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233
On the capacity improvement of ad hoc wireless networks using directional antennas
 In 4th ACM MobiHoc
, 2003
"... The capacity of ad hoc wireless networks is constrained by the interference between concurrent transmissions from neighboring nodes. Gupta and Kumar have shown that the capacity of an ad hoc network does not scale well with the increasing number of nodes in the system when using omnidirectional ante ..."
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Cited by 181 (5 self)
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The capacity of ad hoc wireless networks is constrained by the interference between concurrent transmissions from neighboring nodes. Gupta and Kumar have shown that the capacity of an ad hoc network does not scale well with the increasing number of nodes in the system when using omnidirectional antennas [6]. We investigate the capacity of ad hoc wireless networks using directional antennas. In this work, we consider arbitrary networks and random networks where nodes are assumed to be static. In arbitrary networks, due to the reduction of the interfer
To Code, or Not to Code: Lossy SourceChannel Communication Revisited
 IEEE TRANS. INFORM. THEORY
, 2003
"... What makes a sourcechannel communication system optimal? It is shown that in order to achieve an optimal costdistortion tradeoff, the source and the channel have to be matched in a probabilistic sense. The match (or lack of it) involves the source distribution, the distortion measure, the channel ..."
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Cited by 160 (7 self)
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What makes a sourcechannel communication system optimal? It is shown that in order to achieve an optimal costdistortion tradeoff, the source and the channel have to be matched in a probabilistic sense. The match (or lack of it) involves the source distribution, the distortion measure, the channel conditional distribution, and the channel input cost function. Closedform necessary and sufficient expressions relating the above entities are given. This generalizes both the separationbased approach as well as the two wellknown examples of optimal uncoded communication. The condition of
On the capacity of large Gaussian relay networks
 IEEE TRANS. INF. THEORY
, 2005
"... The capacity of a particular large Gaussian relay network is determined in the limit as the number of relays tends to infinity. Upper bounds are derived from cutset arguments, and lower bounds follow from an argument involving uncoded transmission. It is shown that in cases of interest, upper and ..."
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Cited by 146 (6 self)
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The capacity of a particular large Gaussian relay network is determined in the limit as the number of relays tends to infinity. Upper bounds are derived from cutset arguments, and lower bounds follow from an argument involving uncoded transmission. It is shown that in cases of interest, upper and lower bounds coincide in the limit as the number of relays tends to infinity. Hence, this paper provides a new example where a simple cutset upper bound is achievable, and one more example where uncoded transmission achieves optimal performance. The findings are illustrated by geometric interpretations. The techniques developed in this paper are then applied to a sensor network situation. This is a network joint source–channel coding problem, and it is well known that the source–channel separation theorem does not extend to this case. The present paper extends this insight by providing an example where separating source from channel coding does not only lead to suboptimal performance—it leads to an exponential penalty in performance scaling behavior (as a function of the number of nodes). Finally, the techniques developed in this paper are extended to include certain models of ad hoc wireless networks, where a capacity scaling law can be established: When all nodes act purely as relays for a single source–destination pair, capacity grows with the logarithm of the number of nodes.
Practical Relay Networks: A Generalization of HybridARQ
 IEEE J. SEL. AREAS COMM
, 2005
"... Wireless networks contain an inherent distributed spatial diversity that can be exploited by the use of relaying. Relay networks take advantage of the broadcastoriented nature of radio and require nodebased, rather than linkbased protocols. Prior work on relay networks has studied performance li ..."
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Cited by 145 (2 self)
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Wireless networks contain an inherent distributed spatial diversity that can be exploited by the use of relaying. Relay networks take advantage of the broadcastoriented nature of radio and require nodebased, rather than linkbased protocols. Prior work on relay networks has studied performance limits either with unrealistic assumptions, complicated protocols, or only a single relay. In this paper, a practical approach to networks comprising multiple relays operating over orthogonal time slots is proposed based on a generalization of hybridautomatic repeat request (ARQ). In contrast with conventional hybridARQ, retransmitted packets do not need to come from the original source radio but could instead be sent by relays that overhear the transmission. An information theoretic framework is exposed that establishes the performance limits of such systems in a block fading environment, and numerical results are presented for some representative topologies and protocols. The results indicate a significant improvement in the energylatency tradeoff when compared with conventional multihop protocols implemented as a cascade of pointtopoint links.
The Nominal Capacity of Wireless Mesh Networks
, 2003
"... Wireless mesh networks (WMNs) are an alternative technology for lastmile broadband Internet access. In WMNs, similar to ad hoc networks, each user node operates not only as a host but also as a router; user packets are forwarded to and from an Internetconnected gateway in multihop fashion. The mes ..."
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Cited by 130 (3 self)
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Wireless mesh networks (WMNs) are an alternative technology for lastmile broadband Internet access. In WMNs, similar to ad hoc networks, each user node operates not only as a host but also as a router; user packets are forwarded to and from an Internetconnected gateway in multihop fashion. The meshed topology provides good reliability, market coverage and scalability, as well as low upfront investments. Despite the recent startup surge in WMNs, much research remains to be done before WMNs realize their full potential. This paper tackles the problem of determining the exact capacity of a WMN. The key concept that we introduce to enable this calculation is the bottleneck collision domain that is defined as the geographical area of the network that bounds from above the amount of data that can be transmitted in the network. We show that for WMNs the throughput of each node decreases as O(1/n),wheren is the total number of nodes in the network. In contrast with most existing work on ad hoc network capacity, we do not limit our study to the asymptotic case. In particular, for a given topology and the set of active nodes, we provide exact upperbounds on the throughput of any node. The calculation can be used to provision the network, to ensure quality of service and fairness, etc. The theoretical results are validated by detailed simulations.
Bounds on capacity and minimum energyperbit for AWGN relay channels
 IEEE TRANS. INF. THEORY
, 2006
"... Upper and lower bounds on the capacity and minimum energyperbit for general additive white Gaussian noise (AWGN) and frequencydivision AWGN (FDAWGN) relay channel models are established. First, the maxflow mincut bound and the generalized blockMarkov coding scheme are used to derive upper an ..."
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Cited by 108 (2 self)
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Upper and lower bounds on the capacity and minimum energyperbit for general additive white Gaussian noise (AWGN) and frequencydivision AWGN (FDAWGN) relay channel models are established. First, the maxflow mincut bound and the generalized blockMarkov coding scheme are used to derive upper and lower bounds on capacity. These bounds are never tight for the general AWGN model and are tight only under certain conditions for the FDAWGN model. Two coding schemes that do not require the relay to decode any part of the message are then investigated. First, it is shown that the “sideinformation coding scheme ” can outperform the blockMarkov coding scheme. It is also shown that the achievable rate of the sideinformation coding scheme can be improved via time sharing. In the second scheme, the relaying functions are restricted to be linear. The problem is reduced to a “singleletter ” nonconvex optimization problem for the FDAWGN model. The paper also establishes a relationship between the minimum energyperbit and capacity of the AWGN relay channel. This relationship together with the lower and upper bounds on capacity are used to establish corresponding lower and upper bounds on the minimum energyperbit that do not differ by more than a factor of 1 45 for the FDAWGN relay channel model and 1 7 for the general AWGN model.
On the Broadcast capacity in multihop wireless networks: Interplay of power, . . .
, 2007
"... In this paper we study the broadcast capacity of multihop wireless networks which we define as the maximum rate at which broadcast packets can be generated in the network such that all nodes receive the packets successfully within a given time. To asses the impact of topology and interference on t ..."
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Cited by 103 (5 self)
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In this paper we study the broadcast capacity of multihop wireless networks which we define as the maximum rate at which broadcast packets can be generated in the network such that all nodes receive the packets successfully within a given time. To asses the impact of topology and interference on the broadcast capacity we employ the Physical Model and Generalized Physical Model for the channel. Prior work was limited either by density constraints or by using the less realistic but manageable Protocol model [1], [2]. Under the Physical Model, we find that the broadcast capacity is within a constant factor of the channel capacity for a wide class of network topologies. Under the Generalized Physical Model, on the other hand, the network configuration is divided into three regimes depending on how the power is tuned in relation to network density and size and in which the broadcast capacity is asymptotically either zero, constant or unbounded. As we show, the broadcast capacity is limited by distant nodes in the first regime and by interference in the second regime. In the second regime, which covers a wide class of networks, the broadcast capacity is within a constant factor of the bandwidth.
Throughput capacity of random ad hoc networks with infrastructure support
 in MOBICOM
, 2003
"... In this paper, we consider the transport capacity of ad hoc networks with a random flat topology under the present support of an infinite capacity infrastructure network. Such a network architecture allows ad hoc nodes to communicate with each other by purely using the remaining ad hoc nodes as thei ..."
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Cited by 100 (0 self)
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In this paper, we consider the transport capacity of ad hoc networks with a random flat topology under the present support of an infinite capacity infrastructure network. Such a network architecture allows ad hoc nodes to communicate with each other by purely using the remaining ad hoc nodes as their relays. In addition, ad hoc nodes can also utilize the existing infrastructure fully or partially by reaching any access point (or gateway) of the infrastructure network in a single or multihop fashion. Using the same tools as in [1], we show that the per source node capacity of Θ(W / log(N)) can be achieved in a random network scenario with the following assumptions: (i) The number of ad hoc nodes per access point is bounded above, (ii) each wireless node, including the access points, is able to transmit at W bits/sec using a fixed transmission range, and (iii) N ad hoc nodes, excluding the access points, constitute a connected topology graph. This is a significant improvement over the capacity of random ad hoc networks with no infrastructure support which is found as Θ(W / p N log(N)) in [1]. Although better capacity figures may be obtained by complex network coding or exploiting mobility in the network, infrastructure approach provides a simpler mechanism that has more practical aspects. We also show that even when less stringent requirements are imposed on topology connectivity, a per source node capacity figure that is arbitrarily close to Θ(1) cannot be obtained. Nevertheless, under these weak conditions, we can further improve per node throughput significantly.
Sourcechannel communication in sensor networks
 LECTURE NOTES IN COMPUTER SCIENCE
, 2003
"... Sensors acquire data, and communicate this to an interested party. The arising coding problem is often split into two parts: First, the sensors compress their respective acquired signals, potentially applying the concepts of distributed source coding. Then, they communicate the compressed version to ..."
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Cited by 87 (11 self)
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Sensors acquire data, and communicate this to an interested party. The arising coding problem is often split into two parts: First, the sensors compress their respective acquired signals, potentially applying the concepts of distributed source coding. Then, they communicate the compressed version to the interested party, the goal being not to make any errors. This coding paradigm is inspired by Shannon’s separation theorem for pointtopoint communication, but it leads to suboptimal performance in general network topologies. The optimal performance for the general case is not known. In this paper, we propose an alternative coding paradigm based on joint sourcechannel coding. This coding paradigm permits to determine the optimal performance for a class of sensor networks, and shows how to achieve it. For sensor networks outside this class, we argue that the goal of the coding system could be to approach our condition for optimal performance as closely as possible. This is supported by examples for which our coding paradigm significantly outperforms the traditional separationbased coding paradigm. In particular, for a Gaussian example considered in this paper, the distortion of the best coding scheme according to the separation paradigm decreases like 1 / log M, while for our coding paradigm, it decreases like 1/M, where M is the total number of sensors.