Optimized Full-Duplex Multi-Antenna Relay in Single-Input Single-Output Link

This thesis studies the performance evaluation and optimization of full-duplex multiple-input multiple-output (MIMO) relaying systems in single-input single-output (SISO) link, based on signal-to-interference-plus-noise ratio (SINR). Relays are transceivers which can improve the throughput of a system by coverage extension in a power-efficient manner, whereas full-duplex (FD) systems are point-to-point communication systems, in which transmission and reception occurs simultaneously on a single frequency band. Deploying relaying systems in the full-duplex mode, however, causes self-interference, because the signal transmitted from the transmitter side of the relay couples at its receiver side. This interference causes performance degradation in these systems. In this thesis, a one-way SISO communication link with a MIMO relay connecting the source and the destination nodes is studied. The relay is considered to be implementing either amplify-and-forward (AF) or decode-and-forward (DF) protocol. First, the end-to-end SINR of the system is derived. With the knowledge of SINR, numerical evaluation is made via computer simulations. The numerical results are reached by introducing different assumptions to the general system, as well as by keeping the system intact. Although the numerical solutions provide high performance, they require much time and computational power. Hence, this thesis offers some computationally efficient analytical solutions to the problem. For example, after setting the transmit filter of the relay, minimum mean square error (MMSE) method is applied on the first hop to optimize the system; or by assuming the relay self-interference channel is a rank-one matrix, a closed-form solution for the transmitter and receiver relay filters eliminating the self-interference is derived. Then, the performance of these methods are compared and discussed in different aspects; such as high SINR and computational requirement. The results indicate that each scheme has certain benefits over the others depending on the system design requirements

Multi-Antenna Techniques for Next Generation Cellular Communications

Future cellular communications are expected to offer substantial improvements for the pre- existing mobile services with higher data rates and lower latency as well as pioneer new types of applications that must comply with strict demands from a wider range of user types. All of these tasks require utmost efficiency in the use of spectral resources. Deploying multiple antennas introduces an additional signal dimension to wireless data transmissions, which provides a significant alternative solution against the plateauing capacity issue of the limited available spectrum. Multi-antenna techniques and the associated key enabling technologies possess unquestionable potential to play a key role in the evolution of next generation cellular systems. Spectral efficiency can be improved on downlink by concurrently serving multiple users with high-rate data connections on shared resources. In this thesis optimized multi-user multi-input multi-output (MIMO) transmissions are investigated on downlink from both filter design and resource allocation/assignment points of view. Regarding filter design, a joint baseband processing method is proposed specifically for high signal-to-noise ratio (SNR) conditions, where the necessary signaling overhead can be compensated for. Regarding resource scheduling, greedy- and genetic-based algorithms are proposed that demand lower complexity with large number of resource blocks relative to prior implementations. Channel estimation techniques are investigated for massive MIMO technology. In case of channel reciprocity, this thesis proposes an overhead reduction scheme for the signaling of user channel state information (CSI) feedback during a relative antenna calibration. In addition, a multi-cell coordination method is proposed for subspace-based blind estimators on uplink, which can be implicitly translated to downlink CSI in the presence of ideal reciprocity. Regarding non-reciprocal channels, a novel estimation technique is proposed based on reconstructing full downlink CSI from a select number of dominant propagation paths. The proposed method offers drastic compressions in user feedback reports and requires much simpler downlink training processes. Full-duplex technology can provide up to twice the spectral efficiency of conventional resource divisions. This thesis considers a full-duplex two-hop link with a MIMO relay and investigates mitigation techniques against the inherent loop-interference. Spatial-domain suppression schemes are developed for the optimization of full-duplex MIMO relaying in a coverage extension scenario on downlink. The proposed methods are demonstrated to generate data rates that closely approximate their global bounds

Transfer Learning for Electricity Price Forecasting

Electricity price forecasting is an essential task for all the deregulated markets of the world. The accurate prediction of the day-ahead electricity prices is an active research field and available data from various markets can be used as an input for forecasting. A collection of models have been proposed for this task, but the fundamental question on how to use the available big data is often neglected. In this paper, we propose to use transfer learning as a tool for utilizing information from other electricity price markets for forecasting. We pre-train a bidirectional Gated Recurrent Units (BGRU) network on source markets and finally do a fine-tuning for the target market. Moreover, we test different ways to use the input data from various markets in the models. Our experiments on five different day-ahead markets indicate that transfer learning improves the performance of electricity price forecasting in a statistically significant manner

Electricity price forecasting using recurrent neural networks

Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of electricity prices, which includes high volatility, sharp price spikes and seasonality, various types of electricity price forecasting models still compete and cannot outperform each other consistently. Neural Networks have been successfully used in machine learning problems and Recurrent Neural Networks (RNNs) have been proposed to address time-dependent learning problems. In particular, Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) are tailor-made for time series price estimation. In this paper, we propose to use multi-layer Gated Recurrent Units as a new technique for electricity price forecasting. We have trained a variety of algorithms with three-year rolling window and compared the results with the RNNs. In our experiments, three-layered GRUs outperformed all other neural network structures and state-of-the-art statistical techniques in a statistically significant manner in the Turkish day-ahead market

Examining the Relationship between the Balance Sheet Accounts of US Biotechnology, Telecommunications and Transportation Sectors

After the period of financial liberalization, the activities of firms can create signals for financial and macroeconomic environment and the relationship between financial and macroeconomic variables can be captured by firm-based empirical evidence. From this point of, we employ panel least squares method to investigate the interactions between the balance sheets accounts of US firms in biotechnology, telecommunications and transportation sectors. Our results expose that the technology level is not sufficient to promote the activity of firms and their liquidity. It is also revealed that the number of employee positively affects the cash account whereas the property account does not have a significant impact on cash. According to our estimations, we suggest that an optimal empirical framework should be derived to capture the microeconomic origins of macroeconomic developments in terms of effects of total productivity shocks in those sectors.(C) 2016 The Authors. Published by Elsevier B.V

The financial effect of the electricity price forecasts' inaccuracy on a hydro-based generation company

Electricity price forecasting has a paramount effect on generation companies (GenCos) due to the scheduling of the electricity generation scheme according to electricity price forecasts. Inaccurate electricity price forecasts could cause important loss of profits to the suppliers. In this paper, the financial effect of inaccurate electricity price forecasts on a hydro-based GenCo is examined. Electricity price forecasts of five individual and four hybrid forecast models and the ex-post actual prices are used to schedule the hydro-based GenCo using Mixed Integer Linear Programming (MILP). The financial effect measures of profit loss, Economic Loss Index (ELI) and Price Forecast Disadvantage Index (PFDI), as well as Mean Absolute Error (MAE) of the models are used for comparison of the data from 24 weeks of the year. According to the results, a hybrid model, 50% Artificial Neural Network (ANN)–50% Long Short Term Memory (LSTM), has the best performance in terms of financial effect. Furthermore, the forecast performance evaluation methods, such as Mean Absolute Error (MAE), are not necessarily coherent with inaccurate electricity price forecasts’ financial effect measures

Factors Affecting Inadequate Empirical Antimicrobial Therapy and the Clinical Course of Upper Urinary Tract Infections in Elderly Patients: A Multicenter Study

Introduction: In this study, we aimed to determine the risk factors associated with inadequate empirical antibiotherapy (IEAT) and hospital-related mortality in elderly patients being treated for upper urinary tract infections (UTI)