Software Defined Radio (SDR) and Cognitive Radio (CR) are entering mainstream. These high performance and high adaptability requiring devices with agile frequency operations hold promise to :1. address the inconsistency between hardware and software advancements, 2. real time mode switching from one radio configuration to another and3. efficient spectrum management in under-utilized spectrum bands. Framed within this statement, in this thesis we have implemented a SDR waveform on 16 Processing Element (PE) Network on chip (NoC) based general purpose Multiprocessors System on chip (MPSoC), with access to four external DDR2 memory banks, which is implemented on a single chip Xilinx Virtex-4 FPGA. We shifted short term development of a waveform into software domain by designing an efficient parallelization and synchronization strategy for each waveform component, individually. We enhance our designed waveform functionality by proposing and implementing three Artificial Neural Networks Schemes : Self Organizing Maps, Linear Vector Quantization and Multi-Layer Perceptrons as effective techniques for reconfiguring CR Transceiver after recognizing the specific standard based on input parameters, pertaining to different layers, extracted from the signal. Our proposed adaptive solution switches to appropriate Artificial Neural Network, based on the features of input signal sensed. We designed an efficient synchronization and parallelization strategy to implement the Artificial Neural Networks based CR Transceiver Algorithms on the aforementioned MPSoC chip. The speed up we obtained for our SDR waveform and CR Transceiver algorithms demonstrated that the general purpose MPSoC devices are the most efficient answer to the acquisition challenge for major organizations that invest or plan to invest in SDR and CR based devices, thereby allowing us to avoid expensive hardware accelerators. We address the case of a complex signal composed of many modulated carriers by dividing the PEs in individual groups, thus received signal with more than one Standard is processed efficiently. We add further functionality in our designed Multi-standard CR Transceiver possessing SDR Waveform by proposing a new approach for radio spectrum management, perhaps the most important aspect of CR. We make our designed waveform Spectrum efficient by modelling the primary user signal Radio Frequency features as a multivariate time series, which is then given as input to Elman Recurrent Neural Network that predicts the evolution of Radio Frequency Time Series to decide if the secondary user can exploit the Spectrum band. We exploit the inherent cyclostationary in primary signals for Non-linear Autoregressive Exogenous Time Series Modeling of Radio Frequency features, as predicting one RF feature needs the previous knowledge of other relevant RF features. We observe a similar trend between predicted and actual values. Ensemble, our designed Spectrum Efficient SDR waveform with a Universal Multi-standard Transceiver answers the SDR and CR performance requirements under resource constraints by efficient algorithm design and implementation using lateral thinking that seeks a greater cross-domain interaction
| Identifer | oai:union.ndltd.org:CCSD/oai:pastel.archives-ouvertes.fr:pastel-00665786 |
| Date | 12 September 2011 |
| Creators | Taj, Muhammad Imran |
| Publisher | Université Paris-Est |
| Source Sets | CCSD theses-EN-ligne, France |
| Language | English |
| Detected Language | English |
| Type | PhD thesis |
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