Dual-polarized spatial-temporal propagation measurement and modeling in uma o2i scenario at 3.5 GHz

Outdoor-to-indoor (O2I) coverage in urban areas by using the sub-6 GHz (sub-6G) band is important in the fifth generation (5G) mobile communication system. The spatial-temporal propagation characteristics in different polarizations in the 5G spectrum are crucial for the network coverage. In this paper, we measured the urban macrocell (UMa) O2I channels at 3.5 GHz in the space, time, and polarization domains simultaneously. The channel sounder utilized two ±45° polarized antenna arrays. The transmitter (TX) was placed on the rooftop of a five-storey building to emulate a base station and the receiver (RX) was moved in the corridors on different floors in another building to emulate user equipments (UEs). We obtained the small-scale parameters of excess delay, power, and azimuth/elevation of arrival (AoA/EoA) of individual multipath components (MPCs), the propagation profiles of azimuth/elevation power spectrum (APS/EPS) and power delay profile (PDP), and the large-scale parameters including azimuth/elevation spread of arrival (ASA/ESA) and delay spread (DS). Based on the measurement results, we propose the lifted-superposed Laplace distribution (LS-Laplace) function and lifted-superposed normal distribution (LS-Normal) function to model the APS and EPS, respectively, and a three-phase model for the PDP. We find that the ASA and ESA follow the lognormal distribution and the DS has a Rayleigh distribution. We also reveal the impact of surrounding environments and polarization on the channel propagation profiles and statistical characteristics. The measurement results and channel models in this paper provide reference for the design and deployment of the 5G system to exploit the spatial and polarization diversities in the UMa O2I scenario.This work was supported in part by the National Natural Science Foundation of China under Grant 61571370, Grant 61601365, and Grant 61801388, in part by the Key Research Program and Industrial Innovation Chain Project of Shaanxi Province under Grant 2019ZDLGY07-10, Grant 2019JQ-253, and Grant 2019JM-345, and in part by the China Postdoctoral Science Foundation under Grant BX20180262, Grant BX20190287, Grant 2018M641020, and Grant 2018M641019.Scopu

Failure mode and effects analysis on the air system of an aero turbofan engine sing the Gaussian model and evidence theory

Failure mode and effects analysis (FMEA) is a proactive risk management approach. Risk management under uncertainty with the FMEA method has attracted a lot of attention. The Dempster–Shafer (D-S) evidence theory is a popular approximate reasoning theory for addressing uncertain information and it can be adopted in FMEA for uncertain information processing because of its flexibility and superiority in coping with uncertain and subjective assessments. The assessments coming from FMEA experts may include highly conflicting evidence for information fusion in the framework of D-S evidence theory. Therefore, in this paper, we propose an improved FMEA method based on the Gaussian model and D-S evidence theory to handle the subjective assessments of FMEA experts and apply it to deal with FMEA in the air system of an aero turbofan engine. First, we define three kinds of generalized scaling by Gaussian distribution characteristics to deal with potential highly conflicting evidence in the assessments. Then, we fuse expert assessments with the Dempster combination rule. Finally, we obtain the risk priority number to rank the risk level of the FMEA items. The experimental results show that the method is effective and reasonable in dealing with risk analysis in the air system of an aero turbofan engine

A Method to Determine Generalized Basic Probability Assignment in the Open World

Dempster-Shafer evidence theory (D-S theory) has been widely used in many information fusion systems since it was proposed by Dempster and extended by Shafer. However, how to determine the basic probability assignment (BPA), which is the main and first step in D-S theory, is still an open issue, especially when the given environment is in an open world, which means the frame of discernment is incomplete. In this paper, a method to determine generalized basic probability assignment in an open world is proposed. Frame of discernment in an open world is established first, and then the triangular fuzzy number models to identify target in the proposed frame of discernment are established. Pessimistic strategy based on the differentiation degree between model and sample is defined to yield the BPAs for known targets. If the sum of all the BPAs of known targets is over one, then they will be normalized and the BPA of unknown target is assigned to 0; otherwise the BPA of unknown target is equal to 1 minus the sum of all the known targets BPAs. IRIS classification examples illustrated the effectiveness of the proposed method

A new weighting factor in combining belief function

Dempster-Shafer evidence theory has been widely used in various applications. However, to solve the problem of counter-intuitive outcomes by using classical Dempster-Shafer combination rule is still an open issue while fusing the conflicting evidences. Many approaches based on discounted evidence and weighted average evidence have been investigated and have made significant improvements. Nevertheless, all of these approaches have inherent flaws. In this paper, a new weighting factor is proposed to address this proble

A modified belief entropy in Dempster-Shafer framework.

How to quantify the uncertain information in the framework of Dempster-Shafer evidence theory is still an open issue. Quite a few uncertainty measures have been proposed in Dempster-Shafer framework, however, the existing studies mainly focus on the mass function itself, the available information represented by the scale of the frame of discernment (FOD) in the body of evidence is ignored. Without taking full advantage of the information in the body of evidence, the existing methods are somehow not that efficient. In this paper, a modified belief entropy is proposed by considering the scale of FOD and the relative scale of a focal element with respect to FOD. Inspired by Deng entropy, the new belief entropy is consistent with Shannon entropy in the sense of probability consistency. What's more, with less information loss, the new measure can overcome the shortage of some other uncertainty measures. A few numerical examples and a case study are presented to show the efficiency and superiority of the proposed method

A Modified Model of Failure Mode and Effects Analysis Based on Generalized Evidence Theory

Due to the incomplete knowledge, how to handle the uncertain risk factors in failure mode and effects analysis (FMEA) is still an open issue. This paper proposes a new generalized evidential FMEA (GEFMEA) model to handle the uncertain risk factor, which may not be included in the conventional FMEA model. In GEFMEA, not only the conventional risk factors, the occurrence, severity, and detectability of the failure mode, but also the other incomplete risk factors are taken into consideration. In addition, the relative importance among all these risk factors is well addressed in the proposed method. GEFMEA is based on the generalized evidence theory, which is efficient in handling incomplete information in the open world. The efficiency and some merit of the proposed method are verified by the numerical example and a real case study on aircraft turbine rotor blades

An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP

A new initial population strategy has been developed to improve the genetic algorithm for solving the well-known combinatorial optimization problem, traveling salesman problem. Based on the k-means algorithm, we propose a strategy to restructure the traveling route by reconnecting each cluster. The clusters, which randomly disconnect a link to connect its neighbors, have been ranked in advance according to the distance among cluster centers, so that the initial population can be composed of the random traveling routes. This process is k-means initial population strategy. To test the performance of our strategy, a series of experiments on 14 different TSP examples selected from TSPLIB have been carried out. The results show that KIP can decrease best error value of random initial population strategy and greedy initial population strategy with the ratio of approximately between 29.15% and 37.87%, average error value between 25.16% and 34.39% in the same running time