Cut-set Bound Research Articles

Infeasibility Proof and Information State in Network Information Theory

In this paper, we revisit the structure of infeasibility results in network information theory, based on a notion of information state. We also discuss ideas for generalizing a known outer bound for lossless transmission of independent sources over a network to one of lossy transmission of dependent sources over the same network. To concretely demonstrate this, we apply our ideas and prove new results for lossy transmission of dependent sources by generalizing: 1) the cut-set bound; 2) the best known outer bound on the capacity region of a general broadcast channel; and 3) the outer bound part of the result of Maric, Yates, and Kramer on strong interference channels with a common message.

Network Capacity Under Traffic Symmetry: Wireline and Wireless Networks

The problem of designing near optimal strategies for multiple unicast traffic in wireline networks is wide open; however, channel symmetry or traffic symmetry can be leveraged to show that routing can achieve with a polylogarithmic approximation factor of the edge-cut bound. For the same problem, the edge-cut bound is known to only upper bound rates of routing flows and unlike the information theoretic cut-set bound, it does not upper bound (capacity-achieving) information rates with general strategies. In this paper, we demonstrate that under channel or traffic symmetry, the edge-cut bound upper-bounds general information rates, thus providing a capacity approximation result. The key technique is a combinatorial result relating edge-cut bounds to generalized network sharing bounds. Finally, we generalize the results to wireless networks via an intermediary class of combinatorial graphs known as polymatroidal networks-our main result is that a natural architecture separating the physical and networking layers is near optimal when the traffic is symmetric among source-destination pairs, even when the channel is asymmetric (due to asymmetric power constraints, or prior frequency allocation like frequency division duplexing). This result is complementary to an earlier work of two of the authors proving a similar result under channel symmetry.

Polytope Codes Against Adversaries in Networks

This paper investigates a network coding problem wherein an adversary controls a subset of nodes in the network of limited quantity but unknown location. This problem is shown to be more difficult than that of an adversary controlling a given number of edges in the network, in that linear codes are insufficient. To solve the node problem, the class of polytope codes is introduced. Polytope codes are constant composition codes operating over bounded polytopes in integer vector fields. The polytope structure creates additional complexity, but it induces properties on marginal distributions of code vectors so that validities of codewords can be checked by internal nodes of the network. It is shown that polytope codes achieve a cut-set bound for a class of planar networks. It is also shown that this cut-set bound is not always tight, and a tighter bound is given for an example network.

Grassmannian Signalling Achieves Tight Bounds on the Ergodic High-SNR Capacity of the Noncoherent MIMO Full-Duplex Relay Channel

This paper considers the ergodic noncoherent capacity of a multiple-input multiple-output frequency-flat block Rayleigh fading full duplex relay channel at high signal-to-noise ratios (SNRs). It is shown that, for these SNRs, restricting the input distribution to be isotropic on a compact Grassmann manifold maximizes an upper bound on the cut-set bound. Furthermore, it is shown that, from a degrees of freedom point of view, no relaying is necessary and Grassmannian signalling at the source achieves the upper bound within an SNR-independent gap. When the source-relay channel is sufficiently stronger than the source-destination and relay-destination channels, it is shown that, with the number of relay transmit antennas appropriately chosen, a Grassmannian decode-and-forward scheme, which is devised herein, achieves the ergodic noncoherent capacity of the relay channel within an approximation gap that goes to zero as the SNR goes to infinity. Closed-form expressions for the optimal number of relay transmit antennas indicate that this number decreases monotonically with the source transmit power.

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.

Information Networks With In-Block Memory

A class of channels is introduced for which there is memory inside blocks of a specified length and no memory across the blocks. The multi-user model is called an information network with in-block memory (NiBM). It is shown that block-fading channels, channels with state known causally at the encoder, and relay networks with delays are NiBMs. A cut-set bound is developed for NiBMs that unifies, strengthens, and generalizes existing cut bounds for discrete memoryless networks. The bound gives new finite-letter capacity expressions for several classes of networks including point-to-point channels, and certain multiaccess, broadcast, and relay channels. Cardinality bounds on the random coding alphabets are developed that improve on existing bounds for channels with action-dependent state available causally at the encoder and for relays without delay. Finally, quantize-forward network coding is shown to achieve rates within an additive gap of the new cut-set bound for linear, additive, Gaussian noise channels, symmetric power constraints, and a multicast session.

Characterizing the Rate Region of the (4,3,3) Exact-Repair Regenerating Codes

Exact-repair regenerating codes are considered for the case (n,k,d)=(4,3,3), for which a complete characterization of the rate region is provided. This characterization answers in the affirmative the open question whether there exists a non-vanishing gap between the optimal bandwidth-storage tradeoff of the functional-repair regenerating codes (i.e., the cut-set bound) and that of the exact-repair regenerating codes. To obtain an explicit information theoretic converse, a computer-aided proof (CAP) approach based on primal and dual relation is developed. This CAP approach extends Yeung's linear programming (LP) method, which was previously only used on information theoretic problems with a few random variables due to the exponential growth of the number of variables in the corresponding LP problem. The symmetry in the exact-repair regenerating code problem allows an effective reduction of the number of variables, and together with several other problem-specific reductions, the LP problem is reduced to a manageable scale. For the achievability, only one non-trivial corner point of the rate region needs to be addressed in this case, for which an explicit binary code construction is given.

Capacity of a Class of Linear Binary Field Multisource Relay Networks

In this paper, we study a layered linear binary field network with time-varying channels, which is a simplified model reflecting broadcast, interference, and fading natures of wireless communications. We observe that fading can play an important role in mitigating interuser interference effectively for both single-hop and multihop networks. We propose new coding schemes with randomized ergodic channel pairing, which exploit such channel variations, and derive their achievable ergodic rates. By comparing them with the cut-set upper bound, the capacity region of single-hop networks and the sum capacity of multihop networks are characterized for some classes of channel distributions and network topologies.

Beyond the Cut-Set Bound: Uncertainty Computations in Network Coding With Correlated Sources

Motivated by communication through a network employing linear network coding, capacities of linear operator channels (LOCs) with arbitrarily distributed transfer matrices over finite fields are studied. Both the Shannon capacity $C$ and the subspace coding capacity $C_{\text{SS}}$ are analyzed. By establishing and comparing lower bounds on $C$ and upper bounds on $C_{\text{SS}}$, various necessary conditions and sufficient conditions such that $C=C_{\text{SS}}$ are obtained. A new class of LOCs such that $C=C_{\text{SS}}$ is identified, which includes LOCs with uniform-given-rank transfer matrices as special cases. It is also demonstrated that $C_{\text{SS}}$ is strictly less than $C$ for a broad class of LOCs. In general, an optimal subspace coding scheme is difficult to find because it requires to solve the maximization of a non-concave function. However, for a LOC with a unique subspace degradation, $C_{\text{SS}}$ can be obtained by solving a convex optimization problem over rank distribution. Classes of LOCs with a unique subspace degradation are characterized. Since LOCs with uniform-given-rank transfer matrices have unique subspace degradations, some existing results on LOCs with uniform-given-rank transfer matrices are explained from a more general way.

On Modulo-Sum Computation Over an Erasure Multiple-Access Channel

We study modulo-sum computation of two binary source sequences over a two-user erasure multiple access channel. The channel is modeled as a binary-input, erasure multiple access channel, which can be in one of three states-either the channel output is a modulo-sum of the two input symbols, or the channel output equals the input symbol on the first link and an erasure on the second link, or vice versa. The associated state sequence is independent and identically distributed. Unlike previously studied multiple-access channels, the proposed channel is not matched to the modulo-sum function and therefore we expect simple cut-set upper bounds to be far from capacity. In this paper, we establish a new upper bound on the modulo-sum capacity that is tighter than the cut-set bound. The key step in establishing this new bound is to provide suitable side information to the encoders to reduce the setup to a compound multiple-access channel and then capture the tension across multiple receivers required to compute the modulo-sum function. In our lower bound, it suffices to use identical linear codebooks at the two encoders. When a (strictly) causal feedback of the channel state is available to the encoders, we present a simple coding scheme that can achieve a rate larger than our upper bound for the case without feedback. This shows that the modulo-sum capacity is increased with feedback. An extension to the case of lossy reconstruction is also treated briefly.

Linear Function Computation in Networks: Duality and Constant Gap Results

In linear function computation, multiple source nodes communicate across a relay network to a single destination whose goal is to recover linear functions of the original source data. When the relay network is a linear deterministic network, a duality relation is established between function computation and broadcast with common messages. Using this relation, a compact sufficient condition is found describing those cases where the cut-set bound is tight. These insights are used to develop results for the case where the relay network contains Gaussian multiple-access channels. The proposed scheme decouples the physical and network layers. Using lattice codes for both source quantization and computation in the physical layer, the original Gaussian sources are converted into discrete sources and the Gaussian network into a linear deterministic network. Network codes for computing functions of discrete sources across the deterministic network are then found by applying the duality relation. The distortion for computing the sum of an arbitrary number of independent Gaussian sources over the Gaussian network is proven to be within a constant factor of the optimal performance. Furthermore, the constant factor results are extended to include asymmetric functions for the case of two sources.

The Approximate Capacity of the Gaussian $N$-Relay Diamond Network

We consider the Gaussian “diamond” or parallel relay network, in which a source node transmits a message to a destination node with the help of N relays. Even for the symmetric setting, in which the channel gains to the relays are identical and the channel gains from the relays are identical, the capacity of this channel is unknown in general. The best known capacity approximation is up to an additive gap of order N bits and up to a multiplicative gap of order N2, with both gaps independent of the channel gains. In this paper, we approximate the capacity of the symmetric Gaussian N-relay diamond network up to an additive gap of 1.8 bits and up to a multiplicative gap of a factor 14. Both gaps are independent of the channel gains and, unlike the best previously known result, are also independent of the number of relays N in the network. Achievability is based on bursty amplify-and-forward, showing that this simple scheme is uniformly approximately optimal, both in the low-rate as well as in the high-rate regimes. The upper bound on capacity is based on a careful evaluation of the cut-set bound. We also present approximation results for the asymmetric Gaussian N-relay diamond network. In particular, we show that bursty amplify-and-forward combined with optimal relay selection achieves a rate within a factor O(log4(N)) of capacity with preconstant in the order notation independent of the channel gains.

Approximately Optimal Wireless Broadcasting

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.

Analysis of decode-and-forward, compress-and-forward and combined protocols for multiterminal half-duplex relay networks with random schedules

In this paper, protocols based on compress-and-forward, decode-and-forward and a combined approach are discussed, achievable rates are derived, and a performance analysis for a multiterminal half-duplex relay network is carried out. Half-duplex relays are subject to an orthogonality constraint, which prohibits simultaneous receiving and transmitting on a time-frequency resource. With this constraint, this paper investigates random transmission schedules, such that the sequence of transmit and receive slots serves as information bearer. This concept is applied to a network of decode-and-forward relays, which decode the full source message or parts of it. Then a compress-and-forward protocol with regular encoding is introduced. Regular encoding for compress-and-forward overcomes the limitations due to the source-channel-coding separation, which is a particular bottleneck in multiterminal networks. Based on the partial decode-and-forward and compress-and-forward protocol, a protocol with one compress-and-forward and one decode-and-forward based relay is presented. Similar to the cut-set bound, both relays operate in a mode in which only one is transmitting whereas the second one is listening to exploit additional information transmitted by the other relay.For the considered scenarios and by contrast to the full-duplex channel, protocols based on compress-and-forward outperform decode-and-forward protocols for a wide range of parameters. Furthermore, the introduced combined approach provides achievable rates close to the cut-set bound for scenarios in which both relays are well separated or very close to each other. If the inter-relay interference is too low for an efficient interference cancellation but also too strong to be ignored, compress-and-forward also outperforms the combined strategy. Copyright © 2012 John Wiley & Sons, Ltd.

On Achievable Rate Regions for Half-Duplex Gaussian MIMO Relay Channels: A Decomposition Approach

We consider uni- and bidirectional communication in the half-duplex multiple-input multiple-output (MIMO) relay channel. Assuming perfect channel state information at all nodes and the use of time division duplex communication protocols with a peak power constraint for every protocol phase, we propose a dual decomposition approach to efficiently determine the cut-set bound and the maximum achievable decode-and-forward rate for the Gaussian MIMO relay channel. A general outer bound on the rate regions that can be achieved in the restricted two-way MIMO relay channel is established, and we present an achievable rate region based on the decode-and-forward scheme which is a superset of several previously derived achievable rate regions. Finally, it is shown how the stated outer bound and achievable rate region can also be evaluated by means of the proposed dual decomposition approach, and we discuss how our work may be used for designing optimal protocols.

Resource Allocation for Two-Way AF Relaying with Receive Channel Knowledge

The resource allocation problem for two sources communicating via an amplify-forward relay is studied from an outage perspective. Analog network coding is considered for half-duplex nodes with perfect receiver-side channel knowledge. Under a sum power constraint, an optimal power allocation that minimizes an approximate outage probability is derived and shown to improve the performance upto 4.77 dB. A cut-set bound is also optimized to serve as a comparison reference. When such a power allocation is not feasible, two novel resource- optimized schemes, which exploit conventional one-way relaying, are proposed to reduce the outage at low multiplexing gains1.

Instantaneous Relaying: Optimal Strategies and Interference Neutralization

In a multi-user wireless network equipped with multiple relay nodes, some relays are more intelligent than other relay nodes. The intelligent relays are able to gather channel state information, perform linear processing and forward signals whereas the dumb relays is only able to serve as amplifiers. As the dumb relays are oblivious to the source and destination nodes, the wireless network can be modeled as a relay network with *smart instantaneous relay* only: the signals of source-destination arrive at the same time as source-relay-destination. Recently, instantaneous relaying is shown to improve the degrees-of-freedom of the network as compared to classical cut-set bound. In this paper, we study an achievable rate region and its boundary of the instantaneous interference relay channel in the scenario of (a) uninformed non-cooperative source-destination nodes (source and destination nodes are not aware of the existence of the relay and are non-cooperative) and (b) informed and cooperative source-destination nodes. Further, we examine the performance of interference neutralization: a relay strategy which is able to cancel interference signals at each destination node in the air. We observe that interference neutralization, although promise to achieve desired degrees-of-freedom, may not be feasible if relay has limited power. Simulation results show that the optimal relay strategies improve the achievable rate region and provide better user-fairness in both uninformed non-cooperative and informed cooperative scenarios.

The value of cooperation between relays in the multiple-access channel with multiple relays

We study the discrete, memoryless multiple-access channel with two independent sources, two relays and a single destination. We refer to this configuration as the multiple- access channel with multiple relays (MACMR), which is a generalization of the multiple-access relay channel (MARC) model obtained by adding a relay node. We present inner and outer bounds on the capacity region of the MACMR. The inner bound is based on a hierarchical decode-and-forward scheme, in which each relay decodes the messages of the lower hierarchy. We extend the regular encoding, sliding-window decoding and backward decoding techniques, previously applied to MARCs and multiple-relay channels, to MACMRs. The outer bounds are obtained using the cut-set bound. For Gaussian MACMRs the bounds are evaluated and compared to those obtained for the multiple-access channel with parallel relays. We conclude that a significant improvement in performance can be obtained by letting the relays interact with each other. Copyright c 0000 John Wiley & Sons, Ltd.

A Decode and Forward Protocol for Two-Stage Gaussian Relay Networks

We propose a multihopping decode and forward relaying protocol for two-stage Gaussian relay networks with half-duplex nodes. We analytically show that the achievable rates in suitably defined strong and weak interference regimes are close to the cut-set bound.

Cooperative Network Coding Strategies for Wireless Relay Networks with Backhaul

We investigate cooperative network coding strategies for relay-aided two-source two-destination wireless networks with a backhaul connection between the source nodes. Each source multicasts information to all destinations using a shared relay. We study cooperative strategies based on different network coding schemes, namely, finite field and linear network coding, and lattice coding. To further exploit the backhaul connection, we also propose network coding based beamforming. We measure the performance in term of achievable rates over Gaussian channels, and observe significant gains over benchmark schemes. We derive the achievable rate regions for these schemes and find the cut-set bound for our system. We also show that the cut-set bound can be achieved by network coding based beamforming when the signal-to-noise ratios lie in the sphere defined by the source-relay and relay-destination channel gains.