The Experts below are selected from a list of 6342 Experts worldwide ranked by ideXlab platform
Jeffrey G. Andrews - One of the best experts on this subject based on the ideXlab platform.
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Downlink cellular network analysis with a dual-slope path Loss model
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
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
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.
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heterogeneous cellular networks with flexible cell association a comprehensive downlink sinr analysis
2012Co-Authors: Young Jin Sang, Ping Xia, Jeffrey G. AndrewsAbstract:In this paper we develop a tractable framework for SINR analysis in downlink heterogeneous cellular networks (HCNs) with flexible cell association policies. The HCN is modeled as a multi-tier cellular network where each tier's base stations (BSs) are randomly located and have a particular transmit power, path Loss Exponent, spatial density, and bias towards admitting mobile users. For example, as compared to macrocells, picocells would usually have lower transmit power, higher path Loss Exponent (lower antennas), higher spatial density (many picocells per macrocell), and a positive bias so that macrocell users are actively encouraged to use the more lightly loaded picocells. In the present paper we implicitly assume all base stations have full queues; future work should relax this. For this model, we derive the outage probability of a typical user in the whole network or a certain tier, which is equivalently the downlink SINR cumulative distribution function. The results are accurate for all SINRs, and their expressions admit quite simple closed-forms in some plausible special cases. We also derive the average ergodic rate of the typical user, and the minimum average user throughput - the smallest value among the average user throughputs supported by one cell in each tier. We observe that neither the number of BSs or tiers changes the outage probability or average ergodic rate in an interference-limited full-loaded HCN with unbiased cell association (no biasing), and observe how biasing alters the various metrics.
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Heterogeneous cellular networks with flexible cell association: A comprehensive downlink SINR analysis
2012Co-Authors: Han Shin Jo, Ping Xia, Young Jin Sang, Jeffrey G. AndrewsAbstract:In this paper we develop a tractable framework for SINR analysis in downlink heterogeneous cellular networks (HCNs) with flexible cell association policies. The HCN is modeled as a multi-tier cellular network where each tier's base stations (BSs) are randomly located and have a particular transmit power, path Loss Exponent, spatial density, and bias towards admitting mobile users. For example, as compared to macrocells, picocells would usually have lower transmit power, higher path Loss Exponent (lower antennas), higher spatial density (many picocells per macrocell), and a positive bias so that macrocell users are actively encouraged to use the more lightly loaded picocells. In the present paper we implicitly assume all base stations have full queues; future work should relax this. For this model, we derive the outage probability of a typical user in the whole network or a certain tier, which is equivalently the downlink SINR cumulative distribution function. The results are accurate for all SINRs, and their expressions admit quite simple closed-forms in some plausible special cases. We also derive the \emph{average ergodic rate} of the typical user, and the \emph{minimum average user throughput} -- the smallest value among the average user throughputs supported by one cell in each tier. We observe that neither the number of BSs or tiers changes the outage probability or average ergodic rate in an interference-limited full-loaded HCN with unbiased cell association (no biasing), and observe how biasing alters the various metrics.
Young Jin Sang - One of the best experts on this subject based on the ideXlab platform.
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heterogeneous cellular networks with flexible cell association a comprehensive downlink sinr analysis
2012Co-Authors: Young Jin Sang, Ping Xia, Jeffrey G. AndrewsAbstract:In this paper we develop a tractable framework for SINR analysis in downlink heterogeneous cellular networks (HCNs) with flexible cell association policies. The HCN is modeled as a multi-tier cellular network where each tier's base stations (BSs) are randomly located and have a particular transmit power, path Loss Exponent, spatial density, and bias towards admitting mobile users. For example, as compared to macrocells, picocells would usually have lower transmit power, higher path Loss Exponent (lower antennas), higher spatial density (many picocells per macrocell), and a positive bias so that macrocell users are actively encouraged to use the more lightly loaded picocells. In the present paper we implicitly assume all base stations have full queues; future work should relax this. For this model, we derive the outage probability of a typical user in the whole network or a certain tier, which is equivalently the downlink SINR cumulative distribution function. The results are accurate for all SINRs, and their expressions admit quite simple closed-forms in some plausible special cases. We also derive the average ergodic rate of the typical user, and the minimum average user throughput - the smallest value among the average user throughputs supported by one cell in each tier. We observe that neither the number of BSs or tiers changes the outage probability or average ergodic rate in an interference-limited full-loaded HCN with unbiased cell association (no biasing), and observe how biasing alters the various metrics.
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Heterogeneous cellular networks with flexible cell association: A comprehensive downlink SINR analysis
2012Co-Authors: Han Shin Jo, Ping Xia, Young Jin Sang, Jeffrey G. AndrewsAbstract:In this paper we develop a tractable framework for SINR analysis in downlink heterogeneous cellular networks (HCNs) with flexible cell association policies. The HCN is modeled as a multi-tier cellular network where each tier's base stations (BSs) are randomly located and have a particular transmit power, path Loss Exponent, spatial density, and bias towards admitting mobile users. For example, as compared to macrocells, picocells would usually have lower transmit power, higher path Loss Exponent (lower antennas), higher spatial density (many picocells per macrocell), and a positive bias so that macrocell users are actively encouraged to use the more lightly loaded picocells. In the present paper we implicitly assume all base stations have full queues; future work should relax this. For this model, we derive the outage probability of a typical user in the whole network or a certain tier, which is equivalently the downlink SINR cumulative distribution function. The results are accurate for all SINRs, and their expressions admit quite simple closed-forms in some plausible special cases. We also derive the \emph{average ergodic rate} of the typical user, and the \emph{minimum average user throughput} -- the smallest value among the average user throughputs supported by one cell in each tier. We observe that neither the number of BSs or tiers changes the outage probability or average ergodic rate in an interference-limited full-loaded HCN with unbiased cell association (no biasing), and observe how biasing alters the various metrics.
Abdelhak M. Zoubir - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Cooperative Localization Using Received Signal Strength With Unknown Path Loss Exponent: Message Passing Approaches
2020Co-Authors: Carsten Fritsche, Fredrik Gustafsson, Abdelhak M. ZoubirAbstract:We propose a Bayesian framework for the received-signal-strength-based cooperative localization problem with unknown path Loss Exponent. Our purpose is to infer the marginal posterior of each unknown parameter: the position or the path Loss Exponent. This probabilistic inference problem is solved using message passing algorithms that update messages and beliefs iteratively. For numerical tractability, we combine the variable discretization and Monte-Carlo-based numerical approximation schemes. To further improve computational efficiency, we develop an auxiliary importance sampler that updates the beliefs with the help of an auxiliary variable. An important ingredient of the proposed auxiliary importance sampler is the ability to sample from a normalized likelihood function. To this end, we develop a stochastic sampling strategy that mathematically interprets and corrects an existing heuristic strategy. The proposed message passing algorithms are analyzed systematically in terms of computational complexity, demonstrating the computational efficiency of the proposed auxiliary importance sampler. Various simulations are conducted to validate the overall good performance of the proposed algorithms.
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bayesian cooperative localization using received signal strength with unknown path Loss Exponent message passing approaches
2019Co-Authors: Carsten Fritsche, Fredrik Gustafsson, Abdelhak M. ZoubirAbstract:We propose a Bayesian framework for the received-signal-strength-based cooperative localization problem with unknown path Loss Exponent. Our purpose is to infer the marginal posterior of each unknown parameter: the position or the path Loss Exponent. This probabilistic inference problem is solved using message passing algorithms that update messages and beliefs iteratively. To enable the numerical tractability, we combine the variable discretization and Monte-Carlo-based numerical approximation schemes. To further improve computational efficiency, we develop an auxiliary importance sampler that updates the beliefs with the help of an auxiliary variable. To sample from a normalized likelihood function, which is an important ingredient of the proposed auxiliary importance sampler, we develop a stochastic sampling strategy that mathematically interprets and corrects an existing heuristic strategy. The proposed message passing algorithms are analyzed systematically in terms of computational complexity, demonstrating the computational efficiency of the proposed auxiliary importance sampler. Various simulations are conducted to validate the overall good performance of the proposed algorithms.
Xinchen Zhang - One of the best experts on this subject based on the ideXlab platform.
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Downlink cellular network analysis with a dual-slope path Loss model
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
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
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.
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the performance of successive interference cancellation in random wireless networks
2012Co-Authors: Xinchen Zhang, Martin HaenggiAbstract:This paper provides a unified framework to study the performance gain of successive interference cancellation (SIC) in d-dimensional interference-limited networks with arbitrary fading distribution and power-law path Loss. We derive bounds on the mean number of users that can be successively decoded and the probability of successively decoding k users. Our results suggest that, without power control, the marginal benefit of enabling the receiver to successively decode k users diminishes very fast with k, especially in networks of high dimensions and small path Loss Exponent. On the other hand, SIC is more beneficial when the users are clustered around the receiver, or very low-rate codes are used.
Stavros Kotsopoulos - One of the best experts on this subject based on the ideXlab platform.
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a closed form expression for outage secrecy capacity in wireless information theoretic security
2009Co-Authors: Theofilos Chrysikos, Tasos Dagiuklas, Stavros KotsopoulosAbstract:This paper provides a closed-form expression for Outage Secrecy Capacity in Wireless Information-Theoretic Security. This is accomplished on the basis of an approximation of the Exponential function via a first-order Taylor series. The error of this method is calculated for two different channel cases, and the resulting precision confirms the correctness of this approach. Thus, the Outage Secrecy Capacity can be calculated for a given Outage Probability and for a given propagation environment (path Loss Exponent, average main channel SNR), allowing us to estimate with increased precision the boundaries of secure communications.
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Impact of channel-dependent variation of path Loss Exponent on wireless information-theoretic security
2009Co-Authors: Theofilos Chrysikos, Stavros KotsopoulosAbstract:Recent published works have manifested a renewed interest in the impact of the wireless channel itself on the achievable level of secure information exchange between two or more communication nodes, in the presence of an undesired eavesdropper, developing the research field of Wireless Information-Theoretic Security (WITS). For quasi-static Rayleigh fading channels, information-theoretic security has been proven to be achievable even when the eavesdropper's channel has a better average Signal-to-Noise Ratio (SNR) than the main channel, thus bypassing the limits considered in the classic AWGN-channels model. In these works, a typical value has been assigned to the path Loss Exponent that dominates the distance ratio (faction of distance between the legitimate receiver and the transmitter to the distance between the eavesdropper and the transmitter). Extensive research has proven, however, that the path Loss Exponent value varies according to the type and the intrinsic characteristics of the wireless channel in question. In our work, we take into account this variation of the path Loss Exponent and examine its impact on the secrecy capacity and the outage probability for a given normalized secrecy rate. Our results establish a link between the different types of wireless environment and the boundaries of secure communications.
