Robust transceiver designs for MIMO relay communication systems

The thesis investigates robust linear and non-linear transceiver design problems for wireless MIMO relay communication systems with the assumption that the partial information of the channel is available at the relay node. The joint source and relay optimization problems for MIMO relay systems are highly nonconvex, in general. We transform the problems into suitable forms which can be efficiently solved using standard convex optimization techniques. The proposed design schemes outperform the existing techniques

Performance analysis of signal-to-noise ratio estimators in AWGN and fading channels

Additive White Gaussian Noise (AWGN) and Rayleigh fading severely degrade the performance of the wireless communication systems. Most of the wireless communication systems require knowledge of the channel Signal-to-Noise ratio. In this paper a few methods are proposed to estimate the SNR in the presence of AWGN and Rayleigh fading. The mean square error (MSE) and root mean square error (RMSE) are used as performance measures. Simulation result shows that the newly proposed estimators mlfad can provide better performance in most circumstances under AWGN and Rayleigh fading channels

Simplified Robust Design for Nonregenerative Multicasting MIMO Relay Systems

In this paper, we propose a robust transceiver design for nonregenerative multicasting multiple-input multiple-output (MIMO) relay systems where a transmitter broadcasts common message to multiple receivers with aid of a relay node and the transmitter, relay and receivers are all equipped with multiple antennas. In the proposed design, the actual channel state information (CSI) is assumed as a Gaussian random matrix with the estimated CSI as the mean value, and the channel estimation errors are derived from the well-known Kronecker model. In the proposed design scheme, the transmitter and relay precoding matrices are jointly optimized to minimize the maximal mean squared-error (MSE) of the estimated signal at all receivers. The optimization problem is highly nonconvex in nature. Hence, we propose a low complexity solution by exploiting the optimal structure of the relay precoding matrix. Numerical simulations demonstrate the improved robustness of the proposed transceiver design algorithm against the CSI mismatch

Channel Covariance Information Based Transceiver Design for AF MIMO Relay Systems with Direct Link

In this paper, we propose a design scheme for amplify-and-forward multiple-input multiple-output (AF MIMO) relay system with direct link to minimize the mean-squared error (MSE) of the signal estimation at the destination. In the proposed design scheme, an optimal precoding matrix is derived with the assumption that the full channel state information (CSI) of the source-relay link and partial channel state information such as channel covariance information (CCI) of the relay-destination link are available at the relay. In practical cases, if the destination is closer to the source, the source-destination link cannot be ignored. Hence, in this paper, we assume that the relay knows the partial channel state information of the source-destination link. Based on this assumption, an iterative optimal covariance algorithm is developed to achieve the minimum MSE of the signal estimation at the destination. In order to reduce computational complexity of the proposed optimal covariance algorithm, a suboptimal covariance algorithm is proposed. A numerical example shows that the developed optimal covariance algorithm outperforms the conventional CCI based MSE algorithms

MMSE based transceiver design for MIMO relay systems with mean and covariance feedback

In this paper, the problem of transceiver design in a non-regenerative MIMO relay system is addressed, where linear signal processing is applied at the source, relay and destination to minimize the mean-squared error (MSE) of the signal waveform estimation at the destination. In the proposed design scheme, optimal structure of the source and relay precoding matrices are obtained with the assumption that the relay knows the mean and channel covariance information (CCI) of the relay-destination link and the full channel state information (CSI) of the source relay link. Based on this assumption, an iterative joint source and relay precoder design is proposed to achieve the minimum MSE of the signal estimation at the destination. In order to reduce computational complexity of the proposed iterative design, a suboptimal relay-only precoder design is proposed. A numerical example shows that the performance of the proposed iterative joint source and relay precoder design is very close to that of the algorithm using full CSI

Tomlinson-Harashima Precoding Based Transceiver Design for MIMO Relay Systems With Channel Covariance Information

In this paper, we investigate the performance of the Tomlinson-Harashima (TH) precoder based nonlinear transceiver design for a nonregenerative multiple-input multiple-output (MIMO) relay system assuming that the full channel state information (CSI) of the source-relay link is known, while only the channel covariance information (CCI) of the relay-destination link is available at the relay node. We first derive the structure of the optimal TH precoding matrix and the source precoding matrix that minimize the mean-squared error (MSE) of the signal waveform estimation at the destination. Then we develop an iterative algorithm to optimize the relay precoding matrix. To reduce the computational complexity of the iterative algorithm, we propose a simplified precoding matrices design scheme. Numerical results show that the proposed precoding matrices design schemes have a better bit-error-rate performance than existing algorithms

Coal-Fired Boiler Fault Prediction using Artificial Neural Networks

Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy

Euclidean Space Data Projection Classifier with Cartesian Genetic Programming (CGP)

Most evolutionary based classifiers are built based on generated rules sets that categorize the data into respective classes. This research work is a preliminary work which proposes an evolutionary-based classifier using a simplified Cartesian Genetic Programming (CGP) evolutionary algorithm. Instead on using evolutionary generated rule sets, the CGP generates i) a reference coordinate ii) projection functions to project data into a new 3 Dimensional Euclidean space. Subsequently, a distance boundary function of the new projected data to the reference coordinates is applied to classify the data into their respective classes. The evolutionary algorithm is based on a simplified CGP Algorithm using a 1+4 evolutionary strategy. The data projection functions were evolved using CGP for 1000 generations before stopping to extract the best functions. The Classifier was tested using three PROBEN 1 benchmarking datasets which are the PIMA Indians diabetes dataset, Heart Disease dataset and Wisconsin Breast Cancer (WBC) Dataset based on 10 fold cross validation dataset partitioning. Testing results showed that data projection function generated competitive results classification rates: Cancer dataset (97.71%), PIMA Indians dataset (77.92%) and heart disease (85.86%)

Euclidean space data projection classifier with cartesian genetic programming (CGP)

Most evolutionary based classifiers are built based on generated rules sets that categorize the data into respective classes. This research work is a preliminary work which proposes an evolutionary-based classifier using a simplified Cartesian Genetic Programming (CGP) evolutionary algorithm. Instead on using evolutionary generated rule sets, the CGP generates i) a reference coordinate ii) projection functions to project data into a new 3 Dimensional Euclidean space. Subsequently, a distance boundary function of the new projected data to the reference coordinates is applied to classify the data into their respective classes. The evolutionary algorithm is based on a simplified CGP Algorithm using a 1+4 evolutionary strategy. The data projection functions were evolved using CGP for 1000 generations before stopping to extract the best functions. The Classifier was tested using three PROBEN 1 benchmarking datasets which are the PIMA Indians diabetes dataset, Heart Disease dataset and Wisconsin Breast Cancer (WBC) Dataset based on 10 fold cross validation dataset partitioning. Testing results showed that data projection function generated competitive results classification rates: Cancer dataset (97.71%), PIMA Indians dataset (77.92%) and heart disease (85.86%)

Designing and Implementing a Wi-Fi Enabled Mobile Robot

The demand for mobile reconnaissance robots has increased since the beginning of the 21st century war. The army mainly uses these robots for reconnaissance and gathering intelligence data without putting the personnel in danger. In this paper we present the design and implementation of Wi-Fi enabled mobile robot for distributed surveillance applications. The robot is built on a 4 wheel chassis, powered by a Linksys WRT54-GL residential router and an Atmel microcontroller. It is also equipped with an IP camera with added pan/tilt capabilities and on-board sensors which relays feedback information to the human operator. The software on the robot is programmed in C language and the Control Console is programmed in Java. In order to test the performance of the robot, the robot is tested under three scenarios, Indoor, Outdoor and Line of sight