Graduate Thesis Or Dissertation


Channel estimation and data detection for mobile MIMO OFDM systems Public Deposited

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  • Designing spectral efficient, high-speed wireless links that offer high quality- of-service and range capability has been a critical research and engineering challenge. In this thesis, we mainly address the complexity and performance issues of channel estimation and data detection in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems over time-varying channels. We derive the probability density function (pdf) expressions of the condition number (i.e., the maximum-to-minimum-singular-value ratio, MMSVR) of the channel state information matrix of MIMO OFDM systems. It is shown that this ratio is directly related to the noise enhancement in open-loop systems and provides a significant insight on the system capacity. A decision-directed (DD) maximum a posteriori probability (MAP) channel estimation scheme of MIMO systems is derived. Error performance of a zero- forcing receiver with the DD MAP and perfect channel estimates is provided and compared. This scheme has a low complexity and can be applied to time-varying Rayleigh fading channels with an arbitrary spaced-time correlation function. We propose an iterative channel estimation and data detection scheme for MIMO OFDM systems in the presence of inter-carrier-interference (ICI) due to the nature of time-varying channels. An ICI-based minimum-mean-square error (MMSE) detection scheme is derived. An expectation-maximization (EM) based least square (LS) channel estimator is proposed to minimize the mean-square error (MSE) of the channel estimates and to reduce the complexity of the implementation. With the estimate of the channel and initially detected symbols, ICI is estimated and removed from the received signal. Thus more accurate estimation of the channel and data detection can be obtained in the next iteration. An EM-based MAP channel estimator is derived by exploiting the frequency/time correlation of the pilot and data sub-carriers. Performance comparison is made between the proposed schemes and the ideal case - time-invariant channels and perfect channel estimation. We optimize the data transmission by exploiting the long term correlation characteristics. The transmitted data is successively detected without an error floor in spatially correlated channels. The algorithms proposed in this thesis allow low-complexity implementation of channel estimation and data detection for MIMO OFDM systems over time-varying fading channels, while providing good error performance.
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