The Experts below are selected from a list of 22959 Experts worldwide ranked by ideXlab platform
Xin Wang - One of the best experts on this subject based on the ideXlab platform.
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cross layer rate optimization for proportional fairness in multihop wireless networks with random access
IEEE Journal on Selected Areas in Communications, 2006Co-Authors: Xin WangAbstract:In this paper, we address the rate control problem in a multihop random access wireless network, with the objective of achieving proportional fairness amongst the end-to-end sessions. The problem is considered in the framework of nonlinear optimization. Compared with its counterpart in a wired network where link capacities are fixed, rate control in a multihop random access network is much more complex and requires joint optimization at both the transport and link layers. This is due to the fact that the attainable throughput on each link in the network is "elastic" and is typically a nonconvex and nonseparable function of the Transmission Attempt rates. Two cross-layer algorithms, a dual-based algorithm and a penalty-based algorithm, are proposed in this paper to solve the rate control problem in a multihop random access network. Both algorithms can be implemented in a distributed manner, and work at the link layer to adjust link Attempt probabilities and at the transport layer to adjust session rates. We prove rigorously that the two proposed algorithms converge to the globally optimal solutions. Simulation results are provided in support of our conclusions
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cross layer rate control for end to end proportional fairness in wireless networks with random access
Mobile Ad Hoc Networking and Computing, 2005Co-Authors: Xin WangAbstract:In this paper, we address the rate control problem in a multi-hop random access wireless network, with the objective of achieving proportional fairness amongst the end-to-end sessions. The problem is considered in the framework of nonlinear optimization. Compared to its counterpart in a wired network where link capacities are assumed to be fixed, rate control in a multi-hop random access network is much more complex and requires joint optimization at both the transport layer and the link layer. This is due to the fact that the attainable throughput on each link in the network is `elastic' and is typically a non-convex and non-separable function of the Transmission Attempt rates. Two cross-layer algorithms, a dual based algorithm and a primal based algorithm, are proposed in this paper to solve the rate control problem in a multi-hop random access network. Both algorithms can be implemented in a distributed manner, and work at the link layer to adjust link Attempt probabilities and at the transport layer to adjust session rates. We prove rigorously that the two proposed algorithms converge to the globally optimal solutions. Simulation results are provided to support our conclusions.
Jeffrey G Andrews - One of the best experts on this subject based on the ideXlab platform.
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multicast outage probability and Transmission capacity of multihop wireless networks
IEEE Transactions on Information Theory, 2011Co-Authors: Chunhung Liu, Jeffrey G AndrewsAbstract:Multicast Transmission, wherein the same packet must be delivered to multiple receivers, is an important aspect of sensor and tactical networks and has several distinctive traits as opposed to more commonly studied unicast networks. Specially, these include 1) identical packets must be delivered successfully to several nodes, 2) outage at any receiver requires the packet to be retransmitted at least to that receiver, and 3) the multicast rate is dominated by the receiver with the weakest link in order to minimize outage and reTransmission. A first contribution of this paper is the development of a tractable multicast model and throughput metric that captures each of these key traits in a multicast wireless network. We utilize a Poisson cluster process (PCP) consisting of a distinct Poisson point process (PPP) for the transmitters and receivers, and then define the multicast Transmission capacity (MTC) as the maximum achievable multicast rate per Transmission Attempt times the maximum intensity of multicast clusters under decoding delay and multicast outage constraints. A multicast cluster is a contiguous area over which a packet is multicasted, and to reduce outage it can be tessellated into v smaller regions of multicast. The second contribution of the paper is the analysis of several key aspects of this model, for which we develop the following main result. Assuming τ/v Transmission Attempts are allowed for each tessellated region in a multicast cluster, we show that the MTC is Θ(ρkxlog(k)vy) where ρ, x and y are functions of τ and v depending on the network size and intensity, and k is the average number of the intended receivers in a cluster. We derive {ρ, x, y} for a number of regimes of interest, and also show that an appropriate number of reTransmissions can significantly enhance the MTC.
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multicast outage probability and Transmission capacity of multihop wireless networks
arXiv: Information Theory, 2010Co-Authors: Chunhung Liu, Jeffrey G AndrewsAbstract:Multicast Transmission, wherein the same packet must be delivered to multiple receivers, is an important aspect of sensor and tactical networks and has several distinctive traits as opposed to more commonly studied unicast networks. Specially, these include (i) identical packets must be delivered successfully to several nodes, (ii) outage at any receiver requires the packet to be retransmitted at least to that receiver, and (iii) the multicast rate is dominated by the receiver with the weakest link in order to minimize outage and reTransmission. A first contribution of this paper is the development of a tractable multicast model and throughput metric that captures each of these key traits in a multicast wireless network. We utilize a Poisson cluster process (PCP) consisting of a distinct Poisson point process (PPP) for the transmitters and receivers, and then define the multicast Transmission capacity (MTC) as the maximum achievable multicast rate per Transmission Attempt times the maximum intensity of multicast clusters under decoding delay and multicast outage constraints. A multicast cluster is a contiguous area over which a packet is multicasted, and to reduce outage it can be tessellated into $v$ smaller regions of multicast. The second contribution of the paper is the analysis of several key aspects of this model, for which we develop the following main result. Assuming $\tau/v$ Transmission Attempts are allowed for each tessellated region in a multicast cluster, we show that the MTC is $\Theta(\rho k^{x}\log(k)v^{y})$ where $\rho$, $x$ and $y$ are functions of $\tau$ and $v$ depending on the network size and intensity, and $k$ is the average number of the intended receivers in a cluster. We derive $\{\rho, x, y\}$ for a number of regimes of interest, and also show that an appropriate number of reTransmissions can significantly enhance the MTC.
Chunhung Liu - One of the best experts on this subject based on the ideXlab platform.
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multicast outage probability and Transmission capacity of multihop wireless networks
IEEE Transactions on Information Theory, 2011Co-Authors: Chunhung Liu, Jeffrey G AndrewsAbstract:Multicast Transmission, wherein the same packet must be delivered to multiple receivers, is an important aspect of sensor and tactical networks and has several distinctive traits as opposed to more commonly studied unicast networks. Specially, these include 1) identical packets must be delivered successfully to several nodes, 2) outage at any receiver requires the packet to be retransmitted at least to that receiver, and 3) the multicast rate is dominated by the receiver with the weakest link in order to minimize outage and reTransmission. A first contribution of this paper is the development of a tractable multicast model and throughput metric that captures each of these key traits in a multicast wireless network. We utilize a Poisson cluster process (PCP) consisting of a distinct Poisson point process (PPP) for the transmitters and receivers, and then define the multicast Transmission capacity (MTC) as the maximum achievable multicast rate per Transmission Attempt times the maximum intensity of multicast clusters under decoding delay and multicast outage constraints. A multicast cluster is a contiguous area over which a packet is multicasted, and to reduce outage it can be tessellated into v smaller regions of multicast. The second contribution of the paper is the analysis of several key aspects of this model, for which we develop the following main result. Assuming τ/v Transmission Attempts are allowed for each tessellated region in a multicast cluster, we show that the MTC is Θ(ρkxlog(k)vy) where ρ, x and y are functions of τ and v depending on the network size and intensity, and k is the average number of the intended receivers in a cluster. We derive {ρ, x, y} for a number of regimes of interest, and also show that an appropriate number of reTransmissions can significantly enhance the MTC.
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multicast outage probability and Transmission capacity of multihop wireless networks
arXiv: Information Theory, 2010Co-Authors: Chunhung Liu, Jeffrey G AndrewsAbstract:Multicast Transmission, wherein the same packet must be delivered to multiple receivers, is an important aspect of sensor and tactical networks and has several distinctive traits as opposed to more commonly studied unicast networks. Specially, these include (i) identical packets must be delivered successfully to several nodes, (ii) outage at any receiver requires the packet to be retransmitted at least to that receiver, and (iii) the multicast rate is dominated by the receiver with the weakest link in order to minimize outage and reTransmission. A first contribution of this paper is the development of a tractable multicast model and throughput metric that captures each of these key traits in a multicast wireless network. We utilize a Poisson cluster process (PCP) consisting of a distinct Poisson point process (PPP) for the transmitters and receivers, and then define the multicast Transmission capacity (MTC) as the maximum achievable multicast rate per Transmission Attempt times the maximum intensity of multicast clusters under decoding delay and multicast outage constraints. A multicast cluster is a contiguous area over which a packet is multicasted, and to reduce outage it can be tessellated into $v$ smaller regions of multicast. The second contribution of the paper is the analysis of several key aspects of this model, for which we develop the following main result. Assuming $\tau/v$ Transmission Attempts are allowed for each tessellated region in a multicast cluster, we show that the MTC is $\Theta(\rho k^{x}\log(k)v^{y})$ where $\rho$, $x$ and $y$ are functions of $\tau$ and $v$ depending on the network size and intensity, and $k$ is the average number of the intended receivers in a cluster. We derive $\{\rho, x, y\}$ for a number of regimes of interest, and also show that an appropriate number of reTransmissions can significantly enhance the MTC.
Neelesh B. Mehta - One of the best experts on this subject based on the ideXlab platform.
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Transmit power control policies for energy harvesting sensors with reTransmissions
IEEE Journal on Selected Topics in Signal Processing, 2013Co-Authors: Anup Aprem, Chandra R. Murthy, Neelesh B. MehtaAbstract:This paper addresses the problem of finding outage-optimal power control policies for wireless energy harvesting sensor (EHS) nodes with automatic repeat request (ARQ)-based packet Transmissions. The power control policy of the EHS specifies the Transmission power for each packet Transmission Attempt, based on all the information available at the EHS. In particular, the acknowledgement (ACK) or negative acknowledgement (NACK) messages received provide the EHS with partial information about the channel state. We solve the problem of finding an optimal power control policy by casting it as a partially observable Markov decision process (POMDP). We study the structure of the optimal power policy in two ways. First, for the special case of binary power levels at the EHS, we show that the optimal policy for the underlying Markov decision process (MDP) when the channel state is observable is a threshold policy in the battery state. Second, we benchmark the performance of the EHS by rigorously analyzing the outage probability of a general fixed-power Transmission scheme, where the EHS uses a predetermined power level at each slot within the frame. Monte Carlo simulation results illustrate the performance of the POMDP approach and verify the accuracy of the analysis. They also show that the POMDP solutions can significantly outperform conventional ad hoc approaches.
Anup Aprem - One of the best experts on this subject based on the ideXlab platform.
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Transmit power control policies for energy harvesting sensors with reTransmissions
IEEE Journal on Selected Topics in Signal Processing, 2013Co-Authors: Anup Aprem, Chandra R. Murthy, Neelesh B. MehtaAbstract:This paper addresses the problem of finding outage-optimal power control policies for wireless energy harvesting sensor (EHS) nodes with automatic repeat request (ARQ)-based packet Transmissions. The power control policy of the EHS specifies the Transmission power for each packet Transmission Attempt, based on all the information available at the EHS. In particular, the acknowledgement (ACK) or negative acknowledgement (NACK) messages received provide the EHS with partial information about the channel state. We solve the problem of finding an optimal power control policy by casting it as a partially observable Markov decision process (POMDP). We study the structure of the optimal power policy in two ways. First, for the special case of binary power levels at the EHS, we show that the optimal policy for the underlying Markov decision process (MDP) when the channel state is observable is a threshold policy in the battery state. Second, we benchmark the performance of the EHS by rigorously analyzing the outage probability of a general fixed-power Transmission scheme, where the EHS uses a predetermined power level at each slot within the frame. Monte Carlo simulation results illustrate the performance of the POMDP approach and verify the accuracy of the analysis. They also show that the POMDP solutions can significantly outperform conventional ad hoc approaches.
