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Online Machine Learning for Wireless Communications: Channel Estimation, Receive Processing, and Resource AllocationLi, Lianjun 03 July 2023 (has links)
Machine learning (ML) has shown its success in many areas such as computer vision, natural language processing, robot control, and gaming. ML also draws significant attention in the wireless communication society. However, applying ML schemes to wireless communication networks is not straightforward, there are several challenges need to addressed: 1). Training data in communication networks, especially in physical and MAC layer, are extremely limited; 2). The high-dynamic wireless environment and fast changing transmission schemes in communication networks make offline training impractical; 3). ML tools are treated as black boxes, which lack of explainability. This dissertation tries to address those challenges by selecting training-efficient neural networks, devising online training frameworks for wireless communication scenarios, and incorporating communication domain knowledge into the algorithm design. Training-efficient ML algorithms are customized for three communication applications: 1). Symbol detection, where real-time online learning-based symbol detection algorithms are designed for MIMO-OFDM and massive MIMO-OFDM systems by utilizing reservoir computing, extreme learning machine, multi-mode reservoir computing, and StructNet; 2) Channel estimation, where residual learning-based offline method is introduced for WiFi-OFDM systems, and a StructNet-based online method is devised for MIMO-OFDM systems; 3) Radio resource management, where reinforcement learning-based schemes are designed for dynamic spectrum access, as well as ORAN intelligent network slicing management. All algorithms introduced in this dissertation have demonstrated outstanding performance in their application scenarios, which paves the path for adopting ML-based solutions in practical wireless networks. / Doctor of Philosophy / Machine learning (ML), which is a branch of computer science that trains machine how to learn a solution from data, has shown its success in many areas such as computer vision, natural language processing, robot control, and gaming. ML also draws significant attention in the wireless communication society. However, applying ML schemes to wireless communication networks is not straightforward, there are several challenges need to addressed: 1). Training issue: unlike areas such as computer vision where large amount of training data are available, the training data in communication systems are limited; 2). Uncertainty in generalization: ML usually requires offline training, where the ML models are trained by artificially generated offline data, with the assumption that offline training data have the same statistical property as the online testing one. However, when they are statistically different, the testing performance can not be guaranteed; 3). Lack of explainability, usually ML tools are treated as black boxes, whose behaviors can hardly be explained in an analytical way. When designed for wireless networks, it is desirable for ML to have similar levels of explainability as conventional methods. This dissertation tries to address those challenges by selecting training-efficient neural networks, devising online training frameworks for wireless communication scenarios, and incorporating communication domain knowledge into the algorithm design. Training-efficient ML algorithms are customized for three communication applications: 1). Symbol detection, which is a critical step of wireless communication receiver processing, it aims to recover the transmitted signals from the corruption of undesired wireless channel effects and hardware impairments; 2) Channel estimation, where transmitter transmits a special type of symbol called pilot whose value and position are known for the receiver, receiver estimates the underlying wireless channel by comparing the received symbols with the known pilots information; 3) Radio resource management, which allocates wireless resources such bandwidth and time slots to different users. All algorithms introduced in this dissertation have demonstrated outstanding performance in their application scenarios, which paves the path for adopting ML-based solutions in practical wireless networks.
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Optimum Design of Doherty RFPA for Mobile WiMAX Base StationsGhazaany, Tahereh S., Abd-Alhameed, Raed, Child, Mark B., Ali, N.T., Rodriguez, Jonathan, Hussaini, Abubakar S. 09 June 2010 (has links)
Yes / RF power amplifiers in mobile WiMAX transceivers operate in an inherently nonlinear manner. It is possible to amplify the signal in the linear region, and avoid distortion, using output power back-off; however, this approach may suffer significant reduction in efficiency and power output. This paper investigates the use of Doherty techniques instead of back-off, to simultaneously achieve good efficiency and acceptable linearity. A 3.5 GHz Doherty RFPA has been designed and optimized using a large signal model simulation of the active device, and performance analysis under different drive levels. However, the Doherty EVM is generally poor for mobile WiMAX. Linearity may be improved by further digital pre-distortion, and a simple pre-distortion method using forward and reverse AM-AM and AM-PM modeling. Measurements on the realized amplifier show that this approach satisfies the EVM requirements for WiMAX base stations. It exhibits a PAE over 60%, and increases the maximum linear output power to 43 dBm, whilst improving the EVM.
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Ultra-Wideband OFDM Radar and Communication SystemSchuerger, Jonathan Paul 23 April 2009 (has links)
No description available.
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Adaptive OFDM Radar Signal DesignSpalding, Scott A., Jr 01 May 2012 (has links)
No description available.
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Pilot-Based Channel Estimation in OFDM SystemWang, Fei 24 May 2011 (has links)
No description available.
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Multi-band OFDM and p-Persistent CSMA/CD-based Indoor Power Line Communication (PLC) SystemsLiu, Quan January 2009 (has links)
No description available.
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ICI Reduction Methods for MC-CDMA SystemsWu, Meng 26 September 2008 (has links)
No description available.
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A Novel Approach to Target Scene Detection and Identification: Theory & ExperimentsSimms, Melissa Jean 10 August 2016 (has links)
No description available.
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Frequency Generalized MC-CDMA Systems and Performance over Multiband Channels and with Multiple Level Orthogonal (MLO) CodesZhang, Jingtao January 2010 (has links)
No description available.
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Approximate Message Passing for Multi-Carrier Transmission over Doubly Selective ChannelsMeng, Dong 19 July 2012 (has links)
No description available.
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