Ritesh Kolte

Capacity Approximations for Gaussian Relay Networks

Consider a Gaussian relay network where a source node communicates to a destination node with the help of several layers of relays. Recent work has shown that compress-and-forward-based strategies can achieve the capacity of this network within an additive gap. Here, the relays quantize their received signals at the noise level and map them to random Gaussian codebooks. The resultant gap to capacity is independent of the SNRs of the channels in the network and the topology, but is linear in the total number of nodes. In this paper, we provide an improved lower bound on the rate achieved by the compress-and-forward-based strategies (noisy network coding in particular) in arbitrary Gaussian relay networks, whose gap to capacity depends on the network not only through the total number of nodes but also through the degrees of freedom of the min cut of the network. We illustrate that for many networks, this refined lower bound can lead to a better approximation of the capacity. In particular, we demonstrate that it leads to a logarithmic rather than linear capacity gap in the total number of nodes for certain classes of layered networks. The improvement comes from quantizing the received signals of the relays at a resolution decreasing with the total number of nodes in the network. This suggests that the rule-of-thumb in the literature of quantizing the received signals at the noise level can be highly suboptimal.

Improved capacity approximations for Gaussian relay networks

Consider a Gaussian relay network where a number of sources communicate to a destination with the help of several layers of relays. Recent work has shown that a compress-and-forward based strategy at the relays can achieve the capacity of this network within an additive gap. In this strategy, the relays quantize their observations at the noise level and map it to a random Gaussian codebook. The resultant capacity gap is independent of the SNRs of the channels in the network but linear in the total number of nodes. In this paper, we show that if the relays quantize their signals at a resolution decreasing with the number of nodes in the network, the additive gap to capacity can be made logarithmic in the number of nodes for a class of layered, time-varying wireless relay networks. This suggests that the rule-of-thumb to quantize the received signals at the noise level used for compress-and-forward in the current literature can be highly suboptimal.

When Are Dynamic Relaying Strategies Necessary in Half-Duplex Wireless Networks?

In this paper, we study a simple question: when are dynamic relaying strategies essential in optimizing the diversity-multiplexing tradeoff (DMT) in half-duplex wireless relay networks? This is motivated by apparently two contrasting results even for a simple three-node network with a single half-duplex relay. When all channels in the system are assumed to be independent and identically fading, a static schedule where the relay listens half the time and transmits half the time combined with quantize-map-and-forward (QMF) relaying is known to achieve the full-duplex performance. However, when there is no direct link between the source and the destination, a dynamic decode-and-forward (DDF) strategy is needed to achieve the optimal tradeoff. In this case, a static schedule is strictly suboptimal and the optimal tradeoff is significantly worse than the full-duplex performance. In this paper, we study the general case when the direct link is neither as strong as the other links nor fully nonexistent, and identify regimes where dynamic schedules are necessary and those where static schedules are enough. We identify four qualitatively different regimes for the single-relay channel, where the tradeoff between diversity and multiplexing is significantly different. We show that in all these regimes one of the above two strategies is sufficient to achieve the optimal tradeoff by developing a new upper bound on the best achievable tradeoff under channel state information available only at the receivers. A natural next question is whether these two strategies are sufficient to achieve the DMT of more general half-duplex wireless networks with a larger number of relays. We propose a generalization of the two existing schemes through a dynamic QMF (DQMF) strategy, where the relay listens for a fraction of time depending on received channel state information but not long enough to be able to decode. We show that such a DQMF strategy is needed to achieve the optimal DMT in a parallel channel with two relays, outperforming both DDF and static QMF strategies.

Cooperative Binning for Semideterministic Channels

The capacity regions of semideterministic multiuser channels, such as the semideterministic relay channel and the multiple access channel with partially cribbing encoders, have been characterized using the idea of partial-decode-forward. However, the requirement to explicitly decode part of the message at intermediate nodes can be restrictive in some settings; for example, when nodes have different side information regarding the state of the channel. In this paper, we generalize this scheme to cooperative-bin-forward by building on the observation that explicit recovering of part of the message is not needed to induce cooperation. Instead, encoders can bin their received signals and cooperatively forward the bin index to the decoder. The main advantage of this new scheme is illustrated by considering state-dependent extensions of the aforementioned semideterministic setups. While partial-decode-forward is suboptimal in these new setups, cooperative-bin-forward continues to achieve capacity.

The capacity region of a class of deterministic state-dependent Z-interference channels.

We consider the problem of communicating over the state-dependent Z-interference channel (S-D Z-IC), when the state is known noncausally only to the interfering transmitter. We present an achievability scheme and show that it is optimal for the injective deterministic S-D Z-IC. This scheme is simple in the sense that it does not involve rate-splitting. The idea of the scheme is that the interfering transmitter chooses its signal to be jointly typical with an auxilary coordination codebook that allows the unintended receiver to partly decode the resultant interference. We then investigate the special case of the modulo-additive S-D Z-IC in detail and show that in this case standard Gelfand-Pinsker coding for the interfering link and treating interference as noise at the second link is optimal. We also extend our main result to the deterministic state-dependent Z-channel (S-D Z-C) in which an additional message is transmitted on the cross-link.

Generalized diversity-multiplexing tradeoff of half-duplex relay networks

Diversity-multiplexing trade-off has been studied extensively to quantify the benefits of different relaying strategies in terms of error and rate performance. However, even in the case of a single half-duplex relay, which seems fully characterized, implications are not clear. When all channels in the system are assumed to be independent and identically fading, a fixed schedule where the relay listens half of the total duration for communication and transmits the second half combined with quantize-map-and-forward relaying (static QMF) is known to achieve the full-duplex performance [1]. However, when there is no direct link between the source and the destination, a dynamic decode-and-forward (DDF) strategy is needed [2]. It is not clear which one of these two conclusions would carry to a less idealized setup, where the direct link can be neither as strong as the other links nor fully non-existent. In this paper, we provide a generalized diversity-multiplexing trade-off for the half-duplex relay channel which accounts for different channel strengths and recovers the two earlier results as two special cases. We show that these two strategies are sufficient to achieve the diversity-multiplexing trade-off across all channel configurations, by characterizing the best achievable trade-off when channel state information (CSI) is only available at the receivers (CSIR). However, for general relay networks we show that a generalization of these two schemes through a dynamic QMF strategy is needed to achieve optimal performance.

Fast near-optimal subnetwork selection in layered relay networks

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.

Multicoding Schemes for Interference Channels

The best known inner bound for the two-user discrete memoryless interference channel is the Han-Kobayashi rate region. The coding schemes that achieve this region are based on rate-splitting and superposition coding. In this paper, we develop a multicoding scheme to achieve the same rate region. A key advantage of the multicoding nature of the proposed coding scheme is that it can be naturally extended to more general settings, such as when encoders have state information or can overhear each other. In particular, we extend our coding scheme to characterize the capacity region of the state-dependent deterministic Z-interference channel when noncausal state information is available at the interfering transmitter. We specialize our results to the case of the linear deterministic model with ON/OFF interference, which models a wireless system where a cognitive transmitter is noncausally aware of the times it interferes with a primary transmission. For this special case, we provide an explicit expression for the capacity region and discuss some interesting properties of the optimal strategy. We also extend our multicoding scheme to find the capacity region of the deterministic Z-interference channel when the signal of the interfering transmitter can be overheard at the other transmitter (also known as unidirectional partial cribbing).

Towards in vivo estimation of reaction kinetics using high-throughput metabolomics data: a maximum likelihood approach

BackgroundHigh-throughput assays such as mass spectrometry have opened up the possibility for large-scale in vivo measurements of the metabolome. This data could potentially be used to estimate kinetic parameters for many metabolic reactions. However, high-throughput in vivo measurements have special properties that are not taken into account in existing methods for estimating kinetic parameters, including significant relative errors in measurements of metabolite concentrations and reaction rates, and reactions with multiple substrates and products, which are sometimes reversible. A new method is needed to estimate kinetic parameters taking into account these factors.ResultsA new method, InVEst (In Vivo Estimation), is described for estimating reaction kinetic parameters, which addresses the specific challenges of in vivo data. InVEst uses maximum likelihood estimation based on a model where all measurements have relative errors. Simulations show that InVEst produces accurate estimates for a reversible enzymatic reaction with multiple reactants and products, that estimated parameters can be used to predict the effects of genetic variants, and that InVEst is more accurate than general least squares and graphic methods on data with relative errors. InVEst uses the bootstrap method to evaluate the accuracy of its estimates.ConclusionsInVEst addresses several challenges of in vivo data, which are not taken into account by existing methods. When data have relative errors, InVEst produces more accurate and robust estimates. InVEst also provides useful information about estimation accuracy using bootstrapping. It has potential applications of quantifying the effects of genetic variants, inference of the target of a mutation or drug treatment and improving flux estimation.

Deterministic Z-interference channels with unidirectional partial cribbing

We consider the problem of communicating over Z-interference channels when the interfering transmitter can overhear the signal of the other transmitter strictly causally. We focus on the deterministic case and characterize the capacity region in this case. The optimal coding scheme that we present is far simpler than currently known schemes for this setting. Motivated by this result, we present another way of viewing the Han-Kobayashi scheme for the interference channel, which can be easier to tweak in more general settings.