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150
Wireless Network Information Flow: A Deterministic Approach
, 2009
"... In contrast to wireline networks, not much is known about the flow of information over wireless networks. The main barrier is the complexity of the signal interaction in wireless channels in addition to the noise in the channel. A widely accepted model is the the additive Gaussian channel model, and ..."
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Cited by 296 (42 self)
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In contrast to wireline networks, not much is known about the flow of information over wireless networks. The main barrier is the complexity of the signal interaction in wireless channels in addition to the noise in the channel. A widely accepted model is the the additive Gaussian channel model, and for this model, the capacity of even a network with a single relay node is open for 30 years. In this paper, we present a deterministic approach to this problem by focusing on the signal interaction rather than the noise. To this end, we propose a deterministic channel model which is analytically simpler than the Gaussian model but still captures two key wireless channel properties of broadcast and superposition. We consider a model for a wireless relay network with nodes connected by such deterministic channels, and present an exact characterization of the endtoend capacity when there is a single source and one or more destinations (all interested in the same information) and an arbitrary number of relay nodes. This result is a natural generalization of the celebrated maxflow mincut theorem for wireline networks. We then use the insights obtained from the analysis of the deterministic model to study information flow for the Gaussian wireless relay network. We present an achievable rate for general Gaussian relay networks and show that it is within a constant number of bits from the cutset bound on the capacity of these networks. This constant depends on the number of nodes in the network, but not the values of the channel gains or the signaltonoise ratios. We show that existing strategies cannot achieve such a constantgap approximation for arbitrary networks and propose a new quantizemapandforward scheme that does. We also give several extensions of the approximation framework including robustness results (through compound channels), halfduplex constraints and ergodic channel variations.
Joint Physical Layer Coding and Network Coding for BiDirectional Relaying
 45th Annual Allerton Conference on Communication, Control and Computing
, 2007
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Computeandforward: Harnessing interference through structured codes
 IEEE TRANS. INF. THEORY
, 2009
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The approximate capacity of the manytoone and onetomany Gaussian interference channels
 in Proc. Allerton Conf. Commun. Control Comput
, 2007
"... region of the twouser Gaussian interference channel to within 1 bit/s/Hz. A natural goal is to apply this approach to the Gaussian interference channel with an arbitrary number of users. We make progress towards this goal by finding the capacity region of the manytoone and onetomany Gaussian in ..."
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Cited by 134 (9 self)
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region of the twouser Gaussian interference channel to within 1 bit/s/Hz. A natural goal is to apply this approach to the Gaussian interference channel with an arbitrary number of users. We make progress towards this goal by finding the capacity region of the manytoone and onetomany Gaussian interference channels to within a constant number of bits. The result makes use of a deterministic model to provide insight into the Gaussian channel. The deterministic model makes explicit the dimension of signal level. A central theme emerges: the use of lattice codes for alignment of interfering signals on the signal level. Index Terms—Capacity, interference alignment, interference channel, lattice codes, multiuser channels. I.
Gossip algorithms for distributed signal processing
 PROCEEDINGS OF THE IEEE
, 2010
"... Gossip algorithms are attractive for innetwork processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the co ..."
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Cited by 116 (30 self)
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Gossip algorithms are attractive for innetwork processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This paper presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmittedmessages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.
Ergodic interference alignment
 in Proceedings of the International Symposium on Information Theory (ISIT 2009), (Seoul, South Korea
, 2009
"... Abstract—Consider a Kuser interference channel with timevarying fading. At any particular time, each receiver will see a signal from most transmitters. The standard approach to such a scenario results in each transmitterreceiver pair achieving a rate proportional to 1 the single user rate. However ..."
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Cited by 96 (24 self)
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Abstract—Consider a Kuser interference channel with timevarying fading. At any particular time, each receiver will see a signal from most transmitters. The standard approach to such a scenario results in each transmitterreceiver pair achieving a rate proportional to 1 the single user rate. However, given two K well chosen time indices, the channel coefficients from interfering users can be made to exactly cancel. By adding up these two signals, the receiver can see an interferencefree version of the desired transmission. We show that this technique allows each user to achieve at least half its interferencefree ergodic capacity at any SNR. Prior work was only able to show that half the interferencefree rate was achievable as the SNR tended to infinity. We examine a finite field channel model and a Gaussian channel model. In both cases, the achievable rate region has a simple description and, in the finite field case, we prove it is the ergodic capacity region. I.
Channel Coding and Decoding in a Relay System Operated with PhysicalLayer Network Coding
 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
, 2009
"... This paper investigates linkbylink channelcoded PNC (Physical layer Network Coding), in which a critical process at the relay is to transform the superimposed channelcoded packets received from the two end nodes (plus noise), Y3 = X1 + X2+W3, to the networkcoded combination of the source packet ..."
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Cited by 56 (8 self)
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This paper investigates linkbylink channelcoded PNC (Physical layer Network Coding), in which a critical process at the relay is to transform the superimposed channelcoded packets received from the two end nodes (plus noise), Y3 = X1 + X2+W3, to the networkcoded combination of the source packets, S1 ⊕ S2. This is in contrast to the traditional multipleaccess problem, in which the goal is to obtain both S1 and S2 explicitly at the relay node. Trying to obtain S1 and S2 explicitly is an overkill if we are only interested in S1⊕S2. In this paper, we refer to the transformation Y3 → S1 ⊕ S2 as the ChanneldecodingNetworkCoding process (CNC) in that it involves both channel decoding and network coding operations. This paper shows that if we adopt the Repeat Accumulate (RA) channel code at the two end nodes, then there is a compatible decoder at the relay that can perform the transformation Y3 → S1 ⊕S2 efficiently. Specifically, we redesign the belief propagation decoding algorithm of the RA code for traditional pointtopoint channel to suit the need of the PNC multipleaccess channel. Simulation results show that our new scheme outperforms the previously proposed schemes significantly in terms of BER without added complexity.
Reliable physical layer network coding
 PROCEEDINGS OF THE IEEE
, 2011
"... When two or more users in a wireless network transmit simultaneously, their electromagnetic signals are linearly superimposed on the channel. As a result, a receiver that is interested in one of these signals sees the others as unwanted interference. This property of the wireless medium is typicall ..."
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Cited by 54 (5 self)
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When two or more users in a wireless network transmit simultaneously, their electromagnetic signals are linearly superimposed on the channel. As a result, a receiver that is interested in one of these signals sees the others as unwanted interference. This property of the wireless medium is typically viewed as a hindrance to reliable communication over a network. However, using a recently developed coding strategy, interference can in fact be harnessed for network coding. In a wired network, (linear) network coding refers to each intermediate node taking its received packets, computing a linear combination over a finite field, and forwarding the outcome towards the destinations. Then, given an appropriate set of linear combinations, a destination can solve for its desired packets. For certain topologies, this strategy can attain significantly higher throughputs over routingbased strategies. Reliable physical layer network coding takes this idea one step further: using judiciously chosen linear errorcorrecting codes, intermediate nodes in a wireless network can directly recover linear combinations of the packets from the observed noisy superpositions of transmitted signals. Starting with some simple examples, this survey explores the core ideas behind this new technique and the possibilities it offers for communication over interferencelimited wireless networks.
The case for structured random codes in network capacity theorems
 in Proceedings of the IEEE Information Theory Workshop (ITW 2007), (Lake Tahoe, CA
, 2007
"... Random coding arguments are the backbone of most channel capacity achievability proofs. In this paper, we show that in their standard form, such arguments are insufficient for proving some network capacity theorems: structured coding arguments, such as random linear or lattice codes, attain higher r ..."
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Cited by 52 (10 self)
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Random coding arguments are the backbone of most channel capacity achievability proofs. In this paper, we show that in their standard form, such arguments are insufficient for proving some network capacity theorems: structured coding arguments, such as random linear or lattice codes, attain higher rates. Historically, structured codes have been studied as a stepping stone to practical constructions. However, Körner and Marton demonstrated their usefulness for capacity theorems through the derivation of the optimal rate region of a distributed functional source coding problem. Here, we use multicasting over finite field and Gaussian multipleaccess networks as canonical examples to demonstrate that even if we want to send bits over a network, structured codes succeed where simple random codes fail. Beyond network coding, we also consider distributed computation over noisy channels and a special relaytype problem. I.
An Algebraic Approach to PhysicalLayer Network Coding
 IEEE TRANS. INFORM. THEORY
, 2010
"... The problem of designing new physicallayer network coding (PNC) schemes via lattice partitions is considered. Building on recent work by Nazer and Gastpar, who demonstrated its asymptotic gain using informationtheoretic tools, we take an algebraic approach to show its potential in nonasymptotic ..."
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Cited by 41 (4 self)
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The problem of designing new physicallayer network coding (PNC) schemes via lattice partitions is considered. Building on recent work by Nazer and Gastpar, who demonstrated its asymptotic gain using informationtheoretic tools, we take an algebraic approach to show its potential in nonasymptotic settings. We first relate NazerGastpar’s approach to the fundamental theorem of finitely generated modules over a principle ideal domain. Based on this connection, we generalize their code construction and simplify their encoding and decoding methods. This not only provides a transparent understanding of their approach, but more importantly, it opens up the opportunity to design efficient and practical PNC schemes. Finally, we apply our framework to the Gaussian relay network and demonstrate its advantage over conventional PNC schemes.