The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform

Wenyi Zhang - One of the best experts on this subject based on the ideXlab platform.

  • The Gauss–Poisson Process for Wireless Networks and the Benefits of Cooperation
    IEEE Transactions on Communications, 2016
    Co-Authors: Yi Zhong, Wenyi Zhang, Martin Haenggi
    Abstract:

    Gauss-Poisson processes (GPPs) are a class of Clustered Point processes, which include the Poisson Point process as a special case and have a simpler structure than the general Poisson Cluster Point processes. A key property of the GPP is that it is completely defined by its first- and second-order statistics. In this paper, we first show the properties of the GPP and provide an approach to fit the GPP to a given Point set. A fitting example is presented. We then propose the GPP as a model for wireless networks that exhibit Clustering behavior and derive the signal-to-interference-ratio distributions for different system models: 1) the basic model where the desired transmitter is independent of the GPP and all nodes in the GPP are interferers; 2) the non-cooperative model where the desired transmitter is one of the nodes in the GPP; and 3) the cooperative model, where the nodes in a GPP Cluster transmit cooperatively. The simulation results indicate that a significant gain can be achieved with cooperation.

  • ISIT - Success probabilities in Gauss-Poisson networks with and without cooperation
    2014 IEEE International Symposium on Information Theory, 2014
    Co-Authors: Yi Zhong, Martin Haenggi, Wenyi Zhang
    Abstract:

    Gauss-Poisson processes (GPPs) are a class of Clustered Point processes, which include the Poisson Point process as a special case and have a simpler structure than general Poisson Cluster Point processes. In this paper, we propose the GPP as a model for wireless networks that exhibit Clustering behavior. We calculate the success probabilities and provide their bounds for three kinds of GPP networks: (1) the basic model where the desired transmitter is independent of the GPP and all nodes in the GPP are interferers; (2) the non-cooperative model where the desired transmitter is one of the nodes in the GPP; (3) the cooperative model where both nodes in a two-node Cluster of the GPP serve a receiver cooperatively using non-coherent joint transmission. Our results show that the bounds, especially the upper bounds, provide useful approximations that well fit the actual success probability for different operating regimes.

  • Success probabilities in Gauss-Poisson networks with and without cooperation
    2014 IEEE International Symposium on Information Theory, 2014
    Co-Authors: Yi Zhong, Martin Haenggi, Wenyi Zhang
    Abstract:

    Gauss-Poisson processes (GPPs) are a class of Clustered Point processes, which include the Poisson Point process as a special case and have a simpler structure than general Poisson Cluster Point processes. In this paper, we propose the GPP as a model for wireless networks that exhibit Clustering behavior. We calculate the success probabilities and provide bounds for three kinds of GPP networks: (1) the basic model where the desired transmitter is independent of the GPP and all nodes in the GPP are interferers; (2) the non-cooperative model where the desired transmitter is one of the nodes in the GPP; (3) the cooperative model where both nodes in a two-node Cluster of the GPP serve a receiver cooperatively using non-coherent joint transmission. Our results show that the bounds, especially the upper bounds, provide good approximations for different operating regimes.

Martin Haenggi - One of the best experts on this subject based on the ideXlab platform.

  • The Gauss–Poisson Process for Wireless Networks and the Benefits of Cooperation
    IEEE Transactions on Communications, 2016
    Co-Authors: Yi Zhong, Wenyi Zhang, Martin Haenggi
    Abstract:

    Gauss-Poisson processes (GPPs) are a class of Clustered Point processes, which include the Poisson Point process as a special case and have a simpler structure than the general Poisson Cluster Point processes. A key property of the GPP is that it is completely defined by its first- and second-order statistics. In this paper, we first show the properties of the GPP and provide an approach to fit the GPP to a given Point set. A fitting example is presented. We then propose the GPP as a model for wireless networks that exhibit Clustering behavior and derive the signal-to-interference-ratio distributions for different system models: 1) the basic model where the desired transmitter is independent of the GPP and all nodes in the GPP are interferers; 2) the non-cooperative model where the desired transmitter is one of the nodes in the GPP; and 3) the cooperative model, where the nodes in a GPP Cluster transmit cooperatively. The simulation results indicate that a significant gain can be achieved with cooperation.

  • ISIT - Success probabilities in Gauss-Poisson networks with and without cooperation
    2014 IEEE International Symposium on Information Theory, 2014
    Co-Authors: Yi Zhong, Martin Haenggi, Wenyi Zhang
    Abstract:

    Gauss-Poisson processes (GPPs) are a class of Clustered Point processes, which include the Poisson Point process as a special case and have a simpler structure than general Poisson Cluster Point processes. In this paper, we propose the GPP as a model for wireless networks that exhibit Clustering behavior. We calculate the success probabilities and provide their bounds for three kinds of GPP networks: (1) the basic model where the desired transmitter is independent of the GPP and all nodes in the GPP are interferers; (2) the non-cooperative model where the desired transmitter is one of the nodes in the GPP; (3) the cooperative model where both nodes in a two-node Cluster of the GPP serve a receiver cooperatively using non-coherent joint transmission. Our results show that the bounds, especially the upper bounds, provide useful approximations that well fit the actual success probability for different operating regimes.

  • Success probabilities in Gauss-Poisson networks with and without cooperation
    2014 IEEE International Symposium on Information Theory, 2014
    Co-Authors: Yi Zhong, Martin Haenggi, Wenyi Zhang
    Abstract:

    Gauss-Poisson processes (GPPs) are a class of Clustered Point processes, which include the Poisson Point process as a special case and have a simpler structure than general Poisson Cluster Point processes. In this paper, we propose the GPP as a model for wireless networks that exhibit Clustering behavior. We calculate the success probabilities and provide bounds for three kinds of GPP networks: (1) the basic model where the desired transmitter is independent of the GPP and all nodes in the GPP are interferers; (2) the non-cooperative model where the desired transmitter is one of the nodes in the GPP; (3) the cooperative model where both nodes in a two-node Cluster of the GPP serve a receiver cooperatively using non-coherent joint transmission. Our results show that the bounds, especially the upper bounds, provide good approximations for different operating regimes.

Yi Zhong - One of the best experts on this subject based on the ideXlab platform.

  • The Gauss–Poisson Process for Wireless Networks and the Benefits of Cooperation
    IEEE Transactions on Communications, 2016
    Co-Authors: Yi Zhong, Wenyi Zhang, Martin Haenggi
    Abstract:

    Gauss-Poisson processes (GPPs) are a class of Clustered Point processes, which include the Poisson Point process as a special case and have a simpler structure than the general Poisson Cluster Point processes. A key property of the GPP is that it is completely defined by its first- and second-order statistics. In this paper, we first show the properties of the GPP and provide an approach to fit the GPP to a given Point set. A fitting example is presented. We then propose the GPP as a model for wireless networks that exhibit Clustering behavior and derive the signal-to-interference-ratio distributions for different system models: 1) the basic model where the desired transmitter is independent of the GPP and all nodes in the GPP are interferers; 2) the non-cooperative model where the desired transmitter is one of the nodes in the GPP; and 3) the cooperative model, where the nodes in a GPP Cluster transmit cooperatively. The simulation results indicate that a significant gain can be achieved with cooperation.

  • ISIT - Success probabilities in Gauss-Poisson networks with and without cooperation
    2014 IEEE International Symposium on Information Theory, 2014
    Co-Authors: Yi Zhong, Martin Haenggi, Wenyi Zhang
    Abstract:

    Gauss-Poisson processes (GPPs) are a class of Clustered Point processes, which include the Poisson Point process as a special case and have a simpler structure than general Poisson Cluster Point processes. In this paper, we propose the GPP as a model for wireless networks that exhibit Clustering behavior. We calculate the success probabilities and provide their bounds for three kinds of GPP networks: (1) the basic model where the desired transmitter is independent of the GPP and all nodes in the GPP are interferers; (2) the non-cooperative model where the desired transmitter is one of the nodes in the GPP; (3) the cooperative model where both nodes in a two-node Cluster of the GPP serve a receiver cooperatively using non-coherent joint transmission. Our results show that the bounds, especially the upper bounds, provide useful approximations that well fit the actual success probability for different operating regimes.

  • Success probabilities in Gauss-Poisson networks with and without cooperation
    2014 IEEE International Symposium on Information Theory, 2014
    Co-Authors: Yi Zhong, Martin Haenggi, Wenyi Zhang
    Abstract:

    Gauss-Poisson processes (GPPs) are a class of Clustered Point processes, which include the Poisson Point process as a special case and have a simpler structure than general Poisson Cluster Point processes. In this paper, we propose the GPP as a model for wireless networks that exhibit Clustering behavior. We calculate the success probabilities and provide bounds for three kinds of GPP networks: (1) the basic model where the desired transmitter is independent of the GPP and all nodes in the GPP are interferers; (2) the non-cooperative model where the desired transmitter is one of the nodes in the GPP; (3) the cooperative model where both nodes in a two-node Cluster of the GPP serve a receiver cooperatively using non-coherent joint transmission. Our results show that the bounds, especially the upper bounds, provide good approximations for different operating regimes.

Jakob Gulddahl Rasmussen - One of the best experts on this subject based on the ideXlab platform.

  • the cylindrical k function and poisson line Cluster Point processes
    Biometrika, 2016
    Co-Authors: Jesper Møller, Farzaneh Safavimanesh, Jakob Gulddahl Rasmussen
    Abstract:

    The analysis of Point patterns with linear structures is of interest in many applications. To detect anisotropy in such patterns, in particular in the case of a columnar structure, we introduce a functional summary statistic, the cylindrical $K$-function, which is a directional $K$-function whose structuring element is a cylinder. We further introduce a class of anisotropic Cox Point processes, called Poisson line Cluster Point processes. The Points of such a process are random displacements of Poisson Point processes defined on the lines of a Poisson line process. Parameter estimation for this model based on moment methods or Bayesian inference is discussed in the case where the underlying Poisson line process is latent. To illustrate the proposed methods, we analyse two- and three-dimensional Point pattern datasets. The three-dimensional dataset is of particular interest as it relates to the minicolumn hypothesis in neuroscience, which claims that pyramidal and other brain cells have a columnar arrangement perpendicular to the surface of the brain.

  • the cylindrical k function and poisson line Cluster Point processes
    arXiv: Statistics Theory, 2015
    Co-Authors: Jesper Møller, Farzaneh Safavimanesh, Jakob Gulddahl Rasmussen
    Abstract:

    Analyzing Point patterns with linear structures has recently been of interest in e.g. neuroscience and geography. To detect anisotropy in such cases, we introduce a functional summary statistic, called the cylindrical $K$-function, since it is a directional $K$-function whose structuring element is a cylinder. Further we introduce a class of anisotropic Cox Point processes, called Poisson line Cluster Point processes. The Points of such a process are random displacements of Poisson Point processes defined on the lines of a Poisson line process. Parameter estimation based on moment methods or Bayesian inference for this model is discussed when the underlying Poisson line process and the Cluster memberships are treated as hidden processes. To illustrate the methodologies, we analyze a two and a three-dimensional Point pattern data set. The 3D data set is of particular interest as it relates to the minicolumn hypothesis in neuroscience, claiming that pyramidal and other brain cells have a columnar arrangement perpendicular to the pial surface of the brain.

  • Spatial Cluster Point processes related to Poisson-Voronoi tessellations
    Stochastic Environmental Research and Risk Assessment, 2014
    Co-Authors: Jesper Møller, Jakob Gulddahl Rasmussen
    Abstract:

    We discuss how to construct models for Cluster Point processes within ‘territories’ modelled by \(d\)-dimensional Voronoi cells whose nuclei are generated by a latent Poisson process (where the planar case \(d=2\) is of our primary interest). Conditional on the territories/cells, the Clusters are independent Poisson processes whose Points may be aggregated around or away from the nuclei and along or away from the boundaries of the cells. Observing the superposition of Clusters within a bounded region, we discuss how to account for edge effects. Bayesian inference for a particular flexible model is discussed in connection to a botanical example.

  • A Sequential Point Process Model and Bayesian Inference for Spatial Point Patterns with Linear Structures
    Scandinavian Journal of Statistics, 2012
    Co-Authors: Jesper Møller, Jakob Gulddahl Rasmussen
    Abstract:

    We introduce a flexible spatial Point process model for spatial Point patterns exhibiting linear structures, without incorporating a latent line process. The model is given by an underlying sequential Point process model, i.e. each new Point is generated given the previous Points. Under this model the Points can be of one of three types: a ‘background Point’, an ‘independent Cluster Point’, or a ‘dependent Cluster Point’. The background and independent Cluster Points are thought to exhibit ‘complete spatial randomness’, while the conditional distribution of a dependent Cluster Point given the previous Points is such that the dependent Cluster Point is likely to occur closely to a previous Cluster Point. We demonstrate the flexibility of the model for producing Point patterns with linear structures, and propose to use the model as the likelihood in a Bayesian setting when analyzing a spatial Point pattern exhibiting linear structures but where the exact mechanism responsible for the formations of lines is unknown. We illustrate this methodology by analyzing two spatial Point pattern data sets (locations of bronze age graves in Denmark and locations of mountain tops in Spain) without knowing which Points are background Points, independent Cluster Points, and dependent Cluster Points.

Robert W. Heath - One of the best experts on this subject based on the ideXlab platform.

  • MIMO Interference Alignment in Random Access Networks
    IEEE Transactions on Communications, 2013
    Co-Authors: Behrang Nosrat-makouei, Radha Krishna Ganti, Jeffrey G. Andrews, Robert W. Heath
    Abstract:

    In this paper, we analyze a multiple-input multiple-output (MIMO) interference channel where nodes are randomly distributed on a plane as a spatial Poisson Cluster Point process. A Poisson Cluster Point process consists of Clusters with fixed number of Points randomly distributed as with the Cluster centers distributed randomly on the plane. The nodes in each Cluster use interference alignment (IA) to suppress intra-Cluster interference but unlike most work on IA, we do not neglect inter-Cluster interference. We also connect the accuracy of channel state information to the distance between the nodes, i.e., the quality of CSI degrades with increasing distance. Accounting for the training and feedback overhead, we derive the transmission capacity of this MIMO IA ad hoc network and then compare it to open-loop (interference-blind) spatial multiplexing. Finally, we present exemplary system setups where spatial multiplexing outperforms IA due to the imperfect channel state information or the non-aligned inter-Cluster interference.

  • MIMO interference alignment in random access networks
    2011 Conference Record of the Forty Fifth Asilomar Conference on Signals Systems and Computers (ASILOMAR), 2011
    Co-Authors: Behrang Nosrat-makouei, Jeffrey G. Andrews, Robert W. Heath, Radha Krishna Ganti
    Abstract:

    In this paper we analyze a multiple-input-multiple-output interference channel where nodes randomly distributed on a plane utilize interference alignment to reduce the Point-to-Point outage. We model the spatial distribution of the nodes as a spatial Poisson Cluster Point process with equal sized Clusters. Each Cluster uses intra-Cluster interference alignment to suppress interference. We link the accuracy of channel state information to the distance between the nodes, i.e., for a fixed SNR, the quality of CSI degrades with increasing distance. Accounting for the inter-Cluster unaligned interference, we compare intra-Cluster interference alignment with open-loop spatial multiplexing. In our analysis we find common system setups where the benefits of using interference alignment over spatial multiplexing degrade the most due to the imperfect channel state information.