Adaptive MIMO Systems with Channel State Information at Transmitter

This dissertation presents adaptation techniques that can achieve high spectral efficiency for single user multiple-input multiple-output (MIMO) systems. Two types of adaptation techniques, adaptive modulation and adaptive power allocation, are employed to adapt the rate and the transmit power to fading channels. We start by investigating the adaptive modulation subject to a certain bit-error-ratio (BER) constraint, either instantaneous BER constraint or average BER constraint. The resulting average spectral efficiencies are obtained in closed-form expressions. It turns out that, by employing the average BER constraint, we can achieve the optimal average spectral efficiency at the cost of prohibitive computational complexity. On the other hand, instantaneous BER constraint leads to inferior performance with little computational complexity. In order to achieve comparable performance to the average BER constraint with limited complexity, a non-linear optimization method is proposed. To further enhance the average spectral efficiency, adaptive power allocation schemes are considered to adjust the transmit power across the temporal domain or the spatial domain, depending on the specific situation. Provided the closed-form expressions of the average spectral efficiency, the optimal MIMO coding scheme that offers the highest average spectral efficiency under the same circumstances can be identified. As we take into account the effect of imperfect channel estimation, the adaptation techniques are revised to tolerate interference introduced by the channel estimation errors. As a result, the degradation with respect to the average spectral efficiency is in proportion to signal-to-noise ratio (SNR). In order to facilitate fast development and verification of the adaptation schemes proposed for various MIMO systems, a reconfigurable Link Layer Simulator (LiLaS) which accommodates a variety of wireless/wireline applications is designed in the environment of MATLAB/OCTAVE. The idea of the simulator is originated from Software Defined Radio (SDR) and evolved to suit Cognitive Radio (CR) applications. For the convenience of modification and reconfiguration, LiLaS is functionally divided into generic blocks and all blocks are parameterized.