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OPTIMAL TRAINING PARAMETERS FOR CONTINUOUSLY VARYING MIMO CHANNELSPotter, Christopher G., Panagos, Adam G., Kosbar, Kurt, Weeks, William 10 1900 (has links)
ITC/USA 2005 Conference Proceedings / The Forty-First Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2005 / Riviera Hotel & Convention Center, Las Vegas, Nevada / To correctly demodulate a signal sent through a multiple-input multiple-output (MIMO) channel, a receiver may use training to learn the channel parameters. The choice of training parameters can significantly impact system performance. Training too often yields low throughput while training infrequently produces poor channel estimates and increased transmission errors. Previous work on optimal training parameters has focused on the block fading Rayleigh model. This work examines a more general case; finding the training parameters that maximize throughput for a continuously varying channel. Training parameters that maximize a lower bound on channel capacity are determined via simulation, and general guidelines are presented for selecting optimal training parameters.
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Wireless Communications and Spectrum Characterization in Impaired Channel EnvironmentsPagadarai, Srikanth 17 January 2012 (has links)
The demand for sophisticated wireless applications capable of conveying information content represented in various forms such as voice, data, audio and video is ever increasing. In order to support such applications, either additional wireless spectrum is needed or advanced signal processing techniques must be employed by the next-generation wireless communication systems. An immediate observation that can be made regarding the first option is that radio frequency spectrum is a limited natural resource. Moreover, since existing spectrum allocation policies of several national regulatory agencies such as the Federal Communications Commission (FCC) restrict spectrum access to licensed entities only, it has been identified that most of the licensed spectrum across time and frequency is inefficiently utilized. To facilitate greater spectral efficiency, many national regulatory agencies are considering a paradigm shift towards spectrum allocation by allowing unlicensed users to temporarily borrow unused spectral resources. This concept is referred to a dynamic spectrum access (DSA). Although, several spectrum measurement campaigns have been reported in the published literature for quantitatively assessing the available vacant spectrum, there are certain aspects of spectrum utilization that need a deeper understanding. First, we examine two complementary approaches to the problem of characterizing the usage of licensed bands. In the first approach, a linear mixed-effects based regression model is proposed, where the variations in percentage spectrum occupancy and activity period of the licensed user are described as a function of certain independent regressor variables. The second approach is based on the creation of a geo-location database consisting of the licensed transmitters in a specific geographical region and identifying the coverage areas that affect the available secondary channels. Both of these approaches are based on the energy spectral density data-samples collected across numerous frequency bands in several locations in the United States. We then study the mutual interference effects in a coexistence scenario consisting of licensed and unclicensed users. We numerically evaluate the impact of interference as a function of certain receiver characteristics. Specifically, we consider the unlicensed user to utilize OFDM or NOFDM symbols since the appropriate subcarriers can be turned off to facilitate non- contiguous spectrum utilization. Finally, it has been demonstrated that multiple-input and multiple-output (MIMO) antennas yield significant throughput while requiring no increase in transmit power or required bandwidth. However, the separation of spectrally overlapping signals is a challenging task that involves the estimation of the channel. We provide results concerning channel and symbol estimation in the scenario described above. In particular, we focus on the MIMO-OFDM transmission scheme and derive capacity lower bounds due to imperfect channel estimation.
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