Optimizing Quantize-Map-and-Forward relaying for Gaussian diamond networks

Sengupta, Ayan; Wang, I-Hsiang; Fragouli, Christina

doi:10.1109/itw.2012.6404698

Cited by 22 publications

(34 citation statements)

References 7 publications

(24 reference statements)

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“…using ∆ = K 1 − 1 as in [11]. From Theorem 3.1, we can straightaway conclude the concavity of (12), since the only additional terms,…”

Section: Using R Nnc Instead Ofc Iid For Optimizationmentioning

confidence: 81%

“…It is important to note that the achievable rate for the NNC scheme in [11] using i.i.d Gaussian vector quantizers of distortion ∆ at each node is fundamentally related to thē C iid expression, while additionally factoring in the quantization loss. The NNC rate for our network as per [11] is given by…”

Section: B Capacity Outer Bounds and Rate Expressionsmentioning

confidence: 99%

“…We resort instead to approximate schemes, such as Quantize-Map-and-Forward (QMF) [2] and Noisy Network Coding (NNC) [7], that are proved to achieve all rates within a constant additive gap (independent of the channels in the network) from the cutset upper bound on capacity for arbitrary single-source singledestination wireless relay networks. We are encouraged in this choice by the fact that initial concerns about the additive gap affecting QMF/NNC performance at moderate SNRs have also been addressed in [11], [4], with appropriate choices of quantizer distortions demonstrating excellent moderate SNR performance. We thus focus our attention on QMF/NNC-based relay networks and hence, use the achievable rate expressions for these schemes [7], or indeed their close siblings-the cutset upper bound expressions on network capacity (which differ from the QMF/NNC rates by a mere constant), as the network objective functions for our problem.…”

Section: Introductionmentioning

confidence: 99%

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Efficient subnetwork selection in relay networks

Brahma

Sengupta

Fragouli

2014

2014 IEEE International Symposium on Information Theory

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Abstract-We consider a source that would like to communicate with a destination over a layered Gaussian relay network. We present a computationally efficient method that enables to select a near-optimal (in terms of throughput) subnetwork of a given size connecting the source with the destination. Our method starts by formulating an integer optimization problem that maximizes the rates that the Quantize-Map-and-Forward relaying protocol can achieve over a selected subnetwork; we then relax the integer constraints to obtain a non-linear optimization over reals. For diamond networks, we prove that this optimization over reals is concave, while for general layered networks we give empirical demonstrations of near-concavity, paving the way for efficient algorithms to solve the relaxed problem. We then round the relaxed solution to select a specific subnetwork. Simulations using off-the-shelf non-linear optimization algorithms demonstrate excellent performance with respect to the true integer optimum for both diamond networks as well as multi-layered networks. Even with these non-customized algorithms, significant time savings are observed vis-à-vis exhaustive integer optimization 1 .

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“…using ∆ = K 1 − 1 as in [11]. From Theorem 3.1, we can straightaway conclude the concavity of (12), since the only additional terms,…”

Section: Using R Nnc Instead Ofc Iid For Optimizationmentioning

confidence: 81%

Section: B Capacity Outer Bounds and Rate Expressionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient subnetwork selection in relay networks

Brahma

Sengupta

Fragouli

2014

2014 IEEE International Symposium on Information Theory

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“…For the Relay Channel, a decoder for QMF has been studied in [7] with Low Density Parity Check (LDPC) and Low Density Generator Matrix (LDGM) codes. For the diamond channel, an iterative message passing decoder for QMF has been proposed in [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Iterative decoding for Noisy Network Coding for Two-Way Relay channels

Heindlmaier

Staudacher²

2016

2016 9th International Symposium on Turbo Codes and Iterative Information Processing (ISTC)

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Abstract-This paper presents a practical implementation of Noisy Network Coding (NNC) for half-duplex Two-Way Relay channels (TWRC). With Noisy Network Coding, the relay quantizes the incoming signals and forwards them digitally. The receivers do not try to recover the relay quantization index but the codewords that are embedded in the quantized signal. Our approach uses irregular repeat-accumulate low-density parity check (LDPC) codes to implement NNC. We derive the joint factor graph and the message passing rules for the corresponding joint iterative decoding scheme. Our simulation results confirm the performance advantages predicted by random coding arguments.

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“…The closeness here is in the sense that the gap between the true capacity and the cutset bound can be bounded independent of the channel SNRs and the topology of the network. The gap is linear in the number of nodes in the network, however more recent results in [5], [6], [7], [8] show that for many network topologies the gap can be made much smaller than linear in the number of nodes. Motivated by these results, we use the cutset bound under i.i.d.…”

Section: Introductionmentioning

confidence: 99%

Fast near-optimal subnetwork selection in layered relay networks

Kolte

Özgür

2014

2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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We consider the problem of finding the largest capacity subnetwork of a given size of a layered Gaussian relay network. While the exact capacity of Gaussian relay networks is unknown in general, motivated by recent capacity approximations we use the information-theoretic cutset bound as a proxy for the true capacity of such networks. There are two challenges in efficiently selecting subnetworks of a Gaussian network. First, evaluating the cutset bound involves a minimization of a cut function over the exponentially many possible cuts of the network and therefore a greedy approach has exponential complexity. Second, even if the min-cut for each subnetwork can be evaluated efficiently, an exhaustive search over the possibly exponentially many subnetworks of a network has prohibitive complexity. Algorithms exploiting the submodularity property of the cut function have been proposed in the literature to address these challenges. Instead, in this paper, we develop algorithms for computing the min-cut of a layered network and selecting its largest capacity subnetwork which are based on the observation that the cut function of a layered network admits a line-structured factor graph representation. We demonstrate numerically that our algorithms exploiting the layered structure can be significantly more efficient than the earlier algorithms exploiting submodularity. Our findings suggest that while submodularity of the cut function holds in more generality independent of the topology of the network, in the case of layered networks, algorithms exploiting the layered structure of the cut function can be much more efficient.

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Optimizing Quantize-Map-and-Forward relaying for Gaussian diamond networks

Cited by 22 publications

References 7 publications

Efficient subnetwork selection in relay networks

Efficient subnetwork selection in relay networks

Iterative decoding for Noisy Network Coding for Two-Way Relay channels

Fast near-optimal subnetwork selection in layered relay networks

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