Relevant bibliographies by topics / Channel prediction

Academic literature on the topic 'Channel prediction'

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Published: 4 June 2021
Last updated: 28 January 2023

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Journal articles on the topic "Channel prediction"

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Gao, Jianzhao, Hong Wei, Alberto Cano, and Lukasz Kurgan. "PSIONplusm Server for Accurate Multi-Label Prediction of Ion Channels and Their Types." Biomolecules 10, no. 6 (June 7, 2020): 876. http://dx.doi.org/10.3390/biom10060876.

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Computational prediction of ion channels facilitates the identification of putative ion channels from protein sequences. Several predictors of ion channels and their types were developed in the last quindecennial. While they offer reasonably accurate predictions, they also suffer a few shortcomings including lack of availability, parallel prediction mode, single-label prediction (inability to predict multiple channel subtypes), and incomplete scope (inability to predict subtypes of the voltage-gated channels). We developed a first-of-its-kind PSIONplusm method that performs sequential multi-label prediction of ion channels and their subtypes for both voltage-gated and ligand-gated channels. PSIONplusm sequentially combines the outputs produced by three support vector machine-based models from the PSIONplus predictor and is available as a webserver. Empirical tests show that PSIONplusm outperforms current methods for the multi-label prediction of the ion channel subtypes. This includes the existing single-label methods that are available to the users, a naïve multi-label predictor that combines results produced by multiple single-label methods, and methods that make predictions based on sequence alignment and domain annotations. We also found that the current methods (including PSIONplusm) fail to accurately predict a few of the least frequently occurring ion channel subtypes. Thus, new predictors should be developed when a larger quantity of annotated ion channels will be available to train predictive models.
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Yue, Yingbo, Fuchun Chen, and Guilin Chen. "Statistical and Comparative Analysis of Multi-Channel Infrared Anomalies before Earthquakes in China and the Surrounding Area." Applied Sciences 12, no. 16 (August 9, 2022): 7958. http://dx.doi.org/10.3390/app12167958.

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Abundant infrared remote sensing images and advanced information processing technologies are used to predict earthquakes. However, most studies only use single long-wave infrared data or its products, and the accuracy of prediction is not high enough. To solve this problem, this paper proposes a statistical method based on connected domain recognition to analyze multi-channel anomalies. We extract pre-seismic anomalies from multi-channel infrared remote sensing images using the relative power spectrum, then calculate positive predictive values, true positive rates and probability gains in different channels. The results show that the probability gain of the single-channel prediction method is extremely low. The positive predictive value of four-channel anomalies is 41.94%, which is higher than that of single-channel anomalies with the same distance threshold of 200 km. The probability gain of the multi-channel method is 2.38, while that of the single-channel method using the data of any channel is no more than 1.26. This study shows the advantages of the multi-channel method to predict earthquakes and indicates that it is feasible to use multi-channel infrared remote sensing images to improve the accuracy of earthquake prediction.
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Gao, Jianzhao, Zhen Miao, Zhaopeng Zhang, Hong Wei, and Lukasz Kurgan. "Prediction of Ion Channels and their Types from Protein Sequences: Comprehensive Review and Comparative Assessment." Current Drug Targets 20, no. 5 (March 5, 2019): 579–92. http://dx.doi.org/10.2174/1389450119666181022153942.

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Background: Ion channels are a large and growing protein family. Many of them are associated with diseases, and consequently, they are targets for over 700 drugs. Discovery of new ion channels is facilitated with computational methods that predict ion channels and their types from protein sequences. However, these methods were never comprehensively compared and evaluated. </P><P> Objective: We offer first-of-its-kind comprehensive survey of the sequence-based predictors of ion channels. We describe eight predictors that include five methods that predict ion channels, their types, and four classes of the voltage-gated channels. We also develop and use a new benchmark dataset to perform comparative empirical analysis of the three currently available predictors. </P><P> Results: While several methods that rely on different designs were published, only a few of them are currently available and offer a broad scope of predictions. Support and availability after publication should be required when new methods are considered for publication. Empirical analysis shows strong performance for the prediction of ion channels and modest performance for the prediction of ion channel types and voltage-gated channel classes. We identify a substantial weakness of current methods that cannot accurately predict ion channels that are categorized into multiple classes/types. </P><P> Conclusion: Several predictors of ion channels are available to the end users. They offer practical levels of predictive quality. Methods that rely on a larger and more diverse set of predictive inputs (such as PSIONplus) are more accurate. New tools that address multi-label prediction of ion channels should be developed.
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Klingaa, Christopher Gottlieb, Sankhya Mohanty, and Jesper Henri Hattel. "Realistic design of laser powder bed fusion channels." Rapid Prototyping Journal 26, no. 10 (October 15, 2020): 1827–36. http://dx.doi.org/10.1108/rpj-01-2020-0010.

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Purpose Conformal cooling channels in additively manufactured molds are superior over conventional channels in terms of cooling control, part warpage and lead time. The heat transfer ability of cooling channels is determined by their geometry and surface roughness. Laser powder bed fusion manufactured channels have an inherent process-induced dross formation that may significantly alter the actual shape of nominal channels. Therefore, it is crucial to be able to predict the expected surface roughness and changes in the geometry of metal additively manufactured conformal cooling channels. The purpose of this paper is to present a new methodology for predicting the realistic design of laser powder bed fusion channels. Design/methodology/approach This study proposes a methodology for making nominal channel design more realistic by the implementation of roughness prediction models. The models are used for altering the nominal shape of a channel to its predicted shape by point cloud analysis and manipulation. Findings A straight channel is investigated as a simple case study and validated against X-ray computed tomography measurements. The modified channel geometry is reconstructed and meshed, resulting in a predicted, more realistic version of the nominal geometry. The methodology is successfully tested on a torus shape and a simple conformal cooling channel design. Finally, the methodology is validated through a cooling test experiment and comparison with simulations. Practical implications Accurate prediction of channel surface roughness and geometry would lead toward more accurate modeling of cooling performance. Originality/value A robust start to finish method for realistic geometrical prediction of metal additive manufacturing cooling channels has yet to be proposed. The current study seeks to fill the gap.
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Ra, Jee S., Tianning Li, and Yan Li. "A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction." Sensors 21, no. 23 (November 29, 2021): 7972. http://dx.doi.org/10.3390/s21237972.

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The key research aspects of detecting and predicting epileptic seizures using electroencephalography (EEG) signals are feature extraction and classification. This paper aims to develop a highly effective and accurate algorithm for seizure prediction. Efficient channel selection could be one of the solutions as it can decrease the computational loading significantly. In this research, we present a patient-specific optimization method for EEG channel selection based on permutation entropy (PE) values, employing K nearest neighbors (KNNs) combined with a genetic algorithm (GA) for epileptic seizure prediction. The classifier is the well-known support vector machine (SVM), and the CHB-MIT Scalp EEG Database is used in this research. The classification results from 22 patients using the channels selected to the patient show a high prediction rate (average 92.42%) compared to the SVM testing results with all channels (71.13%). On average, the accuracy, sensitivity, and specificity with selected channels are improved by 10.58%, 23.57%, and 5.56%, respectively. In addition, four patient cases validate over 90% accuracy, sensitivity, and specificity rates with just a few selected channels. The corresponding standard deviations are also smaller than those used by all channels, demonstrating that tailored channels are a robust way to optimize the seizure prediction.
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M. Al-Sammna, Ahmed, Marwan Hadri Azmi, and Tharek Abd Rahman. "Time-Varying Ultra-Wideband Channel Modeling and Prediction." Symmetry 10, no. 11 (November 12, 2018): 631. http://dx.doi.org/10.3390/sym10110631.

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This paper considers the channel modeling and prediction for ultra-wideband (UWB) channels. The sparse property of UWB channels is exploited, and an efficient prediction framework is developed by introducing two simplified UWB channel impulse response (CIR) models, namely, the windowing-based on window delay (WB-WD) and the windowing-based on bin delay (WB-BD). By adopting our proposed UWB windowing-based CIR models, the recursive least square (RLS) algorithm is used to predict the channel coefficients. By using real CIR coefficients generated from measurement campaign data conducted in outdoor environments, the modeling and prediction performance results and the statistical properties of the root mean square (RMS) delay spread values are presented. Our proposed framework improves the prediction performances with lower computational complexity compared with the performance of the recommended ITU-R UWB-CIR model. It is shown that our proposed framework can achieved 15% lower prediction error with a complexity reduction by a factor of 12.
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Hagebölling, F., and U. Zölzer. "A multi channel coupling based approach for the prediction of the channel capacity of MIMO-systems." Advances in Radio Science 9 (July 29, 2011): 153–58. http://dx.doi.org/10.5194/ars-9-153-2011.

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Abstract. When installing Multiple Input Multiple Output (MIMO)-systems, the antenna positioning has a major influence upon the achievable transmission quality. To determine those antenna positions, which maximize the transmission quality, in adequate time, a computer based prediction of the channel capacity is imperative. In this paper, we will show that Ray Tracing, which is a very popular prediction method and well suited for the prediction of transfer functions or power delay profiles, produces unacceptable errors when predicting the channel capacity of MIMO-systems. Furthermore we identify the source of the prediction errors and present a new algorithm, based on an approach known as Multi Channel Coupling (MCC), which avoids this error source. Finally a comparison of the prediction results of our algorithm with prediction results gained with an Image Ray Tracer as well as with measured results is used to show the formidable increasement of prediction accuracy which can be gained by using our algorithm.
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Park, Sangwoo, and Osvaldo Simeone. "Speeding Up Training of Linear Predictors for Multi-Antenna Frequency-Selective Channels via Meta-Learning." Entropy 24, no. 10 (September 26, 2022): 1363. http://dx.doi.org/10.3390/e24101363.

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An efficient data-driven prediction strategy for multi-antenna frequency-selective channels must operate based on a small number of pilot symbols. This paper proposes novel channel-prediction algorithms that address this goal by integrating transfer and meta-learning with a reduced-rank parametrization of the channel. The proposed methods optimize linear predictors by utilizing data from previous frames, which are generally characterized by distinct propagation characteristics, in order to enable fast training on the time slots of the current frame. The proposed predictors rely on a novel long short-term decomposition (LSTD) of the linear prediction model that leverages the disaggregation of the channel into long-term space-time signatures and fading amplitudes. We first develop predictors for single-antenna frequency-flat channels based on transfer/meta-learned quadratic regularization. Then, we introduce transfer and meta-learning algorithms for LSTD-based prediction models that build on equilibrium propagation (EP) and alternating least squares (ALS). Numerical results under the 3GPP 5G standard channel model demonstrate the impact of transfer and meta-learning on reducing the number of pilots for channel prediction, as well as the merits of the proposed LSTD parametrization.
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Zhao, Zijian, and Pengyuan Zou. "Attention-based Dual-channel Deep Neural Network For Aero-engine RUL Prediction Under Time-varying Operating Conditions." Journal of Physics: Conference Series 2386, no. 1 (December 1, 2022): 012027. http://dx.doi.org/10.1088/1742-6596/2386/1/012027.

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Abstract For complex systems such as aerospace, remaining useful life (RUL) prediction is a general technique that provides information for decision-making in predictive maintenance. In the industrial field, RUL prediction under time-varying operating conditions is a challenging task. In this paper, an attention-based dual-channel deep neural network is proposed to fuse the time-varying operating conditions, with both prediction channels using long short-term memory (LSTM) neural networks. First, the features are extracted by a one-dimensional convolutional neural network (CNN). The operating conditions and sensor data are put into the dual-channel LSTM neural networks separately for prediction. The obtained results are combined with the attention mechanism to assign weights and finally put into the fully connected network for linear mapping to get the final RUL prediction results. This study is based on the N-CMAPSS dataset published by NASA. Compared with traditional methods, this method demonstrates its superiority and effectiveness.
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Song, Yamin, Xia Lei, Zhaofu Kong, and Binhong Dong. "Antenna calibration using channel prediction for time-varying channels." Journal of Modern Transportation 20, no. 4 (December 2012): 213–19. http://dx.doi.org/10.1007/bf03325801.

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Dissertations / Theses on the topic "Channel prediction"

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Anderson, Alan John. "Channel prediction in wireless communications." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/16188.

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Knowledge of the channel over which signals are sent is of prime importance in modern wireless communications. Inaccurate or incomplete channel information leads to high error rates and wasted bandwidth and energy. Although active channel measurement is commonly used to gain channel knowledge, it can only accurately represent the channel at the time the measurement was taken, makes energy and bandwidth demands, and adds significant complexity to the radio system. Due to the highly time variant nature of wireless channels, active measurements become invalid almost as soon as they are taken, making alternative approaches to predicting future behaviour highly attractive. Such systems would allow maximum advantage to be taken of the limited bandwidth available and make significant power savings. This thesis investigates a number of complementary technologies, leading towards a channel prediction scheme suitable for mobile devices. As a first step towards channel prediction, anomaly detection is investigated within periodic wireless signals to establish when radical changes in the channel occur. In pre- vious experiments, long monotonic sequences had been observed to coincide with certain anomalies but not others when using Kullback-Leibler Divergence (KLD) analysis, possibly allowing the characterisation of anomaly types. An investigation is described to explain the origin of these features in a rigorous mathematical sense. A proof is given for the causes of the monotonic sequences, followed by a discussion of the types of signal anomaly which would underly such a feature and the value of this information. The second part describes a novel channel characterisation method which uses a class of Recurrent Neural Network (RNN) called an Echo State Network (ESN). Using this tool, a channel characterisation system can be constructed without an explicit statistical or mathematical model of the wireless environment, relying instead on observed data. This approach is much more convenient than existing models which require detailed information about the wireless system's parameters and also allows for new channel classifications to be added easily. It is able to achieve double the correct classification rate of a conventional statistical classifier, and is computationally simple to implement, making it ideal for inclusion on low-power mobile devices. Following their successful use in characterisation, ESNs are used in the final part in an investigation into channel prediction in a number of different scenarios. They were however found to be unable to produce useful predictions for all but the most trivial channel models. An alternative method is described for indoor environments using an approach inspired by ray tracing. It is simple and computationally lightweight to implement, again making it suitable for mobile devices. Simulation results show that it can outperform pilot-assisted methods by a significant margin, while not wasting bandwidth on channel measurement.
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Wiklund, Ingrid. "Channel Prediction for Moving Relays." Thesis, Uppsala universitet, Signaler och System, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-195259.

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In mobile communications, channel side information at transmitters can increasecapacity. For moving relays nodes, local nodes placed on buses and trams in urbanareas, the channel state information is outdated for control delays of severalmilliseconds, as in the LTE system. Prediction of the channel based on statistic is notadequate for vehicular velocities. In this thesis, prediction made with an additionalantenna, a ``predictor antenna", placed in front of the main antenna is evaluated. Thepredictor utilises that the channel of the predictor antenna is highly correlated to thechannel experienced by the main antenna somewhat later, when the main antenna hasmoved to the position previous occupied by the predictor antenna. A normalisedcorrelation of up to 0.98 could be measured between the channels of the antennasfor an antenna separation of several wavelengths, but it was found that the closeenvironment and the antenna pattern have a big impact on the correlation. Thepredictions made with the antenna are also combined with predictions based onstatistics of past measurements from the main antenna to see if a better result can beachieved. For a prediction range of 0.5 carrier wavelengths, a prediction as good as anormalised mean square error (NMSE) of -13.9 dB could be seen. This is sufficient togive a gain in the performance when using link adaptation and opportunistic multi-userscheduling, based on channel state information at transmitter. The evaluations isbased on measurements on a 20 MHz downlink channel at 2.68 GHz.
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Choi, Jihwan Patrick 1975. "Channel prediction and adaptation over satellite channels with weather-induced impairments." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/9070.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (leaves 85-87).
Title as it appears in MIT commencement exercises program, June 2000: Satellite channels with weather-induced impairments.
Bad weather conditions, especially due to rain, cause satellites operating at high frequencies (above 10 GHz) to have significant link attenuation. Usually extra link margins are used to assure link availability. These margins cause inefficient use of precious satellite and terminal power, and unnecessarily limit data throughputs. Efficiency improvements using channel prediction and adaptation over satellite channels with weather-induced impairments are considered in this thesis. First, we consider scintillation and rain attenuation as two dominant factors for signal fading over satellite-earth paths above 10 GHz, and explore physical and mathematical modeling of the two processes. Statistical and spectral analyses of these processes using one or two pole autoregressive (AR) models yield simple linear estimators for the received signal attenuation. Using these estimators, we present results where we can predict the received signal attenuation within ±0.5 dB 1 second ahead and within ± 1.0 dB 4 seconds ahead. For adaptation, we change the signal transmission power, the modulation symbol size, and the code rate adaptively. In particular, we suggest a continuous power control and discrete rate control strategy, through which we build a set of modulation/code states, and discretely change the modulation symbol size and the code rate from state to state. Within each state, continuous power control is implemented. Several examples that use this technique and quantitative analyses of power increase and capacity are provided. The analyses indicate that there is a substantial gain in performance either in capacity and/or power consumption with the adaptive schemes.
by Jihwan Patrick Choi.
S.M.
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Björsell, Joachim. "Long Range Channel Predictions for Broadband Systems : Predictor antenna experiments and interpolation of Kalman predictions." Thesis, Uppsala universitet, Signaler och System, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-281058.

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The field of wireless communication is under massive development and the demands on the cellular system, especially, are constantly increasing as the utilizing devices are increasing in number and diversity. A key component of wireless communication is the knowledge of the channel, i.e, how the signal is affected when sent over the wireless medium. Channel prediction is one concept which can improve current techniques or enable new ones in order to increase the performance of the cellular system. Firstly, this report will investigate the concept of a predictor antenna on new, extensive measurements which represent many different environments and scenarios. A predictor antenna is a separate antenna that is placed in front of the main antenna on the roof of a vehicle. The predictor antenna could enable good channel prediction for high velocity vehicles. The measurements show to be too noisy to be used directly in the predictor antenna concept but show potential if the measurements can be noise-filtered without distorting the signal. The use of low-pass filter and Kalman filter to do this, did not give the desired results but the technique to do this should be further investigated. Secondly, a interpolation technique will be presented which utilizes predictions with different prediction horizon by estimating intermediate channel components using interpolation. This could save channel feedback resources as well as give a better robustness to bad channel predictions by letting fresh, local, channel predictions be used as quality reference of the interpolated channel estimates. For a linear interpolation between 8-step and 18-step Kalman predictions with Normalized Mean Square Error (NMSE) of -15.02 dB and -10.88 dB, the interpolated estimates had an average NMSE of -13.14 dB, while lowering the required feedback data by about 80 %. The use of a warning algorithm reduced the NMSE by a further 0.2 dB. It mainly eliminated the largest prediction error which otherwise could lead to retransmission, which is not desired.
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Flåm, John Torjus. "Adaptive Frequency Hopping with Channel Prediction." Thesis, Norwegian University of Science and Technology, Department of Electronics and Telecommunications, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9307.

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The number of radio systems operating in the 2.4 GHz band is rising as a result of increased usage of wireless technologies. Since such devices interfere with one another, satisfactory co-existence becomes important. Several techniques serve to reduce the interference. Included among these are frequency hopping (FH) and power-control. This report focuses only on FH, and particularly on methods that make FH schemes adaptive. An FH scheme is adaptive if it responds to the noise and fading by avoiding channels that are unfit for transmission. An example of such a scheme is already implemented in an audio transceiver, the nRF24Z1, manufactured by Nordic Semiconductor. That transceiver provides the framework for this study, and the main objective is to suggest improvements to its FH algorithm. Better performance is particularly interesting in high quality audio streaming because such transmissions generally have strict real time requirements. Thus, the time to retransmit corrupted data is limited, and measures to reduce the impact of interference and fading are desired. The FH scheme implemented in the nRF24Z1 works broadly as follows: If a channel distorts more than a certain share of the transmitted data, it is extracted from the FH routine and listed as banned for usage. The ban list has room for maximum 18 out of 38 channels and can therefore filter out significant parts of the spectrum. If the system identifies more poor channels than the list can hold, the oldest channel in the ban list is released, and the newly identified one takes its place. In a scenario where noise and deep fades come to occupy a rather stable group of channels, the banned channels will match the unsuited parts of the spectrum quite accurately, and the scheme performs well. However, when the noise and fading is changing, maybe quickly and non-periodically, the performance drops significantly. The reason is that channels are banned only after they have caused trouble, which has two negative effects. Firstly, it is likely that the bulk of the transmitted data was distorted, and the need for retransmission can therefore be large. Secondly, since the transmission conditions are changing, the ban list becomes outdated and reflects the actual interference and fading poorly. Therefore, in this report, the possibility of predicting poor channels in order to avoid them beforehand, is investigated. For the purpose of prediction, small test packets are transmitted. In short, the principle of operation is that if a test packet is readable at the receiver, the channel is used. Otherwise it is avoided. Computer simulations indicate that this technique improves transmission conditions and reduces the need for retransmission when the noise and fading change significantly. Large changes are indeed common in practice. They occur, for example, if a broadband interferer is switched off or greatly varies its output power. Plainly, they could also come about when objects move. Despite promising simulations, channel testing does not come without side effects. An audio streaming system like the nRF24Z1 must secure a certain flow of data to avoid audible errors. If prediction algorithms are to secure that flow, a compromise must be made: the more time a system spends on channel testing, the less time remains for transmission of data. Therefore, at some point, testing must be terminated to leave room for the real job. In consequence, the key issue of finding the best trade-off between testing and transmission must be addressed. This report presents three adaptive FH schemes that approach that issue in their own manner. The performance of the proposed prediction schemes has been investigated using a channel model for the ISM band (Industrial, Medical, and Scientific). It is coded and developed in MATLAB. The model mimics the effects of a real mobile channel quite well, and this inspires non-negligible confidence in the simulation results.

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Olesen, Rikke Abildgaard. "Channel Prediction for Coordinated Multipoint Transmission." Thesis, Uppsala universitet, Signaler och System, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-142955.

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One of the currently explored strategies for interference avoidance and improving Signal to Noise Ratio (SNR) for mobile communication systems is Coordinated MultiPoint (CoMP) transmission. The general idea of the strategy is to let two or more base stations serve the same user. Due to delay factors, the channels from each serving base station needs to be predicted to obtain an adaptive CoMP system. In this thesis, a user interface is created to act as an experimental platform for a set of measured downlink channel data. The user interface supports editing of the channel data, model estimation, Kalman filtering and prediction and evaluation of the channel statistics. The user interface and the measured channel downlink data is then used to examine how well we can predict the weakest channel in a CoMP setup with three base stations. The predictions are carried out using an m-step Kalman predictor which uses an AR4 model, estimated from previous channel data. For the investigation, the user moves at pedestrian speed and the signals from the three different base stations use orthogonal Common Reference Signals (CRS). A comparison of different CRS patterns is also included in the investigation. It is concluded that 5 ms predictions of the weakest channel achieves a normalized mean squared error (NMSE) of -8 dB or lower provided that the weakest signal has an SNR of at least 5 dB and is no more than 15 dB lower than the combined received signal. Additionally, it is found that predictions are more accurate for CRS patterns spread over time than over subcarriers.
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Potter, Chris, Kurt Kosbar, and Adam Panagos. "MIMO Channel Prediction Using Recurrent Neural Networks." International Foundation for Telemetering, 2008. http://hdl.handle.net/10150/606193.

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ITC/USA 2008 Conference Proceedings / The Forty-Fourth Annual International Telemetering Conference and Technical Exhibition / October 27-30, 2008 / Town and Country Resort & Convention Center, San Diego, California
Adaptive modulation is a communication technique capable of maximizing throughput while guaranteeing a fixed symbol error rate (SER). However, this technique requires instantaneous channel state information at the transmitter. This can be obtained by predicting channel states at the receiver and feeding them back to the transmitter. Existing algorithms used to predict single-input single-output (SISO) channels with recurrent neural networks (RNN) are extended to multiple-input multiple-output (MIMO) channels for use with adaptive modulation and their performance is demonstrated in several examples.
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Schaubach, Kurt Richard. "Microcellular radio channel prediction using ray tracing." Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-09122009-040308/.

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Rudd, Richard. "Statistical prediction of indoor radio channel impulse response." Thesis, University of Surrey, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.486097.

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The characterisation of the indoor radio channel has been an active area of research in the last decades, and this is likely to continue with the increasing importance of wireless techniques for the provision of connectivity within homes and offices. This thesis gives a survey of existing models for the indoor radio channel, noting that sitespecific predictions are often of limited use owing to the difficulty of determining accurate details ofthe indoor environment. While ray tracing models, for instance, are potentially capable of offering highly accur~te prediction, this promise is seldom fulfilled in practice owing to the difficulty of capturing the necessary input data for such models. As a consequence, statistical treatments ofthe indoor channel are popular, and two such models for the temporal response ofthe wideband channel are discussed in some detail. These models, however, rely on the use of empirical parameters that will either represent only the generality of buildings, or will need to be determined by measurement for each specific case. An alternative model was therefore sought which combined physical and statistical elements to introduce a greater degree of site-specificity. The initial modelling reported in this thesis took, as its basis, the methods developed within the acoustic community for the prediction of impulse response within reverberant rooms. The thesis then proposes a new model for the prediction ofthe average power delay profile (PDP) within a room, taking the extreme room dimensions as parameters to allow the accommodation of site-specific detail. A measurement campaign, using a channel sounder developed for the purpose, is described, and the results compared with the predictions offered by the new model. Finally, the thesis suggests ways in which the new model might be used as a component of other, more general, models for the indoor radio channel.
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Rameshwaran, Ponnambalam. "Conveyance prediction for meandering two-stage channel flows." Thesis, University of Aberdeen, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363682.

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An examination was carried out to determine the conveyance behaviour of overbank flow in meandering two-stage channel systems. Eleven influential parameters were identified namely sinuosity, aspect ratio, cross-sectional shape and bank slope of the main channel, relative boundary roughness of floodplain to main channel, floodplain longitudinal slope, meander belt width relative to floodplain width, sinuosity of the floodplain bank, relative overbank flow depth, system scale and lateral slope of the floodplain. The first ten of the eleven parameters were examined. Observations were made of the coherent flow structure of the floodplain and main channel systems, which influences energy losses. The effect of each of the influential parameters is quantified through a non-dimensional discharge function F* which is the ratio of the actual discharge in a two-stage channel to a theoretical discharge aggregated for three cross-section zones, the main channel, the floodplain within the meandering channel and the floodplain outside the meander belt. The effect of each parameter on the energy losses in meandering two-stage channel flows for the case involving straight floodbanks and a floodplain without cross-fall are analysed in terms of Darcy-Weisbach resistance coefficients using the Prandt-von Karman resistance relationship and treating the system as a whole. Flow domains are defined in the first of which viscosity and roughness are influential and in the second flow resistance is independent of viscosity. A design approach is presented for predicting the conveyance capacity in each of these domains and is based on small-scale data obtained at the Universities of Aberdeen and Glasgow and large-scale data from the UK Flood Channel Facility (UK-FCF). This approach was applied to the available independent laboratory data along with the James and Wark (1992) method. The author's approach was found to give good predictions of conveyance capacity. This approach was also applied to River Roding field data. The floodplain roughness which varies throughout the year and is difficult to estimate, is shown to be the most significant source of energy loss and is environmentally sensitive in natural meandering two-stage rivers.
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Books on the topic "Channel prediction"

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Montgomery, David R. Channel classification, prediction of channel response, and assessment of channel condition. [Seattle: University of Washington?, 1993.

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J, Millard Brian. Channel analysis: The key to share price prediction. 2nd ed. Chichester, England: J. Wiley, 1997.

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Channel analysis: The key to share price prediction. Bramhall: Qudos, 1990.

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Mann, Michael E. Dire predictions: Understanding global warming. New York, N.Y: DK Pub., 2009.

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Mann, Michael E. Dire predictions: Understanding global warming. London: DK, 2008.

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1944-, Shukla J., and North Atlantic Treaty Organization. Scientific Affairs Division., eds. Prediction of interannual climate variations. Berlin: Springer-Verlag, 1993.

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Łapuszek, Marta. Intensywność i prognoza zmian położenia den koryt rzecznych w lewobrzeżnych dopływach górnej Wisły: The establish and prediction of the river channel evolution of the left-side tributaries of the Upper Vistula River = Intensité et la prévision de l'évolution du lit de la riviére des tributaires du côté gauche de la Haute Vistule. Kraków: Politechnika Krakowska im. Tadeusza Kościuszki, 2013.

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M, Wallace John. El Niño and climate prediction. [Boulder, Colo.]: [University Corporation for Atmospheric Research, Office for Interdisciplinary Earth Studies], 1994.

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Wallace, John M. El Niño and climate prediction. [Washington, D.C.?]: National Oceanic and Atmosphere Administration, 1999.

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R, Kump Lee, and Intergovernmental Panel on Climate Change., eds. Dire predictions: Understanding global warming. London: DK, 2008.

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Book chapters on the topic "Channel prediction"

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Potter, Chris. "RNN Based MIMO Channel Prediction." In Differential Evolution in Electromagnetics, 177–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12869-1_8.

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Carlemalm, Catharina, and Fredrik Gustafsson. "Detection and Discrimination of Double Talk and Echo Path Changes in a Telephone Channel." In Signal Analysis and Prediction, 407–18. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1768-8_28.

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Sahu, Piyush Pritam, Kanhu Charan Patra, and Abinash Mohanta. "Discharge Prediction Approaches in Meandering Compound Channel." In Water Science and Technology Library, 225–47. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59148-9_16.

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Kejalakshmi, V., and S. Arivazhagan. "Transmit Precoding for Time Varying Channel by Considering Channel Prediction Error." In Trends in Network and Communications, 149–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22543-7_15.

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Moradi, H., S. Abbas Moradi, and L. Kashani. "Students’ Performance Prediction Using Multi-Channel Decision Fusion." In Educational Data Mining, 151–74. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02738-8_6.

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Henry, F. S., M. W. Collins, and M. Ciofalo. "Prediction of Swirling Flow in a Corrugated Channel." In Applications of Supercomputers in Engineering II, 307–17. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3660-0_22.

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Bilgehan, Bülent. "Fuzzy Based Wireless Channel Path Loss Prediction Model." In Advances in Intelligent Systems and Computing, 515–22. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64058-3_64.

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Paviglianiti, Annunziata, Vincenzo Randazzo, Giansalvo Cirrincione, and Eros Pasero. "Double Channel Neural Non Invasive Blood Pressure Prediction." In Intelligent Computing Theories and Application, 160–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60799-9_14.

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Schiesaro, Andrea, and Gerhard F. Ecker. "Prediction of hERG Channel Inhibition Using In Silico Techniques." In Ion Channels and Their Inhibitors, 191–239. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19922-6_7.

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Yeager, T., T. D. Ard, F. W. Carver, T. Holroyd, and R. Coppola. "MEG Virtual Channel Methods for Movement Prediction and Training." In IFMBE Proceedings, 171–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12197-5_37.

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Conference papers on the topic "Channel prediction"

1

Tenenbaum, Adam J., Raviraj S. Adve, and Young-Soo Yuk. "Channel prediction and feedback in multiuser broadcast channels." In 2009 11th Canadian Workshop on Information Theory (CWIT). IEEE, 2009. http://dx.doi.org/10.1109/cwit.2009.5069523.

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Xin Fang and Xiaohu Yu. "Optimum channel quality prediction algorithm over fading channels." In IET International Conference on Wireless Mobile and Multimedia Networks Proceedings (ICWMMN 2006). IEE, 2006. http://dx.doi.org/10.1049/cp:20061343.

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Condrat, Christopher, Priyank Kalla, and Steve Blair. "Channel routing for integrated optics." In 2013 ACM/IEEE International Workshop on System Level Interconnect Prediction (SLIP). IEEE, 2013. http://dx.doi.org/10.1109/slip.2013.6681678.

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Jian Zhang, D. Smith, and Zhuo Chen. "Linear finite state Markov chain predictor for channel prediction." In 2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC 2012). IEEE, 2012. http://dx.doi.org/10.1109/pimrc.2012.6362698.

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Wang, Jinsu, Sharad Mehrotra, and Nalini Venkatasubramanian. "PBCA - Prediction Based Channel Allocation." In IEEE GLOBECOM 2007-2007 IEEE Global Telecommunications Conference. IEEE, 2007. http://dx.doi.org/10.1109/glocom.2007.911.

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Konstantinov, A. S., and A. V. Pestryakov. "Fading Channel Prediction for 5G." In 2019 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO). IEEE, 2019. http://dx.doi.org/10.1109/synchroinfo.2019.8813950.

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Dong, Liang. "Turbo Equalization with Channel Prediction and Iterative Channel Estimation." In 2009 IEEE Wireless Communications and Networking Conference. IEEE, 2009. http://dx.doi.org/10.1109/wcnc.2009.4918006.

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Chen, Yifan, Zheng Dou, Yun Lin, and Ying Li. "Prediction of V2V channel quality under double-Rayleigh fading channels." In 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). IEEE, 2020. http://dx.doi.org/10.1109/vtc2020-spring48590.2020.9129411.

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Yang, Du, W. Liu, Lie-Liang Yang, and L. Hanzo. "Channel Prediction and Predictive Vector Quantization Aided Channel Impulse Response Feedback for SDMA Downlink Preprocessing." In 2008 IEEE 68th Vehicular Technology Conference (VTC 2008-Fall). IEEE, 2008. http://dx.doi.org/10.1109/vetecf.2008.73.

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Kenarangi, Farid, and Inna Partin-Vaisband. "Security Network On-Chip for Mitigating Side-Channel Attacks." In 2019 ACM/IEEE International Workshop on System Level Interconnect Prediction (SLIP). IEEE, 2019. http://dx.doi.org/10.1109/slip.2019.8771328.

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Reports on the topic "Channel prediction"

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Liang, George. Site Specific Propagation Prediction Software Tool For Communication Channel Modeling. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/ada357796.

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Yesha, Yaacov. Channel coding for code excited linear prediction (CELP) encoded speech in mobile radio applications. Gaithersburg, MD: National Institute of Standards and Technology, 1994. http://dx.doi.org/10.6028/nist.ir.5503.

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Parchure, Trimbak M., Soraya Sarruff, and Ben Brown. Desktop Study for La Quinta Project; Shoaling Prediction in La Quinta Navigation Channel and Effect of a Barrier on Siltation in Extended La Quinta Channel. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada407991.

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Blain, Cheryl A., and W. E. Rogers. Coastal Tide Prediction Using the ADCIRC-2DDI Hydrodynamic Finite Element Model: Model Validation and Sensitivity Analyses in the Southern North Sea/English Channel. Fort Belvoir, VA: Defense Technical Information Center, December 1998. http://dx.doi.org/10.21236/ada358752.

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Frydman, Roman, Søren Johansen, Anders Rahbek, and Morten Nyboe Tabor. Asset Prices Under Knightian Uncertainty. Institute for New Economic Thinking Working Paper Series, December 2021. http://dx.doi.org/10.36687/inetwp172.

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We extend Lucas’s classic asset-price model by opening the stochastic process driving dividends to Knightian uncertainty arising from unforeseeable change. Implementing Muth’s hypothesis, we represent participants’ expectations as being consistent with our model’s predictions and formalize their ambiguity-averse decisions with maximization of intertemporal multiple-priors utility. We characterize the asset-price function with a stochastic Euler equation and derive a novel prediction that the relationship between prices and dividends undergoes unforeseeable change. Our approach accords participants’ expectations, driven by both fundamental and psychological factors, an autonomous role in driving the asset price over time, without presuming that participants are irrational.
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Morales, Paola, Daniel Osorio-Rodíguez, Juan S. Lemus-Esquivel, and Miguel Sarmiento. The internationalization of domestic banks and the credit channel of monetary policy. Banco de la República, November 2021. http://dx.doi.org/10.32468/be.1181.

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How does the expansion of domestic banks in international markets affect the bank lending channel of monetary policy? Using bank-firm loan-level data, we find that loan growth and loan rates from international banks respond less to monetary policy changes than domestic banks and that internationalization partially mitigates the risk-taking channel of monetary policy. Banks with a large international presence tend to tolerate more their credit risk exposition relative to domestic banks. Moreover, international banks tend to rely more on foreign funding when policy rates change, allowing them to insulate better the monetary policy changes from their credit supply than domestic banks. This result is consistent with the predictions of the internal capital markets hypothesis. We also show that macroprudential FX regulation reduces banks with high FX exposition access to foreign funding, ultimately contributing to monetary policy transmission. Overall, our results suggest that the internationalization of banks lowers the potency of the bank lending channel. Furthermore, it diminishes the risk-taking channel of monetary policy within the limit established by macroprudential FX regulations.
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Dunkin, Lauren, Lauren Coe, and Jay Ratcliff. Corps Shoaling Analysis Tool : predicting channel shoaling. Engineer Research and Development Center (U.S.), November 2018. http://dx.doi.org/10.21079/11681/30382.

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Kinikles, Dellena, and John McCartney. Hyperbolic Hydro-mechanical Model for Seismic Compression Prediction of Unsaturated Soils in the Funicular Regime. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, December 2022. http://dx.doi.org/10.55461/yunw7668.

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A semi-empirical elasto-plastic constitutive model with a hyperbolic stress-strain curve was developed with the goal of predicting the seismic compression of unsaturated sands in the funicular regime of the soil-water retention curve (SWRC) during undrained cyclic shearing. Using a flow rule derived from energy considerations, the evolution in plastic volumetric strain (seismic compression) was predicted from the plastic shear strains of the hysteretic hyperbolic stress-strain curve. The plastic volumetric strains are used to predict the changes in degree of saturation from phase relationships and changes in pore air pressure from Boyle’s and Henry’s laws. The degree of saturation was used to estimate changes in matric suction from the transient scanning paths of the SWRC. Changes in small-strain shear modulus estimated from changes in mean effective stress computed from the constant total stress and changes in pore air pressure, degree of saturation and matric suction, in turn affect the hyperbolic stress-strain curve’s shape and the evolution in plastic volumetric strain. The model was calibrated using experimental shear stress-strain backbone curves from drained cyclic simple shear tests and transient SWRC scanning path measurements from undrained cyclic simple shear tests. Then the model predictions were validated using experimental data from undrained cyclic simple shear tests on unsaturated sand specimens with different initial degrees of saturation in the funicular regime. While the model captured the coupled evolution in hydro-mechanical variables (pore air pressure, pore water pressure, matric suction, degree of saturation, volumetric strain, effective stress, shear modulus) well over the first 15 cycles of shearing, the predictions were less accurate after continued cyclic shearing up to 200 cycles. After large numbers of cycles of undrained shearing, a linear decreasing trend between seismic compression and initial degree of saturation was predicted from the model while a nonlinear increasing-decreasing trend was observed in the cyclic simple shear experiments. This discrepancy may be due to not considering post shearing reconsolidation in the model, calibration of model parameters, or experimental issues including a drift in the position of the hysteretic shear-stress strain curve. Nonetheless, the trend from the model is consistent with predictions from previously- developed empirical models in the funicular regime of the SWRC. The developments of the new mechanistic model developed in this study will play a key role in the future development of a holistic model for predicting the seismic compression across all regimes of the SWRC.
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Harris, Kathleen, and Travis Dahl. Technical assessment of the Old, Mississippi, Atchafalaya, and Red (OMAR) Rivers : HEC-RAS BSTEM analysis of the Atchafalaya River. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/45174.

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This report documents the bank erosion modeling performed under Task 6 (HEC-RAS Sediment Modeling) of the Old, Mississippi, Atchafalaya, and Red (OMAR) Rivers System Technical Assessment. The objectives of the bank erosion modeling effort were to compare the relative impact various flow scenarios might have on bank retreat on a stretch of the Atchafalaya River between Simmesport, LA, and the Whiskey Bay Pilot Channel. The effort included compilation of field and soil boring data, selection of bank retreat sites, creation of representative soil profiles for the reach, calibration of soil parameters to measured retreat rates, and modeling bank retreat and volume of material eroded under various flow scenarios. This modeling effort was intended for scenario comparison and should not be used as a prediction of exact rates of bank erosion. The study found that varying the amount of flow entering the Atchafalaya River from the Mississippi River could increase dramatically or significantly reduce the extent of bank erosion, relative to the current management scenario.
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Alviarez, Vanessa, Michele Fioretti, Ken Kikkawa, and Monica Morlacco. Two-Sided Market Power in Firm-to-Firm Trade. Inter-American Development Bank, August 2021. http://dx.doi.org/10.18235/0003493.

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Firms in global value chains (GVCs) are granular and exert bargaining power over the terms of trade. We show that these features are crucial to understanding the well-established variation in prices and pass-through across importers and exporters. We develop a novel theory of prices in GVCs, which tractably nests a wide range of bilateral concentration and bargaining power configurations. We test and evaluate the models predictions using a novel dataset merging transaction-level U.S. import data with balance sheet data for both U.S. importers and foreign exporters. Our pricing framework enhances traditional frameworks in the literature in accurately predicting price changes following a tariff shock. The results shed light on the role of firms in determining the tariff pass-through onto import prices.
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