In the 3.5 GHz Citizens Broadband Radio Service (CBRS) band, accurate path loss prediction is very important to protect the incumbent from harmful interference caused by the lower tier users. The current CBRS standards developed by the Wireless Innovation Forum use the irregular terrain model (ITM), also known as the Longley-Rice model, for path loss calculation. However, the model does not include clutter data, and thus, it underestimates the path loss. This paper utilizes a model-aided deep learning (DL) technique with satellite images to improve path loss prediction. Numerical study shows that the proposed approach can achieve a 4.23 dB root mean square error (RMSE) and outperform the Longley-Rice model and some tuned or fitted propagation models.

MOTIVATION: Bioinformatic tools capable of annotating, rapidly and reproducibly, large, targeted lipidomic datasets are limited. Specifically, few programs enable high-throughput peak assessment of liquid chromatography-electrospray ionization tandem mass spectrometry data acquired in either selected or multiple reaction monitoring modes. RESULTS: We present here Bayesian Annotations for Targeted Lipidomics, a Gaussian naïve Bayes classifier for targeted lipidomics that annotates peak identities according to eight features related to retention time, intensity, and peak shape. Lipid identification is achieved by modeling distributions of these eight input features across biological conditions and maximizing the joint posterior probabilities of all peak identities at a given transition. When applied to sphingolipid and glycerophosphocholine selected reaction monitoring datasets, we demonstrate over 95% of all peaks are rapidly and correctly identified. AVAILABILITY AND IMPLEMENTATION: BATL software is freely accessible online at https://complimet.ca/batl/ and is compatible with Safari, Firefox, Chrome and Edge. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Commercial-federal spectrum sharing in the 3.5 GHz band requires an Environmental Sensing Capability (ESC) system, consisting of sensors deployed along the coasts, to detect federal incumbent shipborne radar in order to protect it from harmful interference from commercial users. The coastal waters where the radar needs protection from interference are divided into a chain of contiguous polygons called Dynamic Protection Areas (DPAs). The sensor(s) associated with each DPA must cover that DPA completely but should minimize any excess coverage on land, in neighboring DPAs, and out at sea. Thus, placement of sensors and their operating parameters are determined by solving this coverage problem. We use existing tower sites as candidate locations for the sensors and the Irregular Terrain Model (ITM) in point-to-point mode to compute the path loss. We present an algorithm for computing the locations and operating parameters of the sensors such that the excess area, and thus the probability of false alarm, is minimized.

The Federal Communications Commission rules for operation in the 3.5 GHz band require that an Environmental Sensing Capability (ESC) system detect the presence of a federal incumbent shipborne radar in order to protect it from harmful interference. Thus, ESC operators have to deploy ESC sensors along the coasts to comply with the rules. We formulate the ESC sensor deployment problem as a coverage problem where ESC sensors need to cover a predefined geometric area inside which radar may experience harmful interference. Using propagation models and radar parameters, we compute antenna lobe patterns for different beamwidths and detection thresholds of the ESC sensors. These patterns are then used to cover the geometric area such that both outage and excess coverage areas are minimized. We present a greedy algorithm and apply it to Dynamic Protection Areas currently being defined for the coasts of the contiguous United States. We evaluate its performance in terms of some key metrics important to the federal incumbent as well as commercial operators.