{"title":"mmRAPID","authors":"Han Yan, B. Domae, Danijela Cabric","doi":"10.1145/3412060.3418432","DOIUrl":"https://doi.org/10.1145/3412060.3418432","url":null,"abstract":"Millimeter-wave communication has the potential to deliver orders of magnitude increases in mobile data rates. A key design challenge is to enable rapid beam alignment with phased arrays. Traditional millimeter-wave systems require a high beam alignment overhead, typically an exhaustive beam sweep, to find the beam direction with the highest beamforming gain. Compressive sensing is a promising framework to accelerate beam alignment. However, model mismatch from practical array hardware impairments poses a challenge to its implementation. In this work, we introduce a neural network assisted compressive beam alignment method that uses noncoherent received signal strength measured by a small number of pseudorandom sounding beams to infer the optimal beam steering direction. We experimentally showcase our proposed approach with a 60GHz 36-element phased array in a suburban line-of-sight environment. The results show that our approach achieves post alignment beamforming gain within 1dB margin compared to an exhaustive search with 90.2% overhead reduction. Compared to purely model-based noncoherent compressive beam alignment, our method has 75% overhead reduction.","PeriodicalId":284872,"journal":{"name":"Proceedings of the 4th ACM Workshop on Millimeter-Wave Networks and Sensing Systems","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121856006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"mMobile","authors":"I. Jain, Raghav Subbaraman, Tejas Harekrishna Sadarahalli, Xiangwei Shao, Hou-Wei Lin, Dinesh Bharadia","doi":"10.1145/3412060.3418433","DOIUrl":"https://doi.org/10.1145/3412060.3418433","url":null,"abstract":"Beamforming methods need to be critically evaluated and improved to achieve the promised performance of millimeter-wave (mmWave) 5G NR in high mobility applications like Vehicle-to-Everything (V2X) communication. Conventional beam management methods developed for higher frequency applications do not directly carry over to the 28 GHz mmWave regime, where propagation and reflection characteristics are vastly different. Further, real system deployments and tests are required to verify these methods in a practical setting. In this work, we develop mMobile, a custom 5G NR compliant mmWave testbed to evaluate beam management algorithms. We describe the architecture and challenges in building such a testbed. We then create a novel, low-complexity beam tracking algorithm by exploiting the 5G NR waveform structure and evaluate its performance on the testbed. The algorithm can sustain almost twice the average throughput compared to the baseline. We also release our outdoor mobility dataset that can be used to improve mobility management further.","PeriodicalId":284872,"journal":{"name":"Proceedings of the 4th ACM Workshop on Millimeter-Wave Networks and Sensing Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114374802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thomas Stadelmayer, Markus Stadelmayer, Avik Santra, R. Weigel, F. Lurz
{"title":"Human Activity Classification Using mm-Wave FMCW Radar by Improved Representation Learning","authors":"Thomas Stadelmayer, Markus Stadelmayer, Avik Santra, R. Weigel, F. Lurz","doi":"10.1145/3412060.3418430","DOIUrl":"https://doi.org/10.1145/3412060.3418430","url":null,"abstract":"The paper proposes a novel Euclidean distance softmax layer for radar-based human activity classification. The method aims to overcome the angular dependency of classical softmax approaches. Through the freedoms thus gained, the activity classes can be distributed freely within the entire embedded feature space, due to which the dimension of the embeddings and the whole neural network size can be reduced. The performance of our novel deep learning architecture is evaluated for 60 GHz mm-wave radar sensor-based human activity classification. The results show that the proposed approach increases the robustness against random and unknown movements compared to state-of-art representation learning techniques.","PeriodicalId":284872,"journal":{"name":"Proceedings of the 4th ACM Workshop on Millimeter-Wave Networks and Sensing Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121581005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michele Polese, L. Bertizzolo, Leonardo Bonati, A. Gosain, T. Melodia
{"title":"An Experimental mmWave Channel Model for UAV-to-UAV Communications","authors":"Michele Polese, L. Bertizzolo, Leonardo Bonati, A. Gosain, T. Melodia","doi":"10.1145/3412060.3418431","DOIUrl":"https://doi.org/10.1145/3412060.3418431","url":null,"abstract":"Unmanned Aerial Vehicle (UAV) networks can provide a resilient communication infrastructure to enhance terrestrial networks in case of traffic spikes or disaster scenarios. However, to be able to do so, they need to be based on high-bandwidth wireless technologies for both radio access and backhaul. With this respect, the millimeter wave (mmWave) spectrum represents an enticing solution, since it provides large chunks of untapped spectrum that can enable ultra-high data-rates for aerial platforms. Aerial mmWave channels, however, experience characteristics that are significantly different from terrestrial deployments in the same frequency bands. As of today, mmWave aerial channels have not been extensively studied and modeled. Specifically, the combination of UAV micro-mobility (because of imprecisions in the control loop, and external factors including wind) and the highly directional mmWave transmissions require ad hoc models to accurately capture the performance of UAV deployments. To fill this gap, we propose an empirical propagation loss model for UAV-to-UAV communications at 60 GHz, based on an extensive aerial measurement campaign conducted with the Facebook Terragraph channel sounders. We compare it with 3GPP channel models and make the measurement dataset publicly available.","PeriodicalId":284872,"journal":{"name":"Proceedings of the 4th ACM Workshop on Millimeter-Wave Networks and Sensing Systems","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129870522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}