Diffusive Molecular Communication with Nanomachine Mobility

This work presents a performance analysis for diffusive molecular communication with mobile transmit and receive nanomachines. To begin with, the optimal test is obtained for symbol detection at the receiver nanomachine. Subsequently, closed-form expressions are derived for the probabilities of detection and false alarm, probability of error, and capacity considering also aberrations such as multi-source interference, inter-symbol interference, and counting errors. Simulation results are presented to corroborate the theoretical results derived and also, to yield various insights into the performance of the system. Interestingly, it is shown that the performance of the mobile diffusive molecular communication can be significantly enhanced by allocating large fraction of total available molecules for transmission as the slot interval increases.Comment: To be submitted in 52th Annual Conference on Information Sciences and Systems (CISS

Cognitive MIMO-RF/FSO Cooperative Relay Communication with Mobile Nodes and Imperfect Channel State Information

This work analyzes the performance of an underlay cognitive radio based decode-and-forward mixed multiple-input multiple-output (MIMO) radio frequency/free space optical (RF/FSO) cooperative relay system with multiple mobile secondary and primary user nodes. The effect of imperfect channel state information (CSI) arising due to channel estimation error is also considered at the secondary user transmitters (SU-TXs) and relay on the power control and symbol detection processes respectively. A unique aspect of this work is that both fixed and proportional interference power constraints are employed to limit the interference at the primary user receivers (PU-RXs). Analytical results are derived to characterize the exact and asymptotic outage and bit error probabilities of the above system under practical conditions of node mobility and imperfect CSI, together with impairments of the optical channel, such as path loss, atmospheric turbulence, and pointing errors, for orthogonal space-time block coded transmission between each SU-TX and relay. Finally, simulation results are presented to yield various interesting insights into the system performance such as the benefits of a midamble versus preamble for channel estimation.Comment: revision submitted to IEEE Transactions on Cognitive Communications and Networkin

Design and Performance Analysis of Dual and Multi-hop Diffusive Molecular Communication Systems

This work presents a comprehensive performance analysis of diffusion based direct, dual-hop, and multi-hop molecular communication systems with Brownian motion and drift in the presence of various distortions such as inter-symbol interference (ISI), multi-source interference (MSI), and counting errors. Optimal decision rules are derived employing the likelihood ratio tests (LRTs) for symbol detection at each of the cooperative as well as the destination nanomachines. Further, closed-form expressions are also derived for the probabilities of detection, false alarm at the individual cooperative, destination nanomachines, as well as the overall end-to-end probability of error for source-destination communication. The results also characterize the impact of detection performance of the intermediate cooperative nanomachine(s) on the end-to-end performance of dual/multi hop diffusive molecular communication systems. In addition, capacity expressions are also derived for direct, dual-hop, and multi-hop molecular communication scenarios. Simulation results are presented to corroborate the theoretical results derived and also, to yield insights into system performance.Comment: in preparatio

Reliable Crowdsourcing for Multi-Class Labeling using Coding Theory

Crowdsourcing systems often have crowd workers that perform unreliable work on the task they are assigned. In this paper, we propose the use of error-control codes and decoding algorithms to design crowdsourcing systems for reliable classification despite unreliable crowd workers. Coding-theory based techniques also allow us to pose easy-to-answer binary questions to the crowd workers. We consider three different crowdsourcing models: systems with independent crowd workers, systems with peer-dependent reward schemes, and systems where workers have common sources of information. For each of these models, we analyze classification performance with the proposed coding-based scheme. We develop an ordering principle for the quality of crowds and describe how system performance changes with the quality of the crowd. We also show that pairing among workers and diversification of the questions help in improving system performance. We demonstrate the effectiveness of the proposed coding-based scheme using both simulated data and real datasets from Amazon Mechanical Turk, a crowdsourcing microtask platform. Results suggest that use of good codes may improve the performance of the crowdsourcing task over typical majority-voting approaches.Comment: 20 pages, 11 figures, under revision, IEEE Journal of Selected Topics in Signal Processin

A Coupon-Collector Model of Machine-Aided Discovery

Empirical studies of scientific discovery---so-called Eurekometrics---have indicated that the output of exploration proceeds as a logistic growth curve. Although logistic functions are prevalent in explaining population growth that is resource-limited to a given carrying capacity, their derivation do not apply to discovery processes. This paper develops a generative model for logistic \emph{knowledge discovery} using a novel extension of coupon collection, where an explorer interested in discovering all unknown elements of a set is supported by technology that can respond to queries. This discovery process is parameterized by the novelty and quality of the set of discovered elements at every time step, and randomness is demonstrated to improve performance. Simulation results provide further intuition on the discovery process.Comment: 5 pages, 9 figures, 2017 KDD Workshop on Data-Driven Discover

Multi-object Classification via Crowdsourcing with a Reject Option

Consider designing an effective crowdsourcing system for an $M$-ary classification task. Crowd workers complete simple binary microtasks whose results are aggregated to give the final result. We consider the novel scenario where workers have a reject option so they may skip microtasks when they are unable or choose not to respond. For example, in mismatched speech transcription, workers who do not know the language may not be able to respond to microtasks focused on phonological dimensions outside their categorical perception. We present an aggregation approach using a weighted majority voting rule, where each worker's response is assigned an optimized weight to maximize the crowd's classification performance. We evaluate system performance in both exact and asymptotic forms. Further, we consider the setting where there may be a set of greedy workers that complete microtasks even when they are unable to perform it reliably. We consider an oblivious and an expurgation strategy to deal with greedy workers, developing an algorithm to adaptively switch between the two based on the estimated fraction of greedy workers in the anonymous crowd. Simulation results show improved performance compared with conventional majority voting.Comment: two column, 15 pages, 8 figures, submitted to IEEE Trans. Signal Proces

Wireless Compressive Sensing Over Fading Channels with Distributed Sparse Random Projections

We address the problem of recovering a sparse signal observed by a resource constrained wireless sensor network under channel fading. Sparse random matrices are exploited to reduce the communication cost in forwarding information to a fusion center. The presence of channel fading leads to inhomogeneity and non Gaussian statistics in the effective measurement matrix that relates the measurements collected at the fusion center and the sparse signal being observed. We analyze the impact of channel fading on nonuniform recovery of a given sparse signal by leveraging the properties of heavy-tailed random matrices. We quantify the additional number of measurements required to ensure reliable signal recovery in the presence of nonidentical fading channels compared to that is required with identical Gaussian channels. Our analysis provides insights into how to control the probability of sensor transmissions at each node based on the channel fading statistics in order to minimize the number of measurements collected at the fusion center for reliable sparse signal recovery. We further discuss recovery guarantees of a given sparse signal with any random projection matrix where the elements are sub-exponential with a given sub-exponential norm. Numerical results are provided to corroborate the theoretical findings

Phase Synchronization of Stimulated Raman Process in Optical Fiber For Long Pulse Regime

We investigate the evolution of coherence property of noise-seeded Stokes wave in short ( 1 ps) regimes. Nonlinear equations expressing the evolution of pump and Stokes wave are solved numerically for both the regions. The simulations include quantum noise by incorporating noise seed in the pump field where one photon per mode with random phase. The spectral phase fluctuations of the Stokes wave for both the regions, are characterized by performing multiple simulations and finally, the degrees of first-order mutual coherence are calculated as a function of wavelength for different conditions. Our statistical analysis proclaim that noise-seeded stimulated Raman process, which plays the role in degradation of coherence in short pulse region, exhibits strong phase synchronization in long pulse regime. The manifestation of phase synchronization occurs by the transition of the Stokes wave from incoherent to coherent spectra in long pulse regime

On the optimality of likelihood ratio test for prospect theory based binary hypothesis testing

In this letter, the optimality of the likelihood ratio test (LRT) is investigated for binary hypothesis testing problems in the presence of a behavioral decision-maker. By utilizing prospect theory, a behavioral decision-maker is modeled to cognitively distort probabilities and costs based on some weight and value functions, respectively. It is proved that the LRT may or may not be an optimal decision rule for prospect theory based binary hypothesis testing and conditions are derived to specify different scenarios. In addition, it is shown that when the LRT is an optimal decision rule, it corresponds to a randomized decision rule in some cases; i.e., nonrandomized LRTs may not be optimal. This is unlike Bayesian binary hypothesis testing in which the optimal decision rule can always be expressed in the form of a nonrandomized LRT. Finally, it is proved that the optimal decision rule for prospect theory based binary hypothesis testing can always be represented by a decision rule that randomizes at most two LRTs. Two examples are presented to corroborate the theoretical results.Comment: 5 pages, 2 figures, to appear in IEEE SP

Linear Coherent Estimation with Spatial Collaboration

A power constrained sensor network that consists of multiple sensor nodes and a fusion center (FC) is considered, where the goal is to estimate a random parameter of interest. In contrast to the distributed framework, the sensor nodes may be partially connected, where individual nodes can update their observations by (linearly) combining observations from other adjacent nodes. The updated observations are communicated to the FC by transmitting through a coherent multiple access channel. The optimal collaborative strategy is obtained by minimizing the expected mean-square-error subject to power constraints at the sensor nodes. Each sensor can utilize its available power for both collaboration with other nodes and transmission to the FC. Two kinds of constraints, namely the cumulative and individual power constraints are considered. The effects due to imperfect information about observation and channel gains are also investigated. The resulting performance improvement is illustrated analytically through the example of a homogeneous network with equicorrelated parameters. Assuming random geometric graph topology for collaboration, numerical results demonstrate a significant reduction in distortion even for a moderately connected network, particularly in the low local-SNR regime.Comment: 22 pages, 9 figures, submitted to IEEE Transactions on Information Theory, an earlier conference version can be found here: http://arxiv.org/abs/1205.328