mmWave and sub-THz frequencies based on extensive radio propagation measurements at 28 and 140 GHz conducted in an indoor office environment from 2014 to 2020. Omnidirectional and directional path loss models and channel statistics such as the number of time clusters, cluster delays, and cluster powers were derived from over 15,000 measured power delay profiles. The resulting channel statistics show that the number of time clusters follows a Poisson distribution and the number of subpaths within each cluster follows a composite exponential distribution for both LOS and NLOS environments at 28 and 140 GHz. This paper proposes a unified indoor statistical channel model for mmWave and sub-Terahertz frequencies following the mathematical framework of the previous outdoor NYUSIM channel models. A corresponding indoor channel simulator is developed, which can recreate 3-D omnidirectional, directional, and multiple input multiple output (MIMO) channels for arbitrary mmWave and sub-THz carrier frequency up to 150 GHz, signal bandwidth, and antenna beamwidth. The presented statistical channel model and simulator will guide future air-interface, beamforming, and transceiver designs for 6G and beyond. Index Terms—Millimeter-wave, terahertz, radio propagation, indoor office scenario, channel measurement, channel modeling, channel simulation, NYUSIM, 28 GHz, 140 GHz, 142 GHz, 5G, 6G.more » « less
-
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.more » « less
-
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
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3508022
