Near-optimal large-MIMO detection using randomized MCMC and randomized search algorithms

Low-complexity near-optimal detection of signals in MIMO systems with large number (tens) of antennas is getting increased attention. In this paper, first, we propose a variant of Markov chain Monte Carlo (MCMC) algorithm which i) alleviates the stalling problem encountered in conventional MCMC algorithm at high SNRs, and ii) achieves near-optimal performance for large number of antennas (e.g., 16×16, 32×32, 64×64 MIMO) with 4-QAM. We call this proposed algorithm as randomized MCMC (R-MCMC) algorithm. Second, we propose an other algorithm based on a random selection approach to choose candidate vectors to be tested in a local neighborhood search. This algorithm, which we call as randomized search (RS) algorithm, also achieves near-optimal performance for large number of antennas with 4-QAM. The complexities of the proposed R-MCMC and RS algorithms are quadratic/sub-quadratic in number of transmit antennas, which are attractive for detection in large-MIMO systems. We also propose message passing aided R-MCMC and RS algorithms, which are shown to perform well for higher-order QAM.

Unsupervised feature selection for outlier detection in categorical data using mutual information

Outlier detection in high dimensional categorical data has been a problem of much interest due to the extensive use of qualitative features for describing the data across various application areas. Though there exist various established methods for dealing with the dimensionality aspect through feature selection on numerical data, the categorical domain is actively being explored. As outlier detection is generally considered as an unsupervised learning problem due to lack of knowledge about the nature of various types of outliers, the related feature selection task also needs to be handled in a similar manner. This motivates the need to develop an unsupervised feature selection algorithm for efficient detection of outliers in categorical data. Addressing this aspect, we propose a novel feature selection algorithm based on the mutual information measure and the entropy computation. The redundancy among the features is characterized using the mutual information measure for identifying a suitable feature subset with less redundancy. The performance of the proposed algorithm in comparison with the information gain based feature selection shows its effectiveness for outlier detection. The efficacy of the proposed algorithm is demonstrated on various high-dimensional benchmark data sets employing two existing outlier detection methods.

Detection limit of etched fiber bragg grating sensors

While Fiber Bragg Grating (FBG) sensors have been extensively used for temperature and strain sensing, clad etched FBGs (EFBGs) have only recently been explored for refractive index sensing. Prior literature in EFBG based refractive index sensing predominantly deals with bulk refractometry only, where the Bragg wavelength shift of the sensor as a function of the bulk refractive index of the sample can be analytically modeled, unlike the situation for adsorption of molecular thin films on the sensor surface. We used a finite element model to calculate the Bragg wavelength change as a function of thickness and refractive index of the adsorbing molecular layer and compared the model with the real-time, in-situ measurement of electrostatic layer-by-layer (LbL) assembly of weak polyelectrolytes on the silica surface of EFBGs. We then used this model to calculate the layer thickness of LbL films and found them to be in agreement with literature. Further, we used this model to arrive at a realistic estimate of the limit of detection of EFBG sensors based on nominal measurement noise levels in current FBG interrogation systems and found that sufficiently thinned EFBGs can provide a competitive platform for real-time measurement of molecular interactions while simultaneously leveraging the high multiplexing capabilities of fiber optics.

Joint Self-Iterating Equalization and Detection for Two-Dimensional Intersymbol-Interference Channels

We develop several novel signal detection algorithms for two-dimensional intersymbol-interference channels. The contribution of the paper is two-fold: (1) We extend the one-dimensional maximum a-posteriori (MAP) detection algorithm to operate over multiple rows and columns in an iterative manner. We study the performance vs. complexity trade-offs for various algorithmic options ranging from single row/column non-iterative detection to a multi-row/column iterative scheme and analyze the performance of the algorithm. (2) We develop a self-iterating 2-D linear minimum mean-squared based equalizer by extending the 1-D linear equalizer framework, and present an analysis of the algorithm. The iterative multi-row/column detector and the self-iterating equalizer are further connected together within a turbo framework. We analyze the combined 2-D iterative equalization and detection engine through analysis and simulations. The performance of the overall equalizer and detector is near MAP estimate with tractable complexity, and beats the Marrow Wolf detector by about at least 0.8 dB over certain 2-D ISI channels. The coded performance indicates about 8 dB of significant SNR gain over the uncoded 2-D equalizer-detector system.

A Guided Wave approach for the detection of damage in a structure having elements with periodic damage

A wave propagation based approach for the detection of damage in components of structures having periodic damage has been proposed. Periodic damage pattern may arise in a structure due to periodicity in geometry and in loading. The method exploits the Block-Floquet band formation mechanism, a feature specific to structures with periodicity, to identify propagation bands (pass bands) and attenuation bands (stop bands) at different frequency ranges. The presence of damage modifies the wave propagation behaviour forming these bands. With proper positioning of sensors a damage force indicator (DFI) method can be used to locate the defect at an accuracy level of sensor to sensor distance. A wide range of transducer frequency may be used to obtain further information about the shape and size of the damage. The methodology is demonstrated using a few 1-D structures with different kinds of periodicity and damage. For this purpose, dynamic stiffness matrix is formed for the periodic elements to obtain the dispersion relationship using frequency domain spectral element and spectral super element method. The sensitivity of the damage force indicator for different types of periodic damages is also analysed.

A Novel Monte-Carlo-Sampling-Based Receiver for Large-Scale Uplink Multiuser MIMO Systems

In this paper, we propose low-complexity algorithms based on Monte Carlo sampling for signal detection and channel estimation on the uplink in large-scale multiuser multiple-input-multiple-output (MIMO) systems with tens to hundreds of antennas at the base station (BS) and a similar number of uplink users. A BS receiver that employs a novel mixed sampling technique (which makes a probabilistic choice between Gibbs sampling and random uniform sampling in each coordinate update) for detection and a Gibbs-sampling-based method for channel estimation is proposed. The algorithm proposed for detection alleviates the stalling problem encountered at high signal-to-noise ratios (SNRs) in conventional Gibbs-sampling-based detection and achieves near-optimal performance in large systems with M-ary quadrature amplitude modulation (M-QAM). A novel ingredient in the detection algorithm that is responsible for achieving near-optimal performance at low complexity is the joint use of a mixed Gibbs sampling (MGS) strategy coupled with a multiple restart (MR) strategy with an efficient restart criterion. Near-optimal detection performance is demonstrated for a large number of BS antennas and users (e. g., 64 and 128 BS antennas and users). The proposed Gibbs-sampling-based channel estimation algorithm refines an initial estimate of the channel obtained during the pilot phase through iterations with the proposed MGS-based detection during the data phase. In time-division duplex systems where channel reciprocity holds, these channel estimates can be used for multiuser MIMO precoding on the downlink. The proposed receiver is shown to achieve good performance and scale well for large dimensions.

Noncoherent Integration for Signal Detection: Analysis Under Model Uncertainties

The performance of postdetection integration (PDI) techniques for the detection of Global Navigation Satellite Systems (GNSS) signals in the presence of uncertainties in frequency offsets, noise variance, and unknown data-bits is studied. It is shown that the conventional PDI techniques are generally not robust to uncertainty in the data-bits and/or the noise variance. Two new modified PDI techniques are proposed, and they are shown to be robust to these uncertainties. The receiver operating characteristics (ROC) and sample complexity performance of the PDI techniques in the presence of model uncertainties are analytically derived. It is shown that the proposed methods significantly outperform existing methods, and hence they could become increasingly important as the GNSS receivers attempt to push the envelope on the minimum signal-to-noise ratio (SNR) for reliable detection.

Cooperative Particle Swarm Optimization Based Receiver for Large-Dimension MIMO-ZPSC Systems

In this paper, we propose a cooperative particle swarm optimization (CPSO) based channel estimation/equalization scheme for multiple-input multiple-output zero-padded single-carrier (MIMO-ZPSC) systems with large dimensions in frequency selective channels. We estimate the channel state information at the receiver in time domain using a PSO based algorithm during training phase. Using the estimated channel, we perform information symbol detection in the frequency domain using FFT based processing. For this detection, we use a low complexity OLA (OverLap Add) likelihood ascent search equalizer which uses minimum mean square (MMSE) equalizer solution as the initial solution. Multiple iterations between channel estimation and data detection are carried out which significantly improves the mean square error and bit error rate performance of the receiver.

Signal Detection in Generalized Gaussian Noise by Nonlinear Wavelet Denoising

In this paper, a nonlinear suboptimal detector whose performance in heavy-tailed noise is significantly better than that of the matched filter is proposed. The detector consists of a nonlinear wavelet denoising filter to enhance the signal-to-noise ratio, followed by a replica correlator. Performance of the detector is investigated through an asymptotic theoretical analysis as well as Monte Carlo simulations. The proposed detector offers the following advantages over the optimal (in the Neyman-Pearson sense) detector: it is easier to implement, and it is more robust with respect to error in modeling the probability distribution of noise.

Detection of Target DNA using Photo-Reactive Protoporphyrin Moeity on a Nanocomposite Substrate

Detection of pathogens from infected biological samples through conventional process involves cell lysis and purification. The main objective of this work is to minimize the time and sample loss, as well as to increase the efficiency of detection of biomolecules. Electrical lysis of medical sample is performed in a closed microfluidic channel in a single integrated platform where the downstream analysis of the sample is possible. The device functions involve, in a sequence, flow of lysate from lysis chamber passed through a thermal denaturation counter where dsDNA is denatured to ssDNA, which is controlled by heater unit. A functionalized binding chamber of ssDNA is prepared by using ZnO nanorods as the matrix and functionalized with bifunctional carboxylic acid, 16-(2-pyridyldithiol) hexadecanoic acid (PDHA) which is further attached to a linker molecule 1-ethyl-3-(3-dimethylaminopropyl) (EDC). Linker moeity is then covalently bound to photoreactive protoporphyrin (PPP) molecule. The photolabile molecule protoporphyrin interacts with -NH2 labeled single stranded DNA (ssDNA) which thus acts as a probe to detect complimentary ssDNA from target organisms. Thereafter the bound DNA with protoporphyrin is exposed to an LED of particular wavelength for a definite period of time and DNA was eluted and analyzed. UV/Vis spectroscopic analysis at 260/280 nm wavelength confirms the purity and peak at 260 nm is reconfirmed for the elution of target DNA. Quantitative and qualitative data obtained from the current experiments show highly selective detection of biomolecule such as DNA which have large number of future applications in Point-of-Care devices.

Channel Hardening-Exploiting Message Passing (CHEMP) Receiver in Large-Scale MIMO Systems

In this paper, we propose a multiple-input multiple-output (MIMO) receiver algorithm that exploits channel hardening that occurs in large MIMO channels. Channel hardening refers to the phenomenon where the off-diagonal terms of the matrix become increasingly weaker compared to the diagonal terms as the size of the channel gain matrix increases. Specifically, we propose a message passing detection (MPD) algorithm which works with the real-valued matched filtered received vector (whose signal term becomes, where is the transmitted vector), and uses a Gaussian approximation on the off-diagonal terms of the matrix. We also propose a simple estimation scheme which directly obtains an estimate of (instead of an estimate of), which is used as an effective channel estimate in the MPD algorithm. We refer to this receiver as the channel hardening-exploiting message passing (CHEMP) receiver. The proposed CHEMP receiver achieves very good performance in large-scaleMIMO systems (e.g., in systems with 16 to 128 uplink users and 128 base station antennas). For the considered large MIMO settings, the complexity of the proposed MPD algorithm is almost the same as or less than that of the minimum mean square error (MMSE) detection. This is because the MPD algorithm does not need a matrix inversion. It also achieves a significantly better performance compared to MMSE and other message passing detection algorithms using MMSE estimate of. Further, we design optimized irregular low density parity check (LDPC) codes specific to the considered large MIMO channel and the CHEMP receiver through EXIT chart matching. The LDPC codes thus obtained achieve improved coded bit error rate performance compared to off-the-shelf irregular LDPC codes.

Third post-Newtonian gravitational waveforms for compact binary systems in general orbits: Instantaneous terms

We compute the instantaneous contributions to the spherical harmonic modes of gravitational waveforms from compact binary systems in general orbits up to the third post-Newtonian (PN) order. We further extend these results for compact binaries in quasielliptical orbits using the 3PN quasi-Keplerian representation of the conserved dynamics of compact binaries in eccentric orbits. Using the multipolar post-Minkowskian formalism, starting from the different mass and current-type multipole moments, we compute the spin-weighted spherical harmonic decomposition of the instantaneous part of the gravitational waveform. These are terms which are functions of the retarded time and do not depend on the history of the binary evolution. Together with the hereditary part, which depends on the binary's dynamical history, these waveforms form the basis for construction of accurate templates for the detection of gravitational wave signals from binaries moving in quasielliptical orbits.

Detection of a quantum particle on a lattice under repeated projective measurements

We consider a quantum particle, moving on a lattice with a tight-binding Hamiltonian, which is subjected to measurements to detect its arrival at a particular chosen set of sites. The projective measurements are made at regular time intervals tau, and we consider the evolution of the wave function until the time a detection occurs. We study the probabilities of its first detection at some time and, conversely, the probability of it not being detected (i.e., surviving) up to that time. We propose a general perturbative approach for understanding the dynamics which maps the evolution operator, which consists of unitary transformations followed by projections, to one described by a non-Hermitian Hamiltonian. For some examples of a particle moving on one-and two-dimensional lattices with one or more detection sites, we use this approach to find exact expressions for the survival probability and find excellent agreement with direct numerical results. A mean-field model with hopping between all pairs of sites and detection at one site is solved exactly. For the one-and two-dimensional systems, the survival probability is shown to have a power-law decay with time, where the power depends on the initial position of the particle. Finally, we show an interesting and nontrivial connection between the dynamics of the particle in our model and the evolution of a particle under a non-Hermitian Hamiltonian with a large absorbing potential at some sites.

Generalized Spatial Modulation in Large-Scale Multiuser MIMO Systems

Generalized spatial modulation (GSM) uses n(t) transmit antenna elements but fewer transmit radio frequency (RF) chains, n(rf). Spatial modulation (SM) and spatial multiplexing are special cases of GSM with n(rf) = 1 and n(rf) = n(t), respectively. In GSM, in addition to conveying information bits through n(rf) conventional modulation symbols (for example, QAM), the indices of the n(rf) active transmit antennas also convey information bits. In this paper, we investigate GSM for large-scale multiuser MIMO communications on the uplink. Our contributions in this paper include: 1) an average bit error probability (ABEP) analysis for maximum-likelihood detection in multiuser GSM-MIMO on the uplink, where we derive an upper bound on the ABEP, and 2) low-complexity algorithms for GSM-MIMO signal detection and channel estimation at the base station receiver based on message passing. The analytical upper bounds on the ABEP are found to be tight at moderate to high signal-to-noise ratios (SNR). The proposed receiver algorithms are found to scale very well in complexity while achieving near-optimal performance in large dimensions. Simulation results show that, for the same spectral efficiency, multiuser GSM-MIMO can outperform multiuser SM-MIMO as well as conventional multiuser MIMO, by about 2 to 9 dB at a bit error rate of 10(-3). Such SNR gains in GSM-MIMO compared to SM-MIMO and conventional MIMO can be attributed to the fact that, because of a larger number of spatial index bits, GSM-MIMO can use a lower-order QAM alphabet which is more power efficient.

Minimum-Error-Probability CFO Estimation for Multiuser MIMO-OFDM Systems

We consider carrier frequency offset (CFO) estimation in the context of multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems over noisy frequency-selective wireless channels with both single- and multiuser scenarios. We conceived a new approach for parameter estimation by discretizing the continuous-valued CFO parameter into a discrete set of bins and then invoked detection theory, analogous to the minimum-bit-error-ratio optimization framework for detecting the finite-alphabet received signal. Using this radical approach, we propose a novel CFO estimation method and study its performance using both analytical results and Monte Carlo simulations. We obtain expressions for the variance of the CFO estimation error and the resultant BER degradation with the single- user scenario. Our simulations demonstrate that the overall BER performance of a MIMO-OFDM system using the proposed method is substantially improved for all the modulation schemes considered, albeit this is achieved at increased complexity.