MIMO techniques for spatially correlated multi-carrier delay diversity modulation and radar imaging systems

In cyclic delay diversity (CDD) based multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, correlation can evolve among parallel channels due to closeness of antennas or lack of scatterers in the environment. In this dissertation, pseudo random binary phase offsets are induced into the transmitting symbols to mitigate the correlation effect. Average capacity per sub-carrier and pairwise error probability (PEP) are used as metrics to analyze the system performance for an arbitrary number of transmitting and receiving antennas. Our analysis reveals that depending on the channel spatial correlation, some of the sub-carriers have substantially lower average capacity than others and may, therefore, deteriorate the system performance. In such cases, the proposed scheme enables those sub-carriers to recover from correlation effects and reinstates all the sub-carriers with the same upper bound of the average capacity as offered by the uncorrelated channel. Our analysis of PEP demonstrates the effects of spatial correlation on the squared Euclidean distance between any two codewords and shows how the proposed scheme mitigates those effects without the knowledge of the channel spatial correlation. We observe in simulation that depending on the channel spatial correlation, our proposed scheme can yield a dramatic performance improvement.;In this dissertation, we use MIMO technology to reduce the hardware requirement of a radar imaging system. Due to parallel transmission of the probing signals, an MIMO system suffers from interference. To handle this situation, we address the usage of time reversal space-time block code (TR-STBC) technique to eliminate the effects of interference as well as to exploit transmit diversity. Our numerical results demonstrate that depending on the system configuration, an MIMO radar with TR-STBC and MMSE-like receiver is capable of providing the same image resolution as the traditional one but with almost 60% less antennas, whereas MIMO radar with MMSE-like receiver can only offer 30% less antennas. Here, both objective and subjective evaluation have been performed to determine the quality of image. Mean square error (MSE) and entropy are used as the objective image quality assessment metrics. Finally, we show how the range-angle bins of a group of sensor nodes can be utilized for target detection and localization. While a radar image obtained from a single node can detect the presence of targets, a spatially scattered group of nodes can together interpolate the targets' positions relative to them and obtain localization information.