Wireless Network Information Flow: A Deterministic Approach

By Amir Salman Avestimehr, Suhas N. Diggavi, David Tse. IEEE Transactions on Information Theory, 2009

883 citations recorded by the source corpus.

In a wireless network with a single source and a single destination and an arbitrary number of relay nodes, what is the maximum rate of information flow achievable? We make progress on this long standing problem through a two-step approach. First, we propose a deterministic channel model which captures the key wireless properties of signal strength, broadcast and superposition. We obtain an exact characterization of the capacity of a network with nodes connected by such deterministic channels. This result is a natural generalization of the celebrated max-flow min-cut theorem for wired networks. Second, we use the insights obtained from the deterministic analysis to design a new quantize-map-and-forward scheme for Gaussian networks. In this scheme, each relay quantizes the received signal at the noise level and maps it to a random Gaussian codeword for forwarding, and the final destination decodes the source's message based on the received signal. We show that, in contrast to existing schemes, this scheme can achieve the cut-set upper bound to within a gap which is independent of the channel parameters. In the case of the relay channel with a single relay as well as the two-relay Gaussian diamond network, the gap is 1 bit/s/Hz. Moreover, the scheme is universal in the sense that the relays need no knowledge of the values of the channel parameters to (approximately) achieve the rate supportable by the network. We also present extensions of the results to multicast networks, half-duplex networks, and ergodic networks.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4c94b9cc12a213a96e4a17999cb971c884fa4126

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Citations in the corpus

Real Interference Alignment: Exploiting the Potential of Single Antenna Systems (2009, result): “As compared to the signal-strength deterministic model [27], the lower-triangular deterministic model admits a wider range of achievable schemes, including real interference alignment.”
Topological Interference Management Through Index Coding (2013, background): “To the extent that the differences in strong versus weak signal strengths have been considered through generalized degrees of freedom (GDoF) studies [19], [20], ADT style layered deterministic models [21], [22], partially connected Wyner-type models [23]–[25], and low-complexity achievable schemes [”
Interference Alignment: A New Look at Signal Dimensions in a Communication Network (2011, background): “On the other hand, deterministic models of [3] may not be as useful when multiple antennas are involved.”