The Experts below are selected from a list of 61911 Experts worldwide ranked by ideXlab platform
Jiwon Seo - One of the best experts on this subject based on the ideXlab platform.
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Practical Simplified Indoor Multiwall Path-Loss Model
arXiv: Signal Processing, 2020Co-Authors: Taewon Kang, Jiwon SeoAbstract:Over the past few decades, attempts had been made to build a suitable channel prediction model to optimize radio transmission systems. It is particularly essential to predict the Path Loss due to the blockage of the signal, in indoor radio system applications. This paper proposed a multiwall Path-Loss propagation model for an indoor environment, operating at a transmission frequency of 2.45 GHz in the industrial, scientific, and medical (ISM) radio band. The effects of the number of the walls to be traversed along the radio propagation Path are considered in the model. To propose the model, the previous works on well-known indoor Path Loss models are discussed. Then, the Path Loss produced by the intervening walls in the propagation Path is measured, and the terms representing the Loss factors in the theoretical PathLoss model are modified. The analyzed results of the Path Loss factors acquired at 2.45 GHz are presented. The proposed Path-Loss model simplifies the Loss factor term with an admissible assumption of the indoor environment and predicts the Path-Loss factor accurately.
Jeffrey G. Andrews - One of the best experts on this subject based on the ideXlab platform.
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impact of dual slope Path Loss on user association in hetnets
Global Communications Conference, 2015Co-Authors: Nikhil Garg, Sarabjot Singh, Jeffrey G. AndrewsAbstract:Intelligent load balancing is essential to fully realize the benefits of dense heterogeneous networks. Current techniques have largely been studied with single slope Path Loss models, though multi-slope models are known to more closely match real deployments. This paper develops insight into the performance of biasing and uplink/downlink decoupling for user association in HetNets with dual slope Path Loss models. It is shown that dual slope Path Loss models change the tradeoffs inherent in biasing and reduce gains from both biasing and uplink/downlink decoupling. The results show that with the dual slope Path Loss models, the bias maximizing the median rate is not optimal for other users, e.g., edge users. Furthermore, optimal downlink biasing is shown to realize most of the gains from downlink-uplink decoupling. Moreover, the user association gains in dense networks are observed to be quite sensitive to the Path Loss exponent beyond the critical distance in a dual slope model.
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ICC - Downlink cellular network analysis with a dual-slope Path Loss model
2015 IEEE International Conference on Communications (ICC), 2015Co-Authors: Xinchen Zhang, Jeffrey G. AndrewsAbstract:Existing cellular network analyses are based on the standard power law Path Loss model. If the base stations are modeled by a Poisson point process, this leads to a tractable analysis of coverage probability and other metrics for downlink cellular networks. Yet, it is also well-known that the standard Path Loss model is idealized and does not capture the distance-dependence of the Path Loss exponent. This paper considers a more precise and general model, the dual-slope Path Loss model, where the Path Loss exponents are different for short links and long links differentiated by a critical distance. We derive compact expressions on the coverage probability and its tight closed-form estimate under this model. The analytical results show that the SINR does not monotonically increase with network density (as under the standard Path Loss model). Rather, ultra-densification leads to worse or even zero coverage when the near-field Path Loss exponent is 2 or less.
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Downlink Cellular Network Analysis With Multi-Slope Path Loss Models
IEEE Transactions on Communications, 2015Co-Authors: Xinchen Zhang, Jeffrey G. AndrewsAbstract:Existing cellular network analyses, and even simulations, typically use the standard Path Loss model where received power decays like $\Vert x\Vert^{-\alpha}$ over a distance $\Vert x\Vert$ . This standard Path Loss model is quite idealized, and in most scenarios the Path Loss exponent $\alpha$ is itself a function of $\Vert x\Vert$ , typically an increasing one. Enforcing a single Path Loss exponent can lead to orders of magnitude differences in average received and interference powers versus the true values. In this paper, we study multi-slope Path Loss models, where different distance ranges are subject to different Path Loss exponents. We focus on the dual-slope Path Loss function, which is a piece-wise power law and continuous and accurately approximates many practical scenarios. We derive the distributions of SIR, SNR, and finally SINR before finding the potential throughput scaling, which provides insight on the observed cell-splitting rate gain. The exact mathematical results show that the SIR monotonically decreases with network density, while the converse is true for SNR, and thus the network coverage probability in terms of SINR is maximized at some finite density. With ultra-densification (network density goes to infinity), there exists a phase transition in the near-field Path Loss exponent $\alpha_{0}$ : if $\alpha_{0} >1$ unbounded potential throughput can be achieved asymptotically; if $\alpha_{0} , ultra-densification leads in the extreme case to zero throughput.
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Downlink Cellular Network Analysis with Multi-slope Path Loss Models
arXiv: Information Theory, 2014Co-Authors: Xinchen Zhang, Jeffrey G. AndrewsAbstract:Existing cellular network analyses, and even simulations, typically use the standard Path Loss model where received power decays like $\|x\|^{-\alpha}$ over a distance $\|x\|$. This standard Path Loss model is quite idealized, and in most scenarios the Path Loss exponent $\alpha$ is itself a function of $\|x\|$, typically an increasing one. Enforcing a single Path Loss exponent can lead to orders of magnitude differences in average received and interference powers versus the true values. In this paper we study \emph{multi-slope} Path Loss models, where different distance ranges are subject to different Path Loss exponents. We focus on the dual-slope Path Loss function, which is a piece-wise power law and continuous and accurately approximates many practical scenarios. We derive the distributions of SIR, SNR, and finally SINR before finding the potential throughput scaling, which provides insight on the observed cell-splitting rate gain. The exact mathematical results show that the SIR monotonically decreases with network density, while the converse is true for SNR, and thus the network coverage probability in terms of SINR is maximized at some finite density. With ultra-densification (network density goes to infinity), there exists a \emph{phase transition} in the near-field Path Loss exponent $\alpha_0$: if $\alpha_0 >1$ unbounded potential throughput can be achieved asymptotically; if $\alpha_0
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downlink cellular network analysis with multi slope Path Loss models
arXiv: Information Theory, 2014Co-Authors: Xinchen Zhang, Jeffrey G. AndrewsAbstract:Existing cellular network analyses, and even simulations, typically use the standard Path Loss model where received power decays like $\|x\|^{-\alpha}$ over a distance $\|x\|$. This standard Path Loss model is quite idealized, and in most scenarios the Path Loss exponent $\alpha$ is itself a function of $\|x\|$, typically an increasing one. Enforcing a single Path Loss exponent can lead to orders of magnitude differences in average received and interference powers versus the true values. In this paper we study \emph{multi-slope} Path Loss models, where different distance ranges are subject to different Path Loss exponents. We focus on the dual-slope Path Loss function, which is a piece-wise power law and continuous and accurately approximates many practical scenarios. We derive the distributions of SIR, SNR, and finally SINR before finding the potential throughput scaling, which provides insight on the observed cell-splitting rate gain. The exact mathematical results show that the SIR monotonically decreases with network density, while the converse is true for SNR, and thus the network coverage probability in terms of SINR is maximized at some finite density. With ultra-densification (network density goes to infinity), there exists a \emph{phase transition} in the near-field Path Loss exponent $\alpha_0$: if $\alpha_0 >1$ unbounded potential throughput can be achieved asymptotically; if $\alpha_0 <1$, ultra-densification leads in the extreme case to zero throughput.
Taewon Kang - One of the best experts on this subject based on the ideXlab platform.
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Practical Simplified Indoor Multiwall Path-Loss Model
arXiv: Signal Processing, 2020Co-Authors: Taewon Kang, Jiwon SeoAbstract:Over the past few decades, attempts had been made to build a suitable channel prediction model to optimize radio transmission systems. It is particularly essential to predict the Path Loss due to the blockage of the signal, in indoor radio system applications. This paper proposed a multiwall Path-Loss propagation model for an indoor environment, operating at a transmission frequency of 2.45 GHz in the industrial, scientific, and medical (ISM) radio band. The effects of the number of the walls to be traversed along the radio propagation Path are considered in the model. To propose the model, the previous works on well-known indoor Path Loss models are discussed. Then, the Path Loss produced by the intervening walls in the propagation Path is measured, and the terms representing the Loss factors in the theoretical PathLoss model are modified. The analyzed results of the Path Loss factors acquired at 2.45 GHz are presented. The proposed Path-Loss model simplifies the Loss factor term with an admissible assumption of the indoor environment and predicts the Path-Loss factor accurately.
Yusun Chang - One of the best experts on this subject based on the ideXlab platform.
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MASS - Improved 5.9GHz V2V Short Range Path Loss Model
2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems, 2015Co-Authors: Billy Kihei, John A. Copeland, Yusun ChangAbstract:Modeling large-scale fading effects in Vehicle-to-Vehicle communications (V2V) in the 5.9GHz Dedicated Short Range Communication band has received broad coverage in the literature over the last 15 years. The majority of V2V channel measurement campaigns have focused on describing the expected Path Loss of the V2V channel through empirical models. The Path Loss is a channel metric which describes how fast the received signal strength decays with distance. It is well known that the Path Loss exponent and reference Path Loss (y-intercept) varies for different environments, but it is not well understood how the channel changes in a given environment relative to lane separation or vehicle orientation. This paper presents an improved Path Loss model for line-of-sight (LOS) V2V communications at distances less than 100m. The Path Loss model removes the Gaussian random variable component, typically used to model shadowing in classic power law Path Loss model, and instead makes the y-intercept and Path Loss exponent Gaussian random variables. Derived from extensive empirical measurement campaigns in which vehicle orientation, approach direction, and lane separation are considered, the new channel model is compared to experimental data in which the vehicles move at different speeds. The improved Path Loss model performs a better fit to experimental data than existing Path Loss models, including two-ray ground reflection, dual-slope piecewise linear, and classic power law.
Andreas F. Molisch - One of the best experts on this subject based on the ideXlab platform.
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Path Loss models with distance-dependent weighted fitting and estimation of censored Path Loss data
IET Microwaves Antennas & Propagation, 2016Co-Authors: Aki Karttunen, Andreas F. Molisch, Carl Gustafson, Rui Wang, Sooyoung Hur, Jianzhong Zhang, Jeong-ho ParkAbstract:Path Loss models are the most fundamental part of wireless propagation channel models. Path Loss is typically modelled as a (single-slope or multi-slope) power-law dependency on distance plus a log-normally distributed shadowing attenuation. Determination of the parameters of this model is usually done by fitting the model to results from measurements or ray tracing. The authors show that the typical least-square fitting to those data points is inherently biased to give the best fitting to the link distances that happen to have more evaluation points. A weighted fitting method is developed that emphasises the accuracy at the distance range that is consciously chosen by the user as most important for a system simulation. As a further important point that is typically not taken into account for Path Loss parameter extraction, the authors show that typically measurement data (but also ray tracing) is censored, i.e. Path Loss values above a certain threshold cannot be measured. The authors present examples of weighted fitting models, and models with and without the censored data, for 28 GHz channels in urban macrocells, and show that these effects have a significant impact on the extracted parameters and that the fitting accuracy can be improved with the presented methods.
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Path Loss modeling for vehicle-to-vehicle communications
IEEE Transactions on Vehicular Technology, 2011Co-Authors: Johan Kåredal, Alexander Paier, Nicolai Czink, Fredrik Tufvesson, Andreas F. MolischAbstract:Vehicle-to-vehicle (V2V) communications have received increasing attention lately, but there is a lack of reported results regarding important quantities such as Path Loss. This paper presents parameterized Path Loss models for V2V communications based on extensive sets of measurement data collected mainly under line-of-sight conditions in four different propagation environments: highway, rural, urban, and suburban. The results show that the Path Loss exponent is low for V2V communications, i.e., Path Loss slowly increases with increasing distance. We compare our results to those previously reported and find that, while they confirm some of the earlier work, there are also differences that motivate the need for further studies.
