Consider orthogonal planes in the 3-D space representing floors and walls in a large building. These planes divide the space into rooms where a wireless infrastructure is deployed. This paper is focused on the analysis of the correlated shadowing field created by this wireless infrastructure through the set of walls and floors. When the locations of the planes and of the wireless nodes are governed by Poisson processes, we obtain a simple stochastic model which captures the non-uniform nature of node deployment and room sizes. This model, which we propose to call the Poisson building, captures the complex in-building shadowing correlations, is scalable in the number of dimensions and is tractable for network performance analysis. It allows an exact mathematical characterization of the interference distribution in both infinite and finite buildings, which further leads to closed-form expressions for the coverage probabilities in in-building cellular networks and the success probability of in-building underlay D2D transmissions.
We propose and analyze a new shadowing field model meant to capture spatial correlations. The interference field associated with this new model is compared to that of the widely used independent shadowing model. Independent shadowing over links is adopted because of the resulting closed forms for performance metrics, and in spite of the well-known fact that the shadowing fields of networks are spatially correlated. The main purpose of this paper is to challenge this independent shadowing approximation. For this, we analyze the interference measured at the origin in networks where 1) nodes which are in the same cell of some random shadowing tessellation share the same shadow, or 2) nodes which share a common mother point in some cluster process share the same shadow. By leveraging stochastic comparison techniques, we give the order relation of the three main user performance metrics, namely coverage probability, Shannon throughput and local delay, under both the correlated and the independent shadowing assumptions. We show that the evaluation of the considered metrics under the independent approximation is systematically pessimistic compared to the correlated shadowing model. The improvement in each metric when adopting the correlated shadow model is quantified and shown to be quite significant.
In this paper, we examine the benefits of multiple antenna communication in random wireless networks, the topology of which is modeled by stochastic geometry. The setting is that of the Poisson bipolar model introduced in [1], which is a natural model for ad-hoc and device-to-device (D2D) networks. The primary finding is that, with knowledge of channel state information between a receiver and its associated transmitter, by zero-forcing successive interference cancellation, and for appropriate antenna configurations, the ergodic spectral efficiency can be made to scale linearly with both 1) the minimum of the number of transmit and receive antennas, 2) the density of nodes and 3) the path-loss exponent. This linear gain is achieved by using the transmit antennas to send multiple data streams (e.g. through an open-loop transmission method) and by exploiting the receive antennas to cancel interference. Furthermore, when a receiver is able to learn channel state information from a certain number of near interferers, higher scaling gains can be achieved when using a successive interference cancellation method. A major implication of the derived scaling laws is that spatial multiplexing transmission methods are essential for obtaining better and eventually optimal scaling laws in multiple antenna random wireless networks. Simulation results support this analysis.
Background: This study aimed to investigate the measures of retention in care (RIC) in persons living with HIV (PLWH) and type 2 diabetes mellitus (T2DM) by age group (younger vs. older adults). Methods: This was a longitudinal retrospective cross-sectional study that used secondary data from the Center for AIDS Research Network of Integrated Clinical Systems (CNICS). We examined RIC in 798 adult PLWH + T2DM who visited a CNICS clinic at least once in 2015. Six measures of RIC were examined: missed visits [measured as a continuous variable (total number of missed visits) and dichotomous variable (0 = never missed, 1 = missed)], visit adherence, 6-month visit gap, 4-month visit constancy, and the Health and Resources Services Administration HIV/AIDS Bureau's RIC measure. We calculated Spearman correlation coefficients and conducted logistic regression and multi-group path analysis. Results: Most RIC measures were significantly correlated (p < 0.05) with one another; only 4-month visit constancy was not correlated with other measures. Except for the number of missed visits in older adult PLWH + T2DM, we found no significant relationships between RIC measures and CD4 cell count using logistic regression. However, multi-group path analysis demonstrated significant positive relationships between most RIC measures and CD4 cell count in both age groups. In younger adults living with HIV (YALWH) + T2DM, HbA1c level, but not CD4 count, was significantly associated with most RIC measures. Conclusions: RIC is related to disease control (CD4 cell count and HbA1c level) in PLWH + T2DM and notably, HbA1c level was only significantly affected in YALWH + T2DM. A future study is needed to find more accurate reasons for the fact that only HbA1c level had significant relationships in YALWH + T2DM. The findings from this study provide guidance in measuring RIC in PLWH who have comorbidities.
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