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Two parties observing correlated data seek to exchange their data using interactive communication. How many bits must they communicate? We propose a new interactive protocol for data exchange, which increases the communication size in steps until the task is done. We also derive a lower bound on the minimum number of bits that is based on relating the data exchange problem to the secret key agreement problem. Our single-shot analysis applies to all discrete random variables and yields upper and lower bounds of a similar form. In fact, the bounds are asymptotically tight and lead to a characterization of the optimal rate of communication needed for data exchange for a general source sequence, such as a mixture of independent and identically distributed (IID) random variables as well as the optimal second-order asymptotic term in the length of communication needed for data exchange for IID random variables, when the probability of error is fixed. This gives a precise characterization of the asymptotic reduction in the length of optimal communication due to interaction; in particular, two-sided Slepian–Wolf compression is strictly suboptimal. [ABSTRACT FROM PUBLISHER]

In this paper, a compress-and-forward scheme with backward decoding is presented for the unicast wireless relay network. The encoding at the source and relay is a generalization of the noisy network coding (NNC) scheme. While it achieves the same reliable data rate as NNC scheme, the backward decoding allows for a better decoding complexity as compared with the joint decoding of the NNC scheme. Characterizing the layered decoding scheme is shown to be equivalent to characterizing an information flow for the wireless network. A node-flow for a graph with bisubmodular capacity constraints is presented and a max-flow min-cut theorem is presented. This generalizes many well-known results of flows over capacity constrained graphs studied in computer science literature. The results for the unicast relay network are generalized to the network with multiple sources with independent messages intended for a single destination. [ABSTRACT FROM AUTHOR]

The performance gain by allowing half-duplex source cooperation is studied for Gaussian interference channels. The source cooperation is in-band, meaning that each source can listen to the other source's transmission, but there is no independent (or orthogonal) channel between the sources. The half-duplex constraint supposes that at each time instant the sources can either transmit or listen, but not do both. Our main result is a characterization of the sum capacity when the cooperation is bidirectional and the channel gains are symmetric. With unidirectional cooperation, we essentially have a cognitive radio channel. By requiring the primary to achieve a rate close to its link capacity, the best possible rate for the secondary is characterized within a constant. Novel inner and outer bounds are derived as part of these characterizations. [ABSTRACT FROM AUTHOR]

We consider all-cast and multicast flow problems where either all of the nodes or only a subset of the nodes may be in session. Traffic from each node in the session has to be sent to every other node in the session. If the session does not consist of all the nodes, the remaining nodes act as relays. The nodes are connected by undirected links whose capacities are independent and identically distributed random variables. We study the asymptotics of the capacity region (with network coding) in the limit of a large number of nodes, and show that the normalized sum rate converges to a constant almost surely. We then provide a decentralized push–pull algorithm that asymptotically achieves this normalized sum rate without network coding. [ABSTRACT FROM AUTHOR]

In the function computation problem, certain nodes of an undirected graph have access to independent data, while some other nodes of the graph require certain functions of the data; this model, motivated by sensor networks and cloud computing, is the focus of this paper. We study the maximum rates at which function computation is possible on a capacitated graph; the capacities on the edges of the graph impose constraints on the communication rate. We consider a simple class of computation strategies based on Steiner-tree packing (so-called em computation trees), which does not involve block coding and has minimal delay. With a single terminal requiring function computation, computation trees are known to be optimal when the underlying graph is itself a directed tree, but have arbitrarily poor performance in general directed graphs. Our main result is that computation trees are em near optimal for a wide class of function computation requirements even at em multiple terminals in em undirected graphs. The key technical contribution involves connecting approximation algorithms for Steiner cuts in undirected graphs to the function computation problem. Furthermore, we show that existing algorithms for Steiner tree packings allow us to compute approximately optimal packings of computation trees in polynomial time. We also show a close connection between the function computation problem and a communication problem involving multiple multicasts. [ABSTRACT FROM PUBLISHER]

We study a wireless broadcast network, where a single source reliably communicates independent messages to multiple destinations, with the potential aid of relays and cooperation between destinations. The wireless nature of the medium is captured by the broadcast nature of transmissions as well as the superposition of transmitted signals plus independent Gaussian noise at the received signal at any radio. We propose a scheme that can achieve rate tuples within a constant gap away from the cut-set bound, where the constant is independent of channel coefficients and power constraints. First, for a deterministic broadcast network, we propose a new coding scheme, constructed by adopting a “receiver-centric” viewpoint, that uses quantize-and-forward relaying as an inner code concatenated with an outer Marton code for the induced deterministic broadcast channel. This scheme is shown to achieve the cut-set bound evaluated with product form distributions. This result is then lifted to the Gaussian network by using a deterministic network called the discrete superposition network as a formal quantization interface. This two-stage construction circumvents the difficulty involved in working with a vector nonlinear non-Gaussian broadcast channel that arises if we construct a similar scheme directly for the Gaussian network. [ABSTRACT FROM AUTHOR]

In this paper, the role of cooperation in managing interference—a fundamental feature of the wireless channel—is investigated by studying the two-user Gaussian interference channel where the source nodes can both transmit and receive in full duplex. The sum capacity of this channel is obtained within a gap of a constant number of bits. The coding scheme used builds up on the superposition scheme of Han and Kobayashi for the two-user interference channel without cooperation. New upperbounds on the sum capacity are also derived. The same coding scheme is shown to obtain the sum capacity of the symmetric two-user Gaussian interference channel with noiseless feedback within a constant gap. [ABSTRACT FROM AUTHOR]

Interference is a fundamental feature of the wireless channel. To better understand the role of cooperation in interference management, the two-user Gaussian interference channel where the destination nodes can cooperate by virtue of being able to both transmit and receive is studied. The sum capacity of this channel is characterized up to a constant number of bits. The coding scheme employed builds up on the superposition scheme of Han and Kobayashi for two-user interference channels without cooperation. New upperbounds to the sum capacity are also derived. [ABSTRACT FROM AUTHOR]

We determine the rate region of the quadratic Gaussian two-encoder source-coding problem. This rate region is achieved by a simple architecture that separates the analog and digital aspects of the compression. Furthermore, this architecture requires higher rates to send a Gaussian source than it does to send any other source with the same covariance. Our techniques can also be used to determine the sum-rate of some generalizations of this classical problem. Our approach involves coupling the problem to a quadratic Gaussian "CEO problem." [ABSTRACT FROM AUTHOR]