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23 results for “mmWave”
5G-PICTURE_Smart_City_Demo_mmWave_60GHz_dataset
<p>In the context of 5G-PICTURE, three demos were performed, one being the Smart City demo. For this demonstration, three 60 GHz devices (one AP and two STA) were deployed to provide the high throughout Point-to-MultiPoint (P2MP) wireless connectivity for two use cases, the Virtual Reality and the Safety Camera. The two links were stationary with a distance of 60 metres and 80 metres, respectively, providing the wireless connectivity to the tents, specifically installed in the Millennium Square for the abovementioned use cases. In particular, for the mmWave (60GHz) backhaul links, the end-to-end throughput and latency have been measured in the field testing in days before the demo.</p> <p>For more details, interested reader should refer to the project website (https://www.5g-picture-project.eu) and deliverable D6.3.</p>
KR, Remote Driving, Remote driving using mmWave communication
<p>Use Case Category: <strong>Remote Driving</strong><br> User Story: <strong>Remote driving using mmWave communication</strong><br> Location: Korean (KR) trial site</p> <p>According to 3GPP TS 22.186 R16, Remote Driving “enables a remote driver or a V2X application to operate a remote vehicle for those passengers who cannot drive themselves or a remote vehicle located in dangerous environments. For a case where variation is limited, and routes are predictable, such as public transportation, driving based on cloud computing can be used. In addition, access to cloud-based back-end service platform can be considered for this use case group”.</p> <p>User Story: <strong>Remote driving using mmWave communication</strong></p> <p>Remote driving use case enables remote operator to access the right of control in case of automated vehicle in under malfunction or driver is in accident. The most important factors for realizing remote driving should comprise the following: Ensuring enough field of view and high definition of view for front camera, ultra-low latency to sharing live video stream between vehicle equipped cameras and remote site, and reliable connectivity to control remote driving vehicle in remote site. Consequently, remote driving vehicle needs to be shared not only driving information like speed, position, and videos (front, right and left side, and rear), but also vehicle status information like steering angle, gear position, throttle pedal position, and fuel consumption with remote operator. The driving and status information provided by the remote driving vehicle should be transmitted to the human operator at the remote site with ultra-low latency. In order to sharing high definition live video stream data with remote site in real-time, very high up-link data rate should be required and it will be realised by 5G network. At the same time, the control data to driving remote vehicle</p> <p>should be generated by human operator at the remote site and be streamed to the remote driving vehicle through down-link with low latency.</p> <p>An emergency stop function of the remote driving vehicle should be enabled automatically when the connectivity is unstable between remote driving vehicle and remote site, thereby achieving more reliable and robust safety.</p> <p>As a deployment scenario for remote driving use case, remote driving vehicle has a connectivity to the 5G network through a base station (BS) including microcell BS, and BS-type road side unit (RSU). In-vehicle UE that is equipped in the remote driving vehicle will provide the connectivity to the 5G network by connected with RSU.</p> <p>The remote driving use case generally supports eMBB 5G-service to stream raw sensor and high definition video data from remote driving vehicle to remote site. Hence, it intrinsically requires high data throughput up to several Gbps. It also supports URLLC 5G-service to exchange safety critical message.</p>
mmWave Radar and RGB-D Camera Sensor Data for Human Activity Recognition(2)
<p>This is a supplementary dataset, which is linked to https://zenodo.org/record/7088054#.YyVF3ehBwQ8. The dataset is composed of corner radar point cloud data and front radar point cloud data collected from environments existing obstacles between volunteers and sensors. </p>
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