A Deep Learning Approach to Location- and Orientation-aided 3D Beam Selection for mmWave Communications

Position-aided beam selection methods have been shown to be an effective approach to achieve high beamforming gain while limiting the overhead and latency of initial access in millimeter wave (mmWave) communications. Most research in the area, however, has focused on vehicular applications, where the orientation of the user terminal (UT) is mostly fixed at each position of the environment. This paper proposes a location- and orientation-based beam selection method to enable context information (CI)-based beam alignment in applications where the UT can take arbitrary orientation at each location. We propose three different network structures, with different amounts of trainable parameters that can be used with different training dataset sizes. A professional 3-dimensional ray tracing tool is used to generate datasets for an IEEE standard indoor scenario. Numerical results show the proposed networks outperform a CI-aided benchmark such as the generalized inverse fingerprinting (GIFP) method as well as hierarchical beam search as a non-CI-based approach. Moreover, compared to the GIFP method, the proposed deep learning-based beam selection shows higher robustness to different line-of-sight blockage probability in the training and test datasets and lower sensitivity to inaccuracies in the position and orientation information.Comment: 30 pages, 12 figure. This article was submitted to IEEE Transactions on Wireless Communications on Oct 11 202

Device-Agnostic Millimeter Wave Beam Selection using Machine Learning

Most research in the area of machine learning-based user beam selection considers a structure where the model proposes appropriate user beams. However, this design requires a specific model for each user-device beam codebook, where a model learned for a device with a particular codebook can not be reused for another device with a different codebook. Moreover, this design requires training and test samples for each antenna placement configuration/codebook. This paper proposes a device-agnostic beam selection framework that leverages context information to propose appropriate user beams using a generic model and a post processing unit. The generic neural network predicts the potential angles of arrival, and the post processing unit maps these directions to beams based on the specific device's codebook. The proposed beam selection framework works well for user devices with antenna configuration/codebook unseen in the training dataset. Also, the proposed generic network has the option to be trained with a dataset mixed of samples with different antenna configurations/codebooks, which significantly eases the burden of effective model training.Comment: 30 pages, 19 figures. This article was submitted to IEEE Trans. Wirel. Commun. on Nov 14 202

Calcium and magnesium concentrations in uterine fluid and blood serum during the estrous cycle in the bovine

To investigate uterine and serum Ca++ and Mg++ variations during the estrous cycle in the bovine, 66 genital tracts and blood samples were collected from Urmia abattoir, Urmia, Iran. The phase of the estrous cycle was determined by examination of the structures present on ovaries and uterine tonicity. Of the collected samples, 17 were pro-estrus, 12 estrus, 14 metestrus and 23 diestrus. The uterine fluid was collected by gentle scraping of the uterine mucosa with a curette. The mean ± SEM concentration of serum Ca++ in pro-estrus, estrus, metestrus and diestrus was 5.77 ± 0.69, 8.87 ± 1.83, 10.95 ± 1.52, 11.09 ± 1.08 mg dL-1, and the mean concentration of uterine fluid Ca++ was 4.40 ± 0.72, 3.15 ± 0.67, 5.89 ± 0.88, 8.63 ± 0.97 mg dL-1, respectively. The mean concentration of serum Mg++ in pro-estrus, estrus, metestrus and diestrus was 3.53 ± 0.30, 4.20 ± 0.52, 3.49 ± 0.38, 3.39 ± 0.29 mg dL-1, and mean concentration of uterine fluid Mg++ was 5.27 ± 0.42, 4.92 ± 0.60, 5.56 ± 0.30, 5.88 ± 0.36 mg dL-1, respectively. The serum and uterine fluid Ca++ in pro-estrus were significantly different from those of the metestrus and diestrus. In all stages of estrous cycle the mean concentration of serum Ca++ was higher than that in the uterine fluid. The difference between serum and uterine fluid Ca++ in estrus, metestrus and diestrus was significant. There was no significant difference between serum Mg++ content nor was it different from uterine fluid Mg++ content at any stages of estrous cycle. In all stages of estrous cycle the uterine fluid Mg++ was higher than that of the serum. These results suggest that during the estrous cycle in the cow, Ca++ is passively secreted in uterine fluids and is mostly dependent on blood serum Ca++ variations but Mg++ is secreted independently and does not follow variations in the serum concentrations

Determination of sensitivity, specificity and cut off point of visual- Motor Bender Gestalt Test in the diagnosis of traumatic brain injury

Background: Bender Gestalt Test is one of the most famous neuropsychological tests, simple and easy to perform, and is used to evaluate brain injuries. This study aimed at determining the rate of sensitivity, characteristic and cut-off point of this test in patients with traumatic brain injury (TBI). Methods and Materials: Overall, 120 TBI patients with mean age of 31.25± 13.60 years old in a descriptive-analytical research design entered the study using nonprobability and consecutive sampling method. All patients underwent Bender Visual-Motor Gestalt Test after neurological evaluations by CT scan. Roc curve test was utilized to analyze the data. Results: In this study, cut-off point was calculated as 6.5%, sensitivity as 55.8%, characteristic as 81.2%, and the area under the Roc curve as 0.69. Moreover, positive predictive value, negative predictive value and efficiency were 95.08%, 22.03%, and 59.17%, respectively. Conclusion: Results of this study revealed that Bender Gestalt Test is relatively weak in diagnosis of mild TBI. Hence, its characteristic is high and it was successful in diagnosing healthy individuals

Location- and Orientation-aware Millimeter Wave Beam Selection for Multi -Panel Antenna Devices

While initial beam alignment (BA) in millimeter-wave networks has been thoroughly investigated, most research assumes a simplified terminal model based on uniform linear/planar arrays with isotropic antennas. Devices with non-isotropic antenna elements need multiple panels to provide good spherical coverage, and exhaustive search over all beams of all the panels leads to unacceptable overhead. This paper proposes a location- and orientation-aware solution that manages the initial BA for multi-panel devices. We present three different neural network structures that provide efficient BA with a wide range of training dataset sizes, complexity, and feedback message sizes. Our proposed methods outperform the generalized inverse fingerprinting and hierarchical panel-beam selection methods for two considered edge and edge-face antenna placement designs.Comment: 5 pages, 7 figure. This article was submitted to IEEE SPAWC 2022 on Mar 11 202