Power efficiency and diversity issues for peak power constrained wireless communications

Along with the rapidly increasing demand for high data rate communications, orthogonal frequency division multiplexing (OFDM) has become a popular modulation in current and future communication standards. By distributing a high-speed data stream to many parallel low-rate data streams, OFDM is able to mitigate the detrimental effects of multipath fading using simple one-tap equalizers and achieve high spectral efficiency. However, the OFDM signal waveform suffers from large envelop variations, which are usually measured by the peak-to-average power ratio (PAR). In wireless transmitters, many RF components, especially the power amplifiers, are inherently nonlinear and peak power constrained. Therefore, low power efficiency and/or severe nonlinear distortions are the main shortcomings of OFDM systems. In this dissertation, we develop algorithms and analyze performance bounds for peak power constrained wireless communications. To address the balance between power efficiency and nonlinear distortions, we model the peak power constrained OFDM systems in both statistical and deterministic manners. We first propose an error vector magnitude (EVM) optimization algorithm to strictly satisfy the distortion requirements in accordance with communication standards and provide the maximum power efficiency for OFDM transmitters without receiver-side cooperations. Moreover, we develop a multi-channel partial transmit sequence (MCPTS) PAR reduction method for OFDM-based frequency-division multiple access (OFDM-FDMA) multiuser systems, which can achieve significant power efficiency improvement without using side information. Joint MCPTS and power allocation schemes are also proposed to improve the error performance of OFDM-FDMA systems. Furthermore, diversity-enabled communication systems have practical merits in combating channel fadings. Therefore, in the second part of this dissertation, peak power constrained diversity techniques are proposed. The error performance of peak power constrained single-input multiple-output (SIMO) OFDM is studied. Several low-complexity SIMO-OFDM transceiver designs are presented to collect full antenna diversity with respective performance and complexity tradeoffs. The next major piece of work in this dissertation addresses the design of peak power constrained amplify-and-forward (AF) cooperative networks, which enable the cooperative diversity with single-antenna terminals. The effects of the availability of channel state information and the peak power constraint on the diversity performance are theoretically studied. Design criteria for general diversity-enabled AF relaying strategies are established and further applied to the designs in peak power constrained networks. In the end, a general theorem that relates the diversity gain function with the probability density function of instantaneous signal-to-noise ratio is derived and used to analyze the diversity performance of relay selection schemes.Ph.D.Committee Chair: Zhou, G. Tong; Committee Member: Foley, Robert D.; Committee Member: Kenny, James Stevenson; Committee Member: Li, Ye (Geoffrey); Committee Member: Ma, Xiaol

Online Scheduling on a Single Machine with Grouped Processing Times

We consider the online scheduling problem on a single machine with the assumption that all jobs have their processing times in [p,(1+α)p], where p>0 and α=(5-1)/2. All jobs arrive over time, and each job and its processing time become known at its arrival time. The jobs should be first processed on a single machine and then delivered by a vehicle to some customer. When the capacity of the vehicle is infinite, we provide an online algorithm with the best competitive ratio of (5+1)/2. When the capacity of the vehicle is finite, that is, the vehicle can deliver at most c jobs at a time, we provide another best possible online algorithm with the competitive ratio of (5+1)/2

A Cost-Sensitive Sparse Representation Based Classification for Class-Imbalance Problem

Sparse representation has been successfully used in pattern recognition and machine learning. However, most existing sparse representation based classification (SRC) methods are to achieve the highest classification accuracy, assuming the same losses for different misclassifications. This assumption, however, may not hold in many practical applications as different types of misclassification could lead to different losses. In real-world application, much data sets are imbalanced of the class distribution. To address these problems, we propose a cost-sensitive sparse representation based classification (CSSRC) for class-imbalance problem method by using probabilistic modeling. Unlike traditional SRC methods, we predict the class label of test samples by minimizing the misclassification losses, which are obtained via computing the posterior probabilities. Experimental results on the UCI databases validate the efficacy of the proposed approach on average misclassification cost, positive class misclassification rate, and negative class misclassification rate. In addition, we sampled test samples and training samples with different imbalance ratio and use -measure, -mean, classification accuracy, and running time to evaluate the performance of the proposed method. The experiments show that our proposed method performs competitively compared to SRC, CSSVM, and CS4VM

Diversity-Enabled and Power-Efficient Transceiver Designs for Peak-Power-Limited SIMO-OFDM Systems

Orthogonal frequency division multiplexing (OFDM) has been widely adopted for high data rate wireless transmissions. By deploying multiple receiving antennas, single-input multiple-output- (SIMO-) OFDM can further enhance the performance with spatial diversity. However, due to the large dynamic range of OFDM signals and the nonlinear nature of analog components, it is pragmatic to model the transmitter with a peak-power constraint. A natural question to ask is whether SIMO-OFDM transmissions can still enjoy the antenna diversity in this case. In this paper, the effect of the peak-power limit on the error performance of uncoded SIMO-OFDM systems is studied. In the case that the receiver has no information about the transmitter nonlinearity, we show that full antenna diversity can still be collected by carefully designing the transmitters, while the receiver performs a maximum ratio combining (MRC) method which is implemented the same as that in the average power constrained case. On the other hand, when the receiver has perfect knowledge of the peak-power-limited transmitter nonlinearity, zero-forcing (ZF) equalizer is able to collect full antenna diversity. In addition, an iterative method on joint MRC and clipping mitigation is proposed to achieve high performance with low complexity

Design of a 360-deg panoramic capture system based on a smart phone

A panoramic imaging system has been developed using two afocal lenses and a smart phone that consists of cameras on both sides. An individual afocal lens has been developed with a large field of view (FOV) and a large exit pupil, which can enlarge the FOV of a smart phone camera to above 180 deg. Some issues with the smart phone-based 360-deg panoramic system, such as relative illuminance and assembly tolerance, were analyzed in detail and taken into consideration during the design procedure. A prototype has been developed with a low fabrication cost, yet producing impressive panoramic image quality.Published versio

Improving Entity Linking by Introducing Knowledge Graph Structure Information

Entity linking involves mapping ambiguous mentions in documents to the correct entities in a given knowledge base. Most of the current methods are a combination of local and global models. The local model uses the local context information around the entity mention to independently resolve the ambiguity of each entity mention. The global model encourages thematic consistency across the target entities of all mentions in the document. However, the known global models calculate the correlation between entities from a semantic perspective, ignoring the correlation information between entities in nature. In this paper, we introduce knowledge graphs to enrich the correlation information between entities and propose an entity linking model that introduces the structural information of the knowledge graph (KGEL). The model can fully consider the relations between entities. To prove the importance of the knowledge graph structure, extensive experiments are conducted on multiple public datasets. Results illustrate that our model outperforms the baseline and achieves superior performance