Deterministic Relay Networks Research Articles

Gomory-Hu Trees Over Wireless

The Gomory-Hu tree is a popular optimization algorithm that enables to efficiently find a min-cut (or equivalently, max-flow) for every pair of nodes in a graph. However, graphs cannot capture broadcasting and interference in wireless: over wireless networks we need to resort to the information-theoretical cut-set to bound the max-flow. Leveraging the submodularity of mutual information, we show that the Gomory-Hu algorithm can be used to efficiently find information-theoretic rate characterizations such as the capacity or an approximation to the capacity, over a number of network scenarios including wireless Gaussian networks and deterministic relay networks.

Using network coding to achieve the capacity of deterministic relay networks with relay messages

AbstractIn this paper, we derive the capacity of the deterministic relay networks with relay messages. We consider a network that consists of five nodes, four of which can only communicate via the fifth one. However, the fifth node is not merely a relay as it may exchange private messages with the other network nodes. First, we develop an upper bound on the capacity region based on the notion of a single‐sided genie. In the course of the achievability proof, we also derive the deterministic capacity of a four‐user relay network (without private messages at the relay). The capacity achieving schemes use a combination of two network coding techniques: the simple ordering scheme and detour scheme. In the simple ordering scheme, we order the transmitted bits at each user such that the bi‐directional messages will be received at the same channel level at the relay, while the basic idea behind the detour scheme is that some parts of the message follow an indirect paths to their respective destinations. This paper, therefore, serves to show that user cooperation and network coding can enhance throughput, even when the users are not directly connected to each other. Finally, we make a conjecture about the capacity region of the general K‐node relay network with relay messages. Copyright © 2016 John Wiley & Sons, Ltd.

Multicasting in Linear Deterministic Relay Network by Matrix Completion

We provide a deterministic polynomial time algorithm for multicasting in a linear deterministic relay network and a wireless communication framework proposed by Avestimehr et al. The running time of our algorithm is faster than existing ones and matches the current best complexity of unicast computations for each sink. Our approach is based on the polylinking flow model of Goemans et al. and the mixed matrix completion technique of Harvey et al.

Efficient Capacity Computation and Power Optimization for Relay Networks

The capacity or approximations to capacity of various single-source single-destination relay network models has been characterized in terms of the cut-set upper bound. In principle, a direct computation of this bound requires evaluating the cut capacity over exponentially many cuts. We show that the minimum cut capacity of a relay network under some special assumptions can be cast as a minimization of a submodular function, and as a result, can be computed efficiently. We use this result to show that the capacity, or an approximation to the capacity within a constant gap for the Gaussian, wireless erasure, and Avestimehr-Diggavi-Tse deterministic relay network models can be computed in polynomial time. We present some empirical results showing that computing constant-gap approximations to the capacity of Gaussian relay networks with around 300 nodes can be done in order of minutes. For Gaussian networks, cut-set capacities are also functions of the powers assigned to the nodes. We consider a family of power optimization problems and show that they can be solved in a polynomial time. In particular, we show that the minimization of the sum of powers assigned to the nodes subject to a minimum rate constraint (measured in terms of cut-set bounds) can be computed in the polynomial time. We propose a heuristic algorithm to solve this problem and measure its performance through simulations on random Gaussian networks. We observe that in the optimal allocations, most of the power is assigned to a small subset of relays, which suggests that network simplification may be possible without excessive performance degradation.

A Deterministic Polynomial-Time Algorithm for Constructing a Multicast Coding Scheme for Linear Deterministic Relay Networks

We propose a new way to construct a multicast coding scheme for linear deterministic relay networks. Our construction can be regarded as a generalization of the well-known multicast network coding scheme of Jaggi <etal xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> to linear deterministic relay networks and is based on the notion of flow for a unicast session that was introduced by the authors in earlier work. We present randomized and deterministic polynomial-time versions of our algorithm and show that for a network with <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$g$</tex></formula> destinations, our deterministic algorithm can achieve the capacity in <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\left \lceil \log (g+1)\right \rceil $</tex></formula> uses of the network and has the fastest construction time among algorithms for this problem.

Combinatiorial algorithms for wireless information flow

A long-standing open question in information theory is to characterize the unicast capacity of a wireless relay network. The difficulty arises due to the complex signal interactions induced in the network, since the wireless channel inherently broadcasts the signals and there is interference among transmissions. Recently, Avestimehr et al. [2007b] proposed a linear deterministic model that takes into account the shared nature of wireless channels, focusing on the signal interactions rather than the background noise. They generalized the min-cut max-flow theorem for graphs to networks of deterministic channels and proved that the capacity can be achieved using information theoretical tools. They showed that the value of the minimum cut is in this case the minimum rank of all the adjacency matrices describing source-destination cuts. In this article, we develop a polynomial-time algorithm that discovers the relay encoding strategy to achieve the min-cut value in linear deterministic (wireless) networks, for the case of a unicast connection. Our algorithm crucially uses a notion of linear independence between channels to calculate the capacity in polynomial time. Moreover, we can achieve the capacity by using very simple one-symbol processing at the intermediate nodes, thereby constructively yielding finite-length strategies that achieve the unicast capacity of the linear deterministic (wireless) relay network.

The multicast capacity of deterministic relay networks with no interference

The multicast capacity is determined for networks that have deterministic channels with broadcasting at the transmitters and no interference at the receivers. The multicast capacity is shown to have a cut-set interpretation. It is further shown that one cannot always layer channel and network coding in such networks. The proof of the latter result partially generalizes to discrete memoryless broadcast channels and is used to bound the common rate for problems where one achieves a cut bound on throughput.