4,144 research outputs found
Coverage prediction and optimization algorithms for indoor environments
A heuristic algorithm is developed for the prediction of indoor coverage. Measurements on one floor of an office building are performed to investigate propagation characteristics and validations with very limited additional tuning are performed on another floor of the same building and in three other buildings. The prediction method relies on the free-space loss model for every environment, this way intending to reduce the dependency of the model on the environment upon which the model is based, as is the case with many other models. The applicability of the algorithm to a wireless testbed network with fixed WiFi 802.11b/g nodes is discussed based on a site survey. The prediction algorithm can easily be implemented in network planning algorithms, as will be illustrated with a network reduction and a network optimization algorithm. We aim to provide an physically intuitive, yet accurate prediction of the path loss for different building types
Data Offloading in Load Coupled Networks: A Utility Maximization Framework
We provide a general framework for the problem of data offloading in a
heterogeneous wireless network, where some demand of cellular users is served
by a complementary network. The complementary network is either a small-cell
network that shares the same resources as the cellular network, or a WiFi
network that uses orthogonal resources. For a given demand served in a cellular
network, the load, or the level of resource usage, of each cell depends in a
non-linear manner on the load of other cells due to the mutual coupling of
interference seen by one another. With load coupling, we optimize the demand to
be served in the cellular or the complementary networks, so as to maximize a
utility function. We consider three representative utility functions that
balance, to varying degrees, the revenue from serving the users vs the user
fairness. We establish conditions for which the optimization problem has a
feasible solution and is convex, and hence tractable to numerical computations.
Finally, we propose a strategy with theoretical justification to constrain the
load to some maximum value, as required for practical implementation. Numerical
studies are conducted for both under-loaded and over-loaded networks.Comment: 12 pages, accepted for publication in IEEE Transactions on Wireless
Communication
RF channel characterization for cognitive radio using support vector machines
Cognitive Radio promises to revolutionize the ways in which a user interfaces with a communications device. In addition to connecting a user with the rest of the world, a Cognitive Radio will know how the user wants to connect to the rest of the world as well as how to best take advantage of unused spectrum, commonly called white space\u27. Through the concept of Dynamic Spectrum Acccess a Cognitive Radio will be able to take advantage of the white space in the spectrum by first identifying where the white space is located and designing a transmit plan for a particular white space. In general a Cognitive Radio melds the capabilities of a Software Defined Radio and a Cognition Engine. The Cognition Engine is responsible for learning how the user interfaces with the device and how to use the available radio resources while the SDR is the interface to the RF world. At the heart of a Cognition Engine are Machine Learning Algorithms that decide how best to use the available radio resources and can learn how the user interfaces to the CR. To decide how best to use the available radio resources, we can group Machine Learning Algorithms into three general categories which are, in order of computational cost: 1.) Linear Least Squares Type Algorithms, e.g. Discrete Fourier Transform (DFT) and their kernel versions, 2.) Linear Support Vector Machines (SVMs) and their kernel versions, and 3.) Neural Networks and/or Genetic Algorithms. Before deciding on what to transmit, a Cognitive Radio must decide where the white space is located. This research is focused on the task of identifying where the white space resides in the spectrum, herein called RF Channel Characterization. Since previous research into the use of Machine Learning Algorithms for this task has focused on Neural Networks and Genetic Algorithms, this research will focus on the use of Machine Learning Algorithms that follow the Support Vector optimization criterion for this task. These Machine Learning Algorithms are commonly called Support Vector Machines. Results obtained using Support Vector Machines for this task are compared with results obtained from using Least Squares Algorithms, most notably, implementations of the Fast Fourier Transform. After a thorough theoretical investigation of the ability of Support Vector Machines to perform the RF Channel Characterization task, we present results of using Support Vector Machines for this task on experimental data collected at the University of New Mexico.\u2
AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information
With expeditious development of wireless communications, location
fingerprinting (LF) has nurtured considerable indoor location based services
(ILBSs) in the field of Internet of Things (IoT). For most pattern-matching
based LF solutions, previous works either appeal to the simple received signal
strength (RSS), which suffers from dramatic performance degradation due to
sophisticated environmental dynamics, or rely on the fine-grained physical
layer channel state information (CSI), whose intricate structure leads to an
increased computational complexity. Meanwhile, the harsh indoor environment can
also breed similar radio signatures among certain predefined reference points
(RPs), which may be randomly distributed in the area of interest, thus mightily
tampering the location mapping accuracy. To work out these dilemmas, during the
offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI
amplitude as location fingerprint, which shares the structural simplicity of
RSS while reserving the most location-specific statistical channel information.
Moreover, an additional angle of arrival (AoA) fingerprint can be accurately
retrieved from CSI phase through an enhanced subspace based algorithm, which
serves to further eliminate the error-prone RP candidates. In the online phase,
by exploiting both CSI amplitude and phase information, a novel bivariate
kernel regression scheme is proposed to precisely infer the target's location.
Results from extensive indoor experiments validate the superior localization
performance of our proposed system over previous approaches
State-of-the-art in Power Line Communications: from the Applications to the Medium
In recent decades, power line communication has attracted considerable
attention from the research community and industry, as well as from regulatory
and standardization bodies. In this article we provide an overview of both
narrowband and broadband systems, covering potential applications, regulatory
and standardization efforts and recent research advancements in channel
characterization, physical layer performance, medium access and higher layer
specifications and evaluations. We also identify areas of current and further
study that will enable the continued success of power line communication
technology.Comment: 19 pages, 12 figures. Accepted for publication, IEEE Journal on
Selected Areas in Communications. Special Issue on Power Line Communications
and its Integration with the Networking Ecosystem. 201
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