Search

Directional measurements of over 2600 links in four distinct factories at 28 GHz are used to formulate the path gain and azimuth gain models to allow reliable 90% coverage estimates. A simple theoretical model of path gain, dependent on ceiling and clutter heights, is found to represent path gain across the four factories with 4.4 dB root-mean-square error (RMSE), contrasted with 6.9 dB slope-intercept fit and 8.5–14.9 dB RMSE for 3GPP factory models. The model also did well against 3.5 GHz path loss data collected over 18 MHz bandwidth in one of the factories, with an RMSE of 3.3 dB. In nonline-of-sight (NLOS) conditions, scattering reduces available antenna azimuth gain from nominal value by up to 7.3 dB in 90% of links. Line-of-sight (LOS) blockage by a 1.7 m × 1 m obstacle in factory aisle leads to 7 dB signal reduction, attributed to availability of other paths. It is found that an access point (AP) using 25 dBm transmit power per polarization, with 23 dBi nominal gain and omnidirectional terminals, supporting $2\times $ 2 MIMO in a 400 MHz bandwidth, can provide 130 Mb/s for 90% of factory locations within 50 m. [ABSTRACT FROM AUTHOR]

Large bandwidth at millimeter wave (mm-wave) is crucial for fifth generation (5G) and beyond, but the high path loss (PL) requires highly accurate PL prediction for network planning and optimization. Statistical models with slope-intercept fit fall short in capturing large variations seen in urban canyons, whereas ray tracing, capable of characterizing site-specific features, faces challenges in describing foliage and street clutter and associated reflection/diffraction ray calculation. Machine learning (ML) is promising but faces three key challenges in PL prediction: 1) insufficient measurement data; 2) lack of extrapolation to new streets; 3) overwhelmingly complex features/models. We propose an ML-based urban canyon PL prediction model based on extensive 28 GHz measurements from Manhattan where street clutters are modeled via a light detection and ranging (LiDAR) point cloud dataset and buildings by a mesh-grid building dataset. We extract expert knowledge-driven street clutter features from the point cloud and aggressively compress the 3-D building information using a convolutional autoencoder. Using a new street-by-street training and testing procedure to improve generalizability, the proposed model using both clutter and building features achieves a prediction error [root-mean-square error (RMSE)] of 4.8 ± 1.1 dB compared to 10.6 ± 4.4 and 6.5 ± 2.0 dB for 3GPP line of sight (LOS) and slope-intercept prediction, respectively, where the standard deviation indicates street-by-street variation. By only using four most influential clutter features, the RMSE of 5.5 ± 1.1 dB is achieved. [ABSTRACT FROM AUTHOR]

Path gain and effective directional gain in azimuth in urban canyons from actual rooftop base station sites are characterized based on a massive data set of 3000 links on 12 streets in two cities, with over 21 million individual continuous-wave power measurements at 28 GHz using vertically polarized antennas. Large street-to-street path gain variation is found, with median street path gain varying over 30 dB at similar distances. Coverage in the street directly illuminated by a roof edge antenna is found to suffer an average excess loss of 11 dB relative to free space at 200 m, with an empirical slope-intercept fit model representing the data with 7.1 dB standard deviation. Offsetting the base antenna 5 m away from the roof edge, as is common in macrocellular deployments, introduces an additional average loss of 15 dB at 100 m, but this additional loss reduces with distance. Around the corner loss is well modeled by a diffraction formula with an empirically obtained diffraction coefficient. Effective azimuthal gain degradation due to scatter is limited to 2 dB for 90% of data, supporting effective use of high-gain antennas in urban street canyons. [ABSTRACT FROM AUTHOR]

Adequate coverage with high-gain antennas is a key to realizing the full promise of the bandwidth available at mm/cm wave bands. We report extensive indoor measurements at 28 GHz (1000 links, 9.9 million individual power measurements, 10 offices, 2 buildings), with/without line of sight (LOS) using a continuous wave channel sounder, with a 10° spinning horn, capable of capturing a full azimuth scan every 200 ms, in up to 171 dB path loss to characterize coverage with 90% confidence level. The environment had prominent corridors and rooms, as opposed to open/mixed offices in latest 3GPP standards. Guiding in corridors leads to much lower RMS azimuth spread (7° median in corridor non-LOS (NLOS) versus 42° in 3GPP) and higher penetration loss into rooms and around corners (30–32 dB, some 12 dB more loss than 3GPP at 20 m NLOS). Measured path gain in NLOS is predicted by a mode-diffusion model with 3.9 dB RMS error. Scattering degraded azimuth gain by up to 4 dB in the corridor and 7 dB in rooms with 90% probability. Link simulations in a canonical building indicate every corridor needs an access point to provide 1 Gb/s rate to adjoining rooms within 50 m using 400 MHz of bandwidth. [ABSTRACT FROM AUTHOR]