arXiv subjects

Urbashi Mitra

Publications and source records attributed to Urbashi Mitra.

At least 91 records · Page 5Linked to original sources

arXiv2015

Nested Sparse Approximation: Structured Estimation of V2V Channels Using Geometry-Based Stochastic Channel Model

Future intelligent transportation systems promise increased safety and energy efficiency. Realization of such systems will require vehicle-to-vehicle (V2V) communication technology. High fidelity V2V communication is, in turn, dependent on accurate V2V channel estimation. V2V channels have characteristics differing from classical cellular communication channels. Herein, geometry-based stochastic modeling is employed to develop a characterization of V2V channel channels. The resultant model exhibits significant structure; specifically, the V2V channel is characterized by three distinct regions within the delay-Doppler plane. Each region has a unique combination of specular reflections and diffuse components resulting in a particular element-wise and group-wise sparsity. This joint sparsity structure is exploited to develop a novel channel estimation algorithm. A general machinery is provided to solve the jointly element/group sparse channel (signal) estimation problem using proximity operators of a broad class of regularizers. The alternating direction method of multipliers using the proximity operator is adapted to optimize the mixed objective function. Key properties of the proposed objective functions are proven which ensure that the optimal solution is found by the new algorithm. The effects of pulse shape leakage are explicitly characterized and compensated, resulting in measurably improved performance. Numerical simulation and real V2V channel measurement data are used to evaluate the performance of the proposed method. Results show that the new method can achieve significant gains over previously proposed methods.

math.OC↗

arXiv2015

A Stochastic Model for Electron Transfer in Bacterial Cables

Biological systems are known to communicate by diffusing chemical signals in the surrounding medium. However, most of the recent literature has neglected the electron transfer mechanism occurring amongst living cells, and its role in cell-cell communication. Each cell relies on a continuous flow of electrons from its electron donor to its electron acceptor through the electron transport chain to produce energy in the form of the molecule adenosine triphosphate, and to sustain the cell's vital operations and functions. While the importance of biological electron transfer is well-known for individual cells, the past decade has also brought about remarkable discoveries of multi-cellular microbial communities that transfer electrons between cells and across centimeter length scales, e.g., biofilms and multi-cellular bacterial cables. These experimental observations open up new frontiers in the design of electron-based communications networks in microbial communities, which may coexist with the more well-known communication strategies based on molecular diffusion, while benefiting from a much shorter communication delay. This paper develops a stochastic model that links the electron transfer mechanism to the energetic state of the cell. The model is also extensible to larger communities, by allowing for electron exchange between neighboring cells. Moreover, the parameters of the stochastic model are fit to experimental data available in the literature, and are shown to provide a good fit.

cs.ET↗

arXiv2015

Capacity of electron-based communication over bacterial cables: the full-CSI case

Motivated by recent discoveries of microbial communities that transfer electrons across centimeter-length scales, this paper studies the information capacity of bacterial cables via electron transfer, which coexists with molecular communications, under the assumption of full causal channel state information (CSI). The bacterial cable is modeled as an electron queue that transfers electrons from the encoder at the electron donor source, which controls the desired input electron intensity, to the decoder at the electron acceptor sink. Clogging due to local ATP saturation along the cable is modeled. A discrete-time scheme is investigated, enabling the computation of an achievable rate. The regime of asymptotically small time-slot duration is analyzed, and the optimality of binary input distributions is proved, i.e., the encoder transmits at either maximum or minimum intensity, as dictated by the physical constraints of the cable. A dynamic programming formulation of the capacity is proposed, and the optimal binary signaling is determined via policy iteration. It is proved that the optimal signaling has smaller intensity than that given by the myopic policy, which greedily maximizes the instantaneous information rate but neglects its effect on the steady-state cable distribution. In contrast, the optimal scheme balances the tension between achieving high instantaneous information rate, and inducing a favorable steady-state distribution, such that those states characterized by high information rates are visited more frequently, thus revealing the importance of CSI. This work represents a first contribution towards the design of electron signaling schemes in complex microbial structures, e.g., bacterial cables and biofilms, where the tension between maximizing the transfer of information and guaranteeing the well-being of the overall bacterial community arises.

cs.IT↗

arXiv2014

Cross-layer design of distributed sensing-estimation with quality feedback, Part I: Optimal schemes

This two-part paper presents a feedback-based cross-layer framework for distributed sensing and estimation of a dynamic process by a wireless sensor network (WSN). Sensor nodes wirelessly communicate measurements to the fusion center (FC). Cross-layer factors such as packet collisions and the sensing-transmission costs are considered. Each SN adapts its sensing-transmission action based on its own local observation quality and the estimation quality feedback from the FC under cost constraints for each SN. In this first part, the optimization complexity is reduced by exploiting the statistical symmetry and large network approximation of the WSN. Structural properties of the optimal policy are derived for a coordinated and a decentralized scheme. It is proved that a dense WSN provides sensing diversity, so that only a few SNs with the best local observation quality need to be activated, despite the fluctuations of the WSN. The optimal policy dictates that, when the estimation quality is poor, only the best SNs activate, otherwise all SNs remain idle to preserve energy. The costs of coordination and feedback are evaluated, revealing the scalability of the decentralized scheme to large WSNs, at the cost of performance degradation. Simulation results demonstrate cost savings from 30% to 70% over a non-adaptive scheme, and significant gains over a previously proposed estimator which does not consider these cross-layer factors.

eess.SY↗

arXiv2014

Cross-layer design of distributed sensing-estimation with quality feedback, Part II: Myopic schemes

This two-part paper presents a feedback-based cross-layer framework for distributed sensing and estimation of a dynamic process by a wireless sensor network (WSN). Sensor nodes wirelessly communicate measurements to the fusion center (FC). Cross-layer factors such as packet collisions and the sensing-transmission costs are considered. Each SN adapts its sensing-transmission action based on its own local observation quality and the estimation quality feedback from the FC under cost constraints for each SN. In this second part, low-complexity myopic sensing-transmission policies (MPs) are designed to optimize a trade-off between performance and the cost incurred by each SN. The MP is computed in closed form for a coordinated scheme, whereas an iterative algorithm is presented for a decentralized one, which converges to a local optimum. The MP dictates that, when the estimation quality is poor, only the best SNs activate, otherwise all SNs remain idle to preserve energy. For both schemes, the threshold on the estimation quality below which the SNs remain idle is derived in closed form, and is shown to be independent of the number of channels. It is also proved that a single channel suffices for severely energy constrained WSNs. The proposed MPs are shown to yield near-optimal performance with respect to the optimal policy of Part I, at a fraction of the complexity, thus being more suitable for practical WSN deployments.

eess.SY↗

arXiv2014

Fundamental Limits of Blind Deconvolution Part I: Ambiguity Kernel

Blind deconvolution is an ubiquitous non-linear inverse problem in applications like wireless communications and image processing. This problem is generally ill-posed, and there have been efforts to use sparse models for regularizing blind deconvolution to promote signal identifiability. Part I of this two-part paper characterizes the ambiguity space of blind deconvolution and shows unidentifiability of this inverse problem for almost every pair of unconstrained input signals. The approach involves lifting the deconvolution problem to a rank one matrix recovery problem and analyzing the rank two null space of the resultant linear operator. A measure theoretically tight (parametric and recursive) representation of the key rank two null space is stated and proved. This representation is a novel foundational result for signal and code design strategies promoting identifiability under convolutive observation models. Part II of this paper analyzes the identifiability of sparsity constrained blind deconvolution and establishes surprisingly strong negative results on scaling laws for the sparsity-ambiguity trade-off.

cs.IT↗

arXiv2014

Identifiability Scaling Laws in Bilinear Inverse Problems

A number of ill-posed inverse problems in signal processing, like blind deconvolution, matrix factorization, dictionary learning and blind source separation share the common characteristic of being bilinear inverse problems (BIPs), i.e. the observation model is a function of two variables and conditioned on one variable being known, the observation is a linear function of the other variable. A key issue that arises for such inverse problems is that of identifiability, i.e. whether the observation is sufficient to unambiguously determine the pair of inputs that generated the observation. Identifiability is a key concern for applications like blind equalization in wireless communications and data mining in machine learning. Herein, a unifying and flexible approach to identifiability analysis for general conic prior constrained BIPs is presented, exploiting a connection to low-rank matrix recovery via lifting. We develop deterministic identifiability conditions on the input signals and examine their satisfiability in practice for three classes of signal distributions, viz. dependent but uncorrelated, independent Gaussian, and independent Bernoulli. In each case, scaling laws are developed that trade-off probability of robust identifiability with the complexity of the rank two null space. An added appeal of our approach is that the rank two null space can be partly or fully characterized for many bilinear problems of interest (e.g. blind deconvolution). We present numerical experiments involving variations on the blind deconvolution problem that exploit a characterization of the rank two null space and demonstrate that the scaling laws offer good estimates of identifiability.

cs.IT↗

arXiv2014

Adaptive Molecule Transmission Rate for Diffusion Based Molecular Communication

In this paper, a simple memory limited transmitter for molecular communication is proposed, in which information is encoded in the diffusion rate of the molecules. Taking advantage of memory, the proposed transmitter reduces the ISI problem by properly adjusting its diffusion rate. The error probability of the proposed scheme is derived and the result is compared with the lower bound on error probability of the optimum transmitter. It is shown that the performance of introduced transmitter is near optimal (under certain simplifications). Simplicity is the key feature of the presented communication system: the transmitter follows a simple rule, the receiver is a simple threshold decoder and only one type of molecule is used to convey the information.

cs.IT↗

arXiv2014

Capacity of Diffusion based Molecular Communication Networks over LTI-Poisson Channels

In this paper, the capacity of a diffusion based molecular communication network under the model of a Linear Time Invarient-Poisson (LTI-Poisson) channel is studied. Introduced in the context of molecular communication, the LTI-Poisson model is a natural extension of the conventional memoryless Poisson channel to include memory. Exploiting prior art on linear ISI channels, a computable finite-letter characterization of the capacity of single-hop LTI-Poisson networks is provided. Then, the problem of finding more explicit bounds on the capacity is examined, where lower and upper bounds for the point to point case are provided. Furthermore, an approach for bounding mutual information in the low SNR regime using the symmetrized KL divergence is introduced and its applicability to Poisson channels is shown. To best of our knowledge, the first non-trivial upper bound on the capacity of Poisson channel with a maximum transmission constraint in the low SNR regime is found. Numerical results show that the proposed upper bound is of the same order as the capacity in the low SNR regime.

cs.IT↗

arXiv2014

Controlled Sensing: A Myopic Fisher Information Sensor Selection Algorithm

This paper considers the problem of state tracking with observation control for a particular class of dynamical systems. The system state evolution is described by a discrete-time, finite-state Markov chain, while the measurement process is characterized by a controlled multi-variate Gaussian observation model. The computational complexity of the optimal control strategy proposed in our prior work proves to be prohibitive. A suboptimal, lower complexity algorithm based on the Fisher information measure is proposed. Toward this end, the preceding measure is generalized to account for multi-valued discrete parameters and control inputs. A closed-form formula for our system model is also derived. Numerical simulations are provided for a physical activity tracking application showing the near-optimal performance of the proposed algorithm.

eess.SY↗

arXiv2014

Nonlinear POMDPs for Active State Tracking with Sensing Costs

Active state tracking is needed in object classification, target tracking, medical diagnosis and estimation of sparse signals among other various applications. Herein, active state tracking of a discrete-time, finite-state Markov chain is considered. Noisy Gaussian observations are dynamically collected by exerting appropriate control over their information content, while incurring a related sensing cost. The objective is to devise sensing strategies to optimize the trade-off between tracking performance and sensing cost. A recently proposed Kalman-like estimator \cite{ZoisTSP14} is employed for state tracking. The associated mean-squared error and a generic sensing cost metric are then used in a partially observable Markov decision process formulation, and the optimal sensing strategy is derived via a dynamic programming recursion. The resulting recursion proves to be non-linear, challenging control policy design. Properties of the related cost functions are derived and sufficient conditions are provided regarding the structure of the optimal control policy enabling characterization of when passive state tracking is optimal. To overcome the associated computational burden of the optimal sensing strategy, two lower complexity strategies are proposed, which exploit the aforementioned properties. The performance of the proposed strategies is illustrated in a wireless body sensing application, where cost savings as high as $60\%$ are demonstrated for a $4\%$ detection error with respect to a static equal allocation sensing strategy.

eess.SY↗

arXiv2014

Receivers for Diffusion-Based Molecular Communication: Exploiting Memory and Sampling Rate

In this paper, a diffusion-based molecular communication channel between two nano-machines is considered. The effect of the amount of memory on performance is characterized, and a simple memory-limited decoder is proposed and its performance is shown to be close to that of the best possible imaginable decoder (without any restriction on the computational complexity or its functional form), using Genie-aided upper bounds. This effect is specialized for the case of Molecular Concentration Shift Keying; it is shown that a four-bits memory achieved nearly the same performance as infinite memory. Then a general class of threshold decoders is considered and shown not to be optimal for Poisson channel with memory, unless SNR is higher than a value specified in the paper. Another contribution is to show that receiver sampling at a rate higher than the transmission rate, i.e., a multi-read system, can significantly improve the performance. The associated decision rule for this system is shown to be a weighted sum of the samples during each symbol interval. The performance of the system is analyzed using the saddle point approximation. The best performance gains are achieved for an oversampling factor of three.

physics.bio-ph↗

arXiv2013

Active Classification for POMDPs: a Kalman-like State Estimator

The problem of state tracking with active observation control is considered for a system modeled by a discrete-time, finite-state Markov chain observed through conditionally Gaussian measurement vectors. The measurement model statistics are shaped by the underlying state and an exogenous control input, which influence the observations' quality. Exploiting an innovations approach, an approximate minimum mean-squared error (MMSE) filter is derived to estimate the Markov chain system state. To optimize the control strategy, the associated mean-squared error is used as an optimization criterion in a partially observable Markov decision process formulation. A stochastic dynamic programming algorithm is proposed to solve for the optimal solution. To enhance the quality of system state estimates, approximate MMSE smoothing estimators are also derived. Finally, the performance of the proposed framework is illustrated on the problem of physical activity detection in wireless body sensing networks. The power of the proposed framework lies within its ability to accommodate a broad spectrum of active classification applications including sensor management for object classification and tracking, estimation of sparse signals and radar scheduling.

eess.SY↗

arXiv2013

Broadcast Channel Games: Equilibrium Characterization and a MIMO MAC-BC Game Duality

The emergence of heterogeneous decentralized networks without a central controller, such as device-to-device communication systems, has created the need for new problem frameworks to design and analyze the performance of such networks. As a key step towards such an analysis for general networks, this paper examines the strategic behavior of \emph{receivers} in a Gaussian broadcast channel (BC) and \emph{transmitters} in a multiple access channel (MAC) with sum power constraints (sum power MAC) using the framework of non-cooperative game theory. These signaling scenarios are modeled as generalized Nash equilibrium problems (GNEPs) with jointly convex and coupled constraints and the existence and uniqueness of equilibrium achieving strategies and equilibrium utilities are characterized for both the Gaussian BC and the sum power MAC. The relationship between Pareto-optimal boundary points of the capacity region and the generalized Nash equilibria (GNEs) are derived for the several special cases and in all these cases it is shown that all the GNEs are Pareto-optimal, demonstrating that there is no loss in efficiency when players adopt strategic behavior in these scenarios. Several key equivalence relations are derived and used to demonstrate a game-theoretic duality between the Gaussian MAC and the Gaussian BC. This duality allows a parametrized computation of the equilibria of the BC in terms of the equilibria of the MAC and paves the way to translate several MAC results to the dual BC scenario.

cs.IT↗

arXiv2012

Coalitional Games for Transmitter Cooperation in MIMO Multiple Access Channels

Cooperation between nodes sharing a wireless channel is becoming increasingly necessary to achieve performance goals in a wireless network. The problem of determining the feasibility and stability of cooperation between rational nodes in a wireless network is of great importance in understanding cooperative behavior. This paper addresses the stability of the grand coalition of transmitters signaling over a multiple access channel using the framework of cooperative game theory. The external interference experienced by each TX is represented accurately by modeling the cooperation game between the TXs in \emph{partition form}. Single user decoding and successive interference cancelling strategies are examined at the receiver. In the absence of coordination costs, the grand coalition is shown to be \emph{sum-rate optimal} for both strategies. Transmitter cooperation is \emph{stable}, if and only if the core of the game (the set of all divisions of grand coalition utility such that no coalition deviates) is nonempty. Determining the stability of cooperation is a co-NP-complete problem in general. For a single user decoding receiver, transmitter cooperation is shown to be \emph{stable} at both high and low SNRs, while for an interference cancelling receiver with a fixed decoding order, cooperation is stable only at low SNRs and unstable at high SNR. When time sharing is allowed between decoding orders, it is shown using an approximate lower bound to the utility function that TX cooperation is also stable at high SNRs. Thus, this paper demonstrates that ideal zero cost TX cooperation over a MAC is stable and improves achievable rates for each individual user.

cs.IT↗

arXiv2012

Cascade Source Coding with a Side Information "Vending Machine"

The model of a side information "vending machine" (VM) accounts for scenarios in which the measurement of side information sequences can be controlled via the selection of cost-constrained actions. In this paper, the three-node cascade source coding problem is studied under the assumption that a side information VM is available and the intermediate and/or at the end node of the cascade. A single-letter characterization of the achievable trade-off among the transmission rates, the distortions in the reconstructions at the intermediate and at the end node, and the cost for acquiring the side information is derived for a number of relevant special cases. It is shown that a joint design of the description of the source and of the control signals used to guide the selection of the actions at downstream nodes is generally necessary for an efficient use of the available communication links. In particular, for all the considered models, layered coding strategies prove to be optimal, whereby the base layer fulfills two network objectives: determining the actions of downstream nodes and simultaneously providing a coarse description of the source. Design of the optimal coding strategy is shown via examples to depend on both the network topology and the action costs. Examples also illustrate the involved performance trade-offs across the network.

cs.IT↗

arXiv2012

On Cascade Source Coding with A Side Information "Vending Machine"

The model of a side information "vending machine" accounts for scenarios in which acquiring side information is costly and thus should be done efficiently. In this paper, the three-node cascade source coding problem is studied under the assumption that a side information vending machine is available either at the intermediate or at the end node. In both cases, a single-letter characterization of the available trade-offs among the rate, the distortions in the reconstructions at the intermediate and at the end node, and the cost in acquiring the side information are derived under given conditions.

cs.IT↗

arXiv2012

Causal State Communication

The problem of state communication over a discrete memoryless channel with discrete memoryless state is studied when the state information is available strictly causally at the encoder. It is shown that block Markov encoding, in which the encoder communicates a description of the state sequence in the previous block by incorporating side information about the state sequence at the decoder, yields the minimum state estimation error. When the same channel is used to send additional independent information at the expense of a higher channel state estimation error, the optimal tradeoff between the rate of the independent information and the state estimation error is characterized via the capacity- distortion function. It is shown that any optimal tradeoff pair can be achieved via rate-splitting. These coding theorems are then extended optimally to the case of causal channel state information at the encoder using the Shannon strategy.

cs.IT↗