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This thesis looks at the problem of channel estimation and equalization in a multiple-input multiple-output (MIMO) dispersive fading environments. Two classes of MIMO receiver structure are proposed with integrated channel estimation and tracking. One is a symbol-by-symbol based receiver using a MIMO minimum mean square error (MMSE) decision feedback equalizer (DFE), and the other is a sequence-based receiver using a partitioned Viterbi algorithm (PVA) which approaches the performance of maximum likelihood sequence estimation (MLSE). A MIMO channel estimator capable of tracking the time and frequency selective channel impulse responses, known as the vector generalized recursive least squares (VGRLS) algorithm, is developed. It has comparable performance and a similar level of complexity as the optimum Kalman filter. However, it does not require any knowledge of the channel statistics to operate and as such it can be employed in a Rician fading channel readily. A reduced complexity form of the estimator, known as the vector generalized least mean squares (VGLMS) algorithm, is also developed. This is achieved by replacing the online recursive computation of the VGRLS algorithm's 'intermediate' Riccatti matrix with an offline pre-computed matrix. This reduces the complexity of the algorithm by an order of a magnitude, but at the expense of degraded performance. The estimators are integrated with the above-mentioned equalizers in a decision directed mode to form a receiver structure that can operate in continuously time-varying fading channels. Due to decision delays, the outputs from the equalizer are delayed and this then produces 'delayed' channel estimates. A simple polynomial-based channel prediction module is employed to provide up-to-date channel estimates required by the equalizers. However, simulation results show that the channel prediction module may be omitted for a very slowly fading channel where the channel responses do not vary much. In the case of the PVA- receiver, the zero-delay tentative decisions are used as feedback to the channel estimators with negligible loss.

In Multiple-Input Multiple-Output (MIMO) broadcast channels, the multi-antenna basestation transmits information to multiple non-cooperative mobile users simultaneously. Among various transmission schemes, zero-forcing beamforming (ZFBF) and random unitary beamforming (RUB) are of particular interest due to their low implementation complexity and ability to explore the multiplexing gain provided by multiple transmit antennas. To investigate the effects of multiuser diversity on sum-rate performance, previous studies of beamforming schemes in multiuser MIMO systems usually employ asymptotical analysis. In this work, while assuming channel gain follows Rayleigh flat fading, we study the sum-rate performance of ZFBF and RUB through exact mathematic analysis. For this purpose, we derive the statistics of selected users's effective channel gain, which enable us to calculate the sum rate accurately and efficiently. With derived sum-rate expressions, we evaluate and compare the sum-rate performance of MIMO broadcast channels with RUB and dual-transmit-antenna ZFBF. In addition, we apply this analytical method to study strategies that mitigate multiuser interference for RUB-based multiuser MIMO systems. The strategies we consider in the thesis include • Reducing the number of served users at a time. We present a new user scheduling scheme, which imposes a threshold On user's SINR for feedback load reduction and only activates those beams that are requested by feedback users. • Exploiting receive diversity. When receivers use more than one antennas, we evaluate the sum-rate performance gain offered by selection combining (SC) and optimum combining (OC) schemes, respectively. In addition to beamforming techniques, we study the symbol error rate (SER) performance of MIMO broadcast channels with vector perturbation (VP) precoding and quantized channel feedback. Based. on the established equivalent relations in terms of minimum mean square error (MMSE) and SER between quantized and perfect channel feedback cases, we investigate the tradeoff between feedback load and achievable diversity gain. / Graduate

Conventional Minimum-Variance (MV) channel estimation is affected by two sources of error, namely the finite number of samples used to estimate the covariance matrix and the asymptotic bias due to interference and additive noise. On the other hand, widely linear (WL) filtering has been shown to improve the estimation of improper complex signals. Researchers have recently demonstrated that the application of WL processing principles can significantly improve the performance of subspace-based channel estimation algorithms. However, in contrast to MV estimation algorithms, subspace-based algorithms assume knowledge of the total number of users in the system, and must be coupled with sophisticated user enumeration algorithm at the expense of increased complexity. In this work, in an effort to combine the practical advantages of MV channel estimation algorithms with the performance of WL filters we propose a widely linear version of the MV channel estimator in the context of multicarrier(MC) CDMA systems employing real modulation. We use numerical simulations to demonstrate that the widely linear minimum-variance algorithm yields more accurate channel estimates compared to the conventional MV algorithm. By considering two simplified transmission/reception models, we also show analytically that the widely linear estimator on average reduces both types of error.