Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach

The impulse response of wireless channels between the N-t transmit and N-r receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., NtNt the channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance.

Probing complex heterostructures using hard X-ray photoelectron spectroscopy (HAXPES)

X-ray Photoelectron Spectroscopy (XPS) plays a central role in the investigation of electronic properties as well as compositional analysis of almost every conceivable material. However, a very short inelastic mean free path (IMFP) and the limited photon flux in standard laboratory conditions render this technique very much surface sensitive. Thus, the electronic structure buried below several layers of a heterogeneous sample is not accessible with usual photoemission techniques. An obvious way to overcome this limitation is to use a considerably higher energy photon source, as this increases the IMFP of the photo-ejected electron, thereby making the technique more depth and bulk sensitive. Due to this obvious advantage, Hard X-ray Photo Electron Spectroscopy (HAXPES) is rapidly becoming an extremely powerful tool for chemical, elemental, compositional and electronic characterization of bulk systems, more so with reference to systems characterized by the presence of buried interfaces and other types of chemical heterogeneity. The relevance of such an investigative tool becomes evident when we specifically note the ever-increasing importance of heterostructures and interfaces in the context of a wide range of device applications, spanning electronic, magnetic, optical and energy applications. The interest in this nondestructive, element specific HAXPES technique has grown rapidly in the past few years; we discuss critically its extensive use in the study of depth resolved electronic properties of nanocrystals, multilayer superlattices and buried interfaces, revealing their internal structures. We specifically present a comparative discussion, with examples, on two most commonly used methods to determine internal structures of heterostructured systems using XPS. (C) 2015 Elsevier B.V. All rights reserved.

Probing the role of highly conserved residues in triosephosphate isomerase-analysis of site specific mutants at positions 64 and 75 in the Plasmodial enzyme

Highly conserved residues in enzymes are often found to be clustered close to active sites, suggesting that functional constraints dictate the nature of amino acid residues accommodated at these sites. Using the Plasmodiumfalciparum triosephosphate isomerase (PfTIM) enzyme () as a template, we have examined the effects of mutations at positions 64 and 75, which are not directly involved in the proton transfer cycle. Thr (T) occurring at position 75 is completely conserved, whereas only Gln (Q) and Glu (E) are accommodated at position 64. Biophysical and kinetic data are reported for four T75 (T75S/V/C/N) and two Q64 (Q64N/E) mutants. The dimeric structure is weakened in the Q64E and Q64N mutants, whereas dimer integrity is unimpaired in all four T75 mutants. Measurement of the concentration dependence of enzyme activity permits an estimate of K-d values for dimer dissociation (Q64N=73.79.2nm and Q64E=44.6 +/- 8.4nm). The T75S/V/C mutants have activities comparable to the wild-type enzyme, whereas a fourfold drop is observed for T75N. All four T75 mutants show a dramatic fall in activity between 35 degrees C and 45 degrees C. Crystal structure determination of the T75S/V/N mutants provides insights into the variations in local interactions, with the T75N mutant showing the largest changes. Hydrogen-bond interactions determine dimer stability restricting the choice of residues at position 64 to Gln (Q) and Glu (E). At position 75, the overwhelming preference for Thr (T) may be dictated by the imperative of maintaining temperature stability of enzyme activity.

In Situ Crystallographic Probing on Ameliorating Effect of Sulfide Additives and Carbon Grafting in Iron Electrodes

Beneficial effects of carbon grafting into the iron active material for rechargeable alkaline-iron-electrodes with and without Bi2S3 additive is probed by in situ X-ray diffraction in conjunction with Extended X-ray Absorption Fine Structure (EXAFS) and electrochemistry. EXAFS data unravel that the composition of pristine active material (PAM) for iron electrodes comprises 87% of magnetite and 13% of alpha-iron while carbon-grafted active material comprises 60% of magnetite and 40% of alpha-iron. In situ XRD patterns are recorded using a specially designed electrochemical cell. XRD data reflect that magnetite present in PAM iron electrode, without bismuth sulfide additive, is not reduced during charging while PAM iron electrode with bismuth sulfide additive is partially reduced to alpha-Fe/Fe(OH)(2). Interestingly, carbon-grafted-iron electrodes with bismuth sulfide exhibit complete conversion of active material to alpha-Fe/Fe(OH)2. The ameliorating effect of carbon grafting is substantiated by kinetic parameters obtained from steady-state potentiostatic polarization and Tafel plots. The mechanism for iron-electrode charge - discharge reactions are discussed in the light of the potential - pH diagrams for Fe - H2O, S - H2O and FeSads - H2O systems and it is surmised that carbon grafting into iron active material promotes its electrochemical utilization. (C) The Author(s) 2015. Published by ECS. All rights reserved.

Near-Optimal Detection Thresholds for Bayesian Spectrum Sensing Under Fading

This paper considers the problem of energy-based, Bayesian spectrum sensing in cognitive radios under various fading environments. Under the well-known central limit theorem based model for energy detection, we derive analytically tractable expressions for near-optimal detection thresholds that minimize the probability of error under lognormal, Nakagami-m, and Weibull fading. For the Suzuki fading case, a generalized gamma approximation is provided, which saves on the computation of an integral. In each case, the accuracy of the theoretical expressions as compared to the optimal thresholds are illustrated through simulations.

Probing 2D black phosphorus by quantum capacitance measurements

Two-dimensional materials and their heterostructures have emerged as a new class of materials, not only for fundamental physics but also for electronic and optoelectronic applications. Black phosphorus (BP) is a relatively new addition to this class of materials. Its strong in-plane anisotropy makes BP a unique material for making conceptually new types of electronic devices. However, the global density of states (DOS) of BP in device geometry has not been measured experimentally. Here, we report the quantum capacitance measurements together with the conductance measurements on an hBN-protected few-layer BP (similar to six layers) in a dual-gated field effect transistor (FET) geometry. The measured DOS from our quantum capacitance is compared with density functional theory (DFT). Our results reveal that the transport gap for quantum capacitance is smaller than that in conductance measurements due to the presence of localized states near the band edge. The presence of localized states is confirmed by the variable range hopping seen in our temperature dependence conductivity. A large asymmetry is observed between the electron and hole side. This asymmetric nature is attributed to the anisotropic band dispersion of BP. Our measurements establish the uniqueness of quantum capacitance in probing the localized states near the band edge, hitherto not seen in conductance measurements.

Probing Photoexcited Carriers in a Few-Layer MoS2 Laminate by Time-Resolved Optical Pump-Terahertz Probe Spectroscopy

We report the dynamics of photoinduced carriers in a free-standing MoS2 laminate consisting of a few layers (1-6 layers) using time-resolved optical pump-terahertz probe spectroscopy. Upon photoexcitation with the 800 nm pump pulse, the terahertz conductivity increases due to absorption by the photoinduced charge carriers. The relaxation of the non-equilibrium carriers shows fast as well as slow decay channels, analyzed using a rate equation model incorporating defect-assisted Auger scattering of photoexcited electrons, holes, and excitons. The fast relaxation time occurs due to the capture of electrons and holes by defects via Auger processes, resulting in nonradiative recombination. The slower relaxation arises since the excitons are bound to the defects, preventing the defect-assisted Auger recombination of the electrons and the holes. Our results provide a comprehensive understanding of the non-equilibrium carrier kinetics in a system of unscreened Coulomb interactions, where defect-assisted Auger processes dominate and should be applicable to other 2D systems.

Error exponent analysis of energy-based Bayesian decentralized spectrum sensing under fading

This paper considers decentralized spectrum sensing, i.e., detection of occupancy of the primary users' spectrum by a set of Cognitive Radio (CR) nodes, under a Bayesian set-up. The nodes use energy detection to make their individual decisions, which are combined at a Fusion Center (FC) using the K-out-of-N fusion rule. The channel from the primary transmitter to the CR nodes is assumed to undergo fading, while that from the nodes to the FC is assumed to be error-free. In this scenario, a novel concept termed as the Error Exponent with a Confidence Level (EECL) is introduced to evaluate and compare the performance of different detection schemes. Expressions for the EECL under general fading conditions are derived. As a special case, it is shown that the conventional error exponent both at individual sensors, and at the FC is zero. Further, closed-form lower bounds on the EECL are derived under Rayleigh fading and lognormal shadowing. As an example application, it answers the question of whether to use pilot-signal based narrowband sensing, where the signal undergoes Rayleigh fading, or to sense over the entire bandwidth of a wideband signal, where the signal undergoes lognormal shadowing. Theoretical results are validated using Monte Carlo simulations. (C) 2015 Elsevier B.V. All rights reserved.

Bayesian Online Changepoint Detection

Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techniques, most Bayesian papers have focused on the retrospective segmentation problem. Here we examine the case where the model parameters before and after the changepoint are independent and we derive an online algorithm for exact inference of the most recent changepoint. We compute the probability distribution of the length of the current ``run,'' or time since the last changepoint, using a simple message-passing algorithm. Our implementation is highly modular so that the algorithm may be applied to a variety of types of data. We illustrate this modularity by demonstrating the algorithm on three different real-world data sets.

Nonparametric Bayesian Density Modeling with Gaussian Processes

We present the Gaussian process density sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a distribution defined by a density that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We describe two such MCMC methods. Both methods also allow inference of the hyperparameters of the Gaussian process.