Receive soft antenna selection for noise-limited/interference MIMO channels

Although the Multi-Input and Multi-Output (MIMO) communication systems provide very high data rates with low error probabilities, these advantages are obtained at the expense of having high signal processing tasks and the hardware cost, e.g. expensive Analog-to-Digital (A/D) converters. The increased hardware cost is mainly due to having multiple Radio Frequency (RF) chains (one for each antenna element). Antenna selection techniques have been proposed to lower the number of RF chains and provide a low cost MIMO system. Among them, due to a beamforming capability Soft Antenna Selection (SAS) schemes have shown a great performance improvement against the traditional antenna sub-set selection methods for the MIMO communication systems with the same number of RF chains. A SAS method is basically realized by a pre-processing module which is located in RF domain of a MIMO system. In this thesis, we investigate on the receive SAS-MIMO, i.e. a MIMO system equipped with a SAS module at the receiver side, in noise-limited/interference channels. For a noise-limited channel, we study the SAS-MIMO system for when the SAS module is implemented before Low Noise Amplifier (LNA), so-called pre-LNA, under both spatial multiplexing and diversity transmission strategies. The pre-LNA SAS module only consists of passive elements. The optimality of the pre-LNA SAS method is investigated under two different practical cases of either the external or internal noise dominates. For the interference channel case, the post-LNA SAS scheme is optimized based on Power Angular Spectrum (PAS) of the received interference signals. The analytical derivations for both noise-limited and interference channels are verified via the computer simulations based on a general Rician statistical MIMO channel model. The simulation results reveal a superiority of the post-LNA SAS to the post-LNA SAS at any condition. Moreover, using the simulations performed for the interference channels we show that the post-LNA SAS is upper bounded by the full-complexity MIMO.;Furthermore, we also employ a ray-tracing channel simulation to assess different SAS configurations, i.e. realized by active (post-LNA) and/or passive (pre-LNA) phased array, in terms of signal coverage. In this regard, a rigorous Signal to Noise Ratio (SNR) analysis is performed for each of these SAS realizations. The results show that although the SAS method performance is generally said to be upperbounded by a full-complexity MIMO, it shows a better signal coverage than the full-complexity MIMO.;Since in both above-mentioned channels, noise-limited and interference, the channel knowledge is needed for the SAS optimization, in this thesis we also propose a two-step channel estimation method for the SAS-MIMO. This channel estimation is based on an Orthogonal Frequency-Division Multiplexing (OFDM) MIMO system. Two different estimators of Least-Square (LS) and Minimum-Mean-Square-Error (MMSE) are applied. Simulation results show a superiority of the MMSE method to the LS estimator for a MIMO system simulated under the 802.16 framing strategy. Moreover, a 802.11a framing based SAS-MIMO is simulated using MATLAB SIMULINK to verify the two-step estimation procedure.