Probe Selection Algorithm Research Articles

A Review of Over-the-Air Testing Methods for Performance Measurement of Antennas and Devices in Communication Systems

With the fast development of communication techniques, over-the-air testing has developed into the main testing method for current communication systems due to its advantages including no cable connection, high measurement efficiency, and independence from environment. OTA testing has become almost the only solution for 5G system testing especially after the popularity of 5G massive multiple-input-multiple-output. In this paper, we summarize the three main methods of OTA testing including the reverberation chamber-based method, the radiation two-step method, and the multiprobe anechoic chamber- (MPAC-) based method, among which we focus on the MPAC method systematically. In the MPAC method, there are two main channel synthesis techniques that have been heavily studied, namely, prefaded signal synthesis and plane wave synthesis. We summarize the current main research based on these two techniques including probe configurations, probe selection algorithms, and probe design based on the PFS technique and the design of plane wave generators based on PWS technique. The main comparisons are made for the performance of different probe selection algorithms and the performance of different plane wave generators as well as the latest advances.

On dynamic channel emulation in sector MPAC for over-the-air testing of beamformed massive MIMO devices

In this article, novel emulation strategies for the sectored multiple probe anechoic chamber (SMPAC) are proposed to enable the reliable evaluation of the massive multiple-input multiple-output (MIMO) device operating at beamforming mode, which requires a realistic non-stationary channel environment. For the dynamic propagation emulation, an efficient closed-form probe weighting strategy minimizing the power angular spectrum (PAS) emulation errors is derived, substantially reducing the associated computational complexity. On the other hand, a novel probe selection algorithm is proposed to reproduce a more accurate fading environment. Various standard channel models and setup configurations are comprehensively simulated to validate the capacity of the proposed methods. The simulation results show that more competent active probes are selected with the proposed method compared to the conventional algorithms. Furthermore, the derived closed-form probe weighting strategy offers identical accuracy to that obtained with complicated numerical optimization. Moreover, a realistic dynamic channel measured in an indoor environment is reconstructed with the developed methodologies, and 95.6% PAS similarity can be achieved with 6 active probes. The satisfactory results demonstrate that the proposed algorithms are suitable for arbitrary channel emulation.

Probabilistic probe selection algorithm for fault diagnosis in communication networks

Since faults unavoidably occur in communication networks, it is important to quickly and accurately localize the faults to ensure the stability, consistency and reliability of communication networks. Most existing works are focused on probe selection for fault diagnosis in the deterministic environment (DE). However, the information about communication networks in real applications is not deterministic due to disturbance, noise and so on, especially large scale networks. Therefore, it is urgent to solve the problem of localizing faults for communication networks in the non-deterministic environment (NDE). In this paper, we propose a probabilistic probe selection (PRPS) algorithm for network fault diagnosis in the NDE. Combining the probability language and node coverage, we use the probabilistic greedy probe selection approach to select the probes for fault detection with less probing cost to cover all network nodes in the NDE. When one or more faults are detected in the network, the probabilistic min search probe selection approach will be triggered to localize the faults. This approach uses min search method to select probes that pass through the minimum number of suspicious nodes and the maximum sum of coverage of suspicious nodes. In order to improve the ability to resist the uncertain factors, this approach updates the node state sets for multiple iterations according to the states of identified nodes. Extensive simulation experiments are conducted to compare our PRPS algorithm with the existing works and evaluate the performance on various network topologies with different parameters. The simulation results show that PRPS algorithm has satisfactory fault localization accuracy, low probing cost and strong adaptability to resist the uncertain factors in the NDE.

Probe Selection for 5G Massive MIMO Base Station Over-the-Air Testing

A sectored multiprobe anechoic chamber is seen as a promising method to carry out the radiated testing of massive multiple-input–multiple-output (MIMO) base station (BS), and realistic channel environment reconstruction is crucial for the performance assessment of wireless devices, especially for the testing of beamforming procedures, such as beam acquisition, tracking, and so on. This letter proposes a novel probe selection algorithm and corresponding device rotation scheme to enable more accurate channel emulation with specific probe antennas. On the other hand, within the acceptable threshold of error, the number of required probe antennas can be reduced, which will mitigate setup complexity and the number of hardware resources. In the simulation, to verify the robustness of the proposed probe configuration, several metrics are examined with different numbers of selected probes, in terms of spatial correlation and power angular spectrum emulation. The simulation results show that the proposed selection and rotation algorithms perform well in synthesizing the accurate target propagation environment on the BS side.

Probe generation for active probing

SummaryActive probing is a widely adopted approach for developing effective solutions for network monitoring and diagnosing. However, the use of probing techniques incurs costs in terms of additional network traffic. Furthermore, probing stations are required to be configured and maintained in the network for sending out probes. The set of probes used for fault detection and/or diagnosis (called the target probe set) is selected by a probe selection algorithm from a larger set called the candidate probe set. Most of the existing techniques for selecting the target probe set assume that the candidate probe set will preexist and the set is determined by the configured routing model in the network. In this paper, we address the problem of generating an expanded candidate probe set, which results in the selection of a more efficient target probe set. We propose the use of heuristics and network partitioning strategies for generating the candidate probe set. For evaluating our approach, we perform experiments to generate candidate probe sets for the networks of several types and sizes. The candidate probe sets are used by the existing probe selection algorithms for selecting target probe sets for fault detection and localization. Our results demonstrate that the target probe set selected from the candidate probe set generated using our approach has a reduced cost of monitoring the network.

Probe Selection in Multiprobe OTA Setups

Standardization work for over-the-air (OTA) testing of multiple-input-multiple-output (MIMO) capable terminals is currently ongoing in COST IC1004, 3GPP, and CTIA, where a multiprobe anechoic chamber based method is a promising candidate. Setting up a multiprobe configuration with channel emulators is costly, so finding ways to limit the number of probes while still reproducing the target channels accurately could make the test system both cheaper and simpler to implement. Several probe selection algorithms are presented in this paper to address this issue. The proposed techniques provide a probe selection framework for the channel emulation techniques published in the literature. Simulation results show that good channel emulation accuracy can be achieved with the selected subset of probes for the considered target channel models. The probe selection algorithm is further supported by measurement results in a practical multiprobe setup.

Flexible and efficient genome tiling design with penalized uniqueness score

BackgroundAs a powerful tool in whole genome analysis, tiling array has been widely used in the answering of many genomic questions. Now it could also serve as a capture device for the library preparation in the popular high throughput sequencing experiments. Thus, a flexible and efficient tiling array design approach is still needed and could assist in various types and scales of transcriptomic experiment.ResultsIn this paper, we address issues and challenges in designing probes suitable for tiling array applications and targeted sequencing. In particular, we define the penalized uniqueness score, which serves as a controlling criterion to eliminate potential cross-hybridization, and a flexible tiling array design pipeline. Unlike BLAST or simple suffix array based methods, computing and using our uniqueness measurement can be more efficient for large scale design and require less memory. The parameters provided could assist in various types of genomic tiling task. In addition, using both commercial array data and experiment data we show, unlike previously claimed, that palindromic sequence exhibiting relatively lower uniqueness.ConclusionsOur proposed penalized uniqueness score could serve as a better indicator for cross hybridization with higher sensitivity and specificity, giving more control of expected array quality. The flexible tiling design algorithm incorporating the penalized uniqueness score was shown to give higher coverage and resolution. The package to calculate the penalized uniqueness score and the described probe selection algorithm are implemented as a Perl program, which is freely available at http://www1.fbn-dummerstorf.de/en/forschung/fbs/fb3/paper/2012-yang-1/OTAD.v1.1.tar.gz.

Efficient probe selection for fault localization using the property of submodularity

As the computer network increasingly grows larger and more complex, fault diagnosis has become a challenging task. Active probing is an efficient tool for fault localization. By implementing some test programs and analyzing the results, active-probing-based techniques can perform diagnosis efficiently and adaptively. Because probes may generate additional traffic overhead, it is important to appropriately select small number of probes to reach the desired diagnostic capability. However, the computation of probe selection problem in such environment is extremely expensive. Most of the past works purchase the speed at the cost of diagnostic accuracy. In this paper, we first verify that probe selection problem satisfies the property of submodularity. Then we take the use of the property and develop a submodularity-based selection algorithm with following novel features: (i) it is cost effective, failure resistant and more accurate; (ii) it could deal with the uncertainties about the network structures and the observations; and (iii) it can select the required probes in near-linear time. Finally, we implement submodularity-based selection algorithm and other two representative probe selection algorithms (bounded path enumeration approximation algorithm and greedy search algorithm) on different settings of networks. The results have shown how the new algorithm outperforms the former two algorithms. Copyright © 2011 John Wiley & Sons, Ltd.

ChipD: a web tool to design oligonucleotide probes for high-density tiling arrays

chipD is a web server that facilitates design of DNA oligonucleotide probes for high-density tiling arrays, which can be used in a number of genomic applications such as ChIP-chip or gene-expression profiling. The server implements a probe selection algorithm that takes as an input, in addition to the target sequences, a set of parameters that allow probe design to be tailored to specific applications, protocols or the array manufacturer’s requirements. The algorithm optimizes probes to meet three objectives: (i) probes should be specific; (ii) probes should have similar thermodynamic properties; and (iii) the target sequence coverage should be homogeneous and avoid significant gaps. The output provides in a text format, the list of probe sequences with their genomic locations, targeted strands and hybridization characteristics. chipD has been used successfully to design tiling arrays for bacteria and yeast. chipD is available at http://chipd.uwbacter.org/.

Empirical Evaluation of Oligonucleotide Probe Selection for DNA Microarrays

DNA-based microarrays are increasingly central to biomedical research. Selecting oligonucleotide sequences that will behave consistently across experiments is essential to the design, production and performance of DNA microarrays. Here our aim was to improve on probe design parameters by empirically and systematically evaluating probe performance in a multivariate context. We used experimental data from 19 array CGH hybridizations to assess the probe performance of 385,474 probes tiled in the Duchenne muscular dystrophy (DMD) region of the X chromosome. Our results demonstrate that probe melting temperature, single nucleotide polymorphisms (SNPs), and homocytosine motifs all have a strong effect on probe behavior. These findings, when incorporated into future microarray probe selection algorithms, may improve microarray performance for a wide variety of applications.

Efficient oligonucleotide probe selection for pan-genomic tiling arrays

BackgroundArray comparative genomic hybridization is a fast and cost-effective method for detecting, genotyping, and comparing the genomic sequence of unknown bacterial isolates. This method, as with all microarray applications, requires adequate coverage of probes targeting the regions of interest. An unbiased tiling of probes across the entire length of the genome is the most flexible design approach. However, such a whole-genome tiling requires that the genome sequence is known in advance. For the accurate analysis of uncharacterized bacteria, an array must query a fully representative set of sequences from the species' pan-genome. Prior microarrays have included only a single strain per array or the conserved sequences of gene families. These arrays omit potentially important genes and sequence variants from the pan-genome.ResultsThis paper presents a new probe selection algorithm (PanArray) that can tile multiple whole genomes using a minimal number of probes. Unlike arrays built on clustered gene families, PanArray uses an unbiased, probe-centric approach that does not rely on annotations, gene clustering, or multi-alignments. Instead, probes are evenly tiled across all sequences of the pan-genome at a consistent level of coverage. To minimize the required number of probes, probes conserved across multiple strains in the pan-genome are selected first, and additional probes are used only where necessary to span polymorphic regions of the genome. The viability of the algorithm is demonstrated by array designs for seven different bacterial pan-genomes and, in particular, the design of a 385,000 probe array that fully tiles the genomes of 20 different Listeria monocytogenes strains with overlapping probes at greater than twofold coverage.ConclusionPanArray is an oligonucleotide probe selection algorithm for tiling multiple genome sequences using a minimal number of probes. It is capable of fully tiling all genomes of a species on a single microarray chip. These unique pan-genome tiling arrays provide maximum flexibility for the analysis of both known and uncharacterized strains.

Active probing based Internet service fault management in uncertain and noisy environment

In Internet service fault management based on active probing, uncertainty and noises will affect service fault management. In order to reduce the impact, challenges of Internet service fault management are analyzed in this paper. Bipartite Bayesian network is chosen to model the dependency relationship between faults and probes, binary symmetric channel is chosen to model noises, and a service fault management approach using active probing is proposed for such an environment. This approach is composed of two phases: fault detection and fault diagnosis. In first phase, we propose a greedy approximation probe selection algorithm (GAPSA), which selects a minimal set of probes while remaining a high probability of fault detection. In second phase, we propose a fault diagnosis probe selection algorithm (FDPSA), which selects probes to obtain more system information based on the symptoms observed in previous phase. To deal with dynamic fault set caused by fault recovery mechanism, we propose a hypothesis inference algorithm based on fault persistent time statistic (FPTS). Simulation results prove the validity and efficiency of our approach.

Efficient probe selection algorithms for fault diagnosis

Increase in the network usage for more and more performance critical applications has caused a demand for tools that can monitor network health with minimum management traffic. Adaptive probing has the potential to provide effective tools for end-to-end monitoring and fault diagnosis over a network. Adaptive probing based algorithms adapt the probe set to localize faults in the network by sending less probes in healthy areas and more probes in the suspected areas of failure. In this paper we present adaptive probing tools that meet the requirements to provide an effective and efficient solution for fault diagnosis for modern communication systems. We present a system architecture for adaptive probing based fault diagnosis tool and propose algorithms for probe selection to perform failure detection and fault localization. We compare the performance and efficiency of the proposed algorithms through simulation results.

Randomized probe selection algorithm for microarray design

DNA microarray technology, originally developed to measure the level of gene expression, has become one of the most widely used tools in genomic study. The crux of microarray design lies in how to select a unique probe that distinguishes a given genomic sequence from other sequences. Due to its significance, probe selection attracts a lot of attention. Various probe selection algorithms have been developed in recent years. Good probe selection algorithms should produce a small number of candidate probes. Efficiency is also crucial because the data involved are usually huge. Most existing algorithms are usually not sufficiently selective and quite a large number of probes are returned. We propose a new direction to tackle the problem and give an efficient algorithm based on randomization to select a small set of probes and demonstrate that such a small set of probes is sufficient to distinguish each sequence from all the other sequences. Based on the algorithm, we have developed probe selection software R andPS, which runs efficiently in practice. The software is available on our website ( http://www.csc.liv.ac.uk/ ∼ cindy/RandPS/RandPS.htm ). We test our algorithm via experiments on different genomes ( Escherichia coli, Saccharamyces cerevisiae, etc.) and our algorithm is able to output unique probes for most of the genes efficiently. The other genes can be identified by a combination of at most two probes.

Probe Selection and Expression Index Computation of Affymetrix Exon Arrays

BackgroundThere is great current interest in developing microarray platforms for measuring mRNA abundance at both gene level and exon level. The Affymetrix Exon Array is a new high-density gene expression microarray platform, with over six million probes targeting all annotated and predicted exons in a genome. An important question for the analysis of exon array data is how to compute overall gene expression indexes. Because of the complexity of the design of exon array probes, this problem is different in nature from summarizing gene-level expression from traditional 3′ expression arrays.Methodology/Principal FindingsIn this manuscript, we use exon array data from 11 human tissues to study methods for computing gene-level expression. We showed that for most genes there is a subset of exon array probes having highly correlated intensities across multiple samples. We suggest that these probes could be used as reliable indicators of overall gene expression levels. We developed a probe selection algorithm to select such a subset of highly correlated probes for each gene, and computed gene expression indexes using the selected probes.Conclusions/SignificanceOur results demonstrate that probe selection improves gene expression estimates from exon arrays. The selected probes can be used in future analyses of other exon array datasets to compute gene expression indexes.

Probe selection algorithm for oligonucleotide array-based medium-resolution genotyping

Medium-resolution genotyping has the goal of distinguishing different subgroups instead of each element in a group. An oligonucleotide array provides an inexpensive, high-throughput method to identify differences in DNA sequence among individuals, which is fundamental for genotyping. As the cost and difficulty of designing and fabricating the oligonucleotide array dramatically increase with the number of probes used, it is therefore important to have a design with a minimum number of probes meeting the requirement of medium-resolution genotyping. The first algorithm for designing and selecting probes for oligonucleotide array-based medium-resolution typing is reported. The goal in deriving the algorithm was to select a minimum number of probes from a large probe set on the premise of minimum loss of resolution. The algorithm, which was based on entropy, conditional entropy and mutual information theory, was used to select the minimum number of probes from a large probe set. The algorithm was tested on a human leukocyte antigen (HLA) sequence data set Thirty probes were selected from 390 probes for HLA-A, and 60 probes were selected from 767 probes for HLA-B. Although the number of probes was reduced by almost ten times, the distinguishability was reduced only a little, by 0.45% (from 99.90% to 99.45%) for HLA-A and 0.27% (from 99.84% to 99.57%) for HLA-B, respectively. This is a satisfactory and practical result.

Probe selection algorithms with applications in the analysis of microbial communities.

We propose two efficient heuristics for minimizing the number of oligonucleotide probes needed for analyzing populations of ribosomal RNA gene (rDNA) clones by hybridization experiments on DNA microarrays. Such analyses have applications in the study of microbial communities. Unlike in the classical SBH (sequencing by hybridization) procedure, where multiple probes are on a DNA chip, in our applications we perform a series of experiments, each one consisting of applying a single probe to a DNA microarray containing a large sample of rDNA sequences from the studied population. The overall cost of the analysis is thus roughly proportional to the number of experiments, underscoring the need for minimizing the number of probes. Our algorithms are based on two well-known optimization techniques, i.e. simulated annealing and Lagrangian relaxation, and our preliminary tests demonstrate that both algorithms are able to find satisfactory probe sets for real rDNA data.