Multiple-Input Multiple-Output (MIMO) systems are viewed as the last available supply for the ever-growing demand on higher data rates in modern wireless communication systems. Smart exploitation of the traditional wireless resources (time-slots or bandwidth under the same transmit power level) has reached its saturation point. By making better use of the free space between the radio links, based on the multipath radio wave propagation, MIMO systems have shown significant capacity improvement with the same traditional wireless resources. In this multi-disciplinary research, we are exploring the link between the electromagnetic propagation and the information theory. Unlike the majority of recent research work, we model the propagation channel matrix between the transmit/receive elements in a deterministic manner under the Maxwellian framework. Having included the environment properties and the characteristics of the radiating elements, the deterministic approach provides a realistic assessment of the MIMO system performance in specific scenarios. The problem addressed in this research is the evaluation of the multi-antenna systems degrees of freedom (DOF) by employing all the available electromagnetic diversity resources (spatial, pattern and polarization). Based on a developed well-defined power independent dimensionality (PID) metric, we start by investigating the information-bearing potential of the collocated multi-polarization MIMO system. We study the hexapole system (exploiting both electric and magnetic fields in conveying independent information) and compare it to the tripole systems (exploiting the vectorial polarization diversity of one field only). We present numerical results for 3 deterministic scenarios: a canonical free-space (near and far field exact solution), a canonical perfect electric conductor (PEC) corridor using rigorous modal analysis, and a lossy-wall corridor using image ray tracing (IRT). Next, we provide deterministic results for the more interesting sampling problem of the electromagnetic vector fields: given a specific MIMO array size, what is the optimum number of packed multi-polarization antennas (i.e. multi-polarization 1D, 2D or 3D sampling) that yields the largest PID for a given environment and what is the estimate of this PID? Using a canonical case of multi-polarized arrays inside a multipath-rich PEC corridor, we show that the spatial frequency spectrum of the electromagnetic field governs the optimum PID of the site-specific scenario. The problem is analogous to the DOF determination of an essentially time-limited-band-limited 1D scalar function using the framework of the prolate spheroidal wave functions. We also present simulation results for the same sampling problem in a lossy-wall indoor environment using IRT.

Array-to-array, or multiple-input multiple-output (MIMO), links are known to provide extremely high spectral efficiencies in rich multipath environments, such as indoor wireless environments. The selection of a subset of receiver array antennas for a MIMO wireless link has been studied by many as a way to reduce cost and complexity in a MIMO system while providing diversity gain. Combined with a switched multi-beam beamformer, it becomes the beam selection system that can gain high signal-to-interference ratio (SIR) improvement in an interference-imited environment. The objective of this research is to evaluate the performance of low-complexity antenna or beam subset selection methods for small MIMO networks. The types of networks include (1) point-to-point MIMO links with out-of-system interference, (2)multi-user networks with a single, but possibly spatially distributed access point. We evaluate various selection techniques on measured indoor channels, which has not been done before. We propose a new practical selection metric, the peak-to-trough ratio of orthogonal frequency division multiplexing (OFDM) training symbols. We also compare antenna and beam selection on measured indoor channels under more general conditions than has previously been done. Finally, we consider some channel modeling issues associated with beamformers. We investigate the validity of three types of statistical MIMO channel models. A new beamformer is designed based on the ideal of the ``Weichselberger model.'

Thesis (M. Phil.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 59-61). Also available in print.

Thesis (M.S.)--Brigham Young University. Dept. of Electrical and Computer Engineering, 2004. / Includes bibliographical references (p. 57-59).

Includes bibliographical references (p. 124-131).

Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Ingram, Mary Ann; Committee Member: Durgin, Greg; Committee Member: Kenney, Steve; Committee Member: Landgren, Jack; Committee Member: Li, Ye. Part of the SMARTech Electronic Thesis and Dissertation Collection.

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

In downlink multi-user multiple-input multiple-output (MU-MIMO) systems, different users, even multiple data streams serving one user, might require different quality-of-services (QoS). The transceiver should allocate resources to different users aiming at satisfying their QoS requirements. In order to design the optimal transceiver, channel state information is necessary. In practice, channel state information has to to be estimated, and estimation error is unavoidable. Therefore, robust transceiver design, which takes the channel estimation uncertainty into consideration, is important. For the previous robust transceiver designs, bounded estimation errors or Gaussian estimation errors were assumed. However, if there exists unknown distributed interference, the distribution of the channel estimation error cannot be modeled accurately a priori. Therefore, in this thesis, we investigate the robust transceiver design problem in downlink MU-MIMO system under probabilistic QoS constraints with arbitrary distributed channel estimation error. To tackle the probabilistic QoS constraints under arbitrary distributed channel estimation error, the transceiver design problem is expressed in terms of worst-case probabilistic constraints. Two methods are then proposed to solve the worst-case problem. Firstly, the Chebyshev inequality based method is proposed. After the worst-case probabilistic constraint is approximated by the Chebyshev inequality, an iteration between two convex subproblems is proposed to solve the approximated problem. The convergence of the iterative method is proved, the implementation issues and the computational complexity are discussed. Secondly, in order to solve the worst-case probabilistic constraint more accurately, a novel duality method is proposed. After a series of reformulations based on duality and S-Lemma, the worst-case statistically constrained problem is transformed into a deterministic finite constrained problem, with strong duality guaranteed. The resulting problem is then solved by a convergence-guaranteed iteration between two subproblems. Although one of the subproblems is still nonconvex, it can be solved by a tight semidefinite relaxation (SDR). Simulation results show that, compared to the non-robust method, the QoS requirement is satisfied by both proposed algorithms. Furthermore, among the two proposed methods, the duality method shows a superior performance in transmit power, while the Chebyshev method demonstrates a lower computational complexity. / published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

The increasing demand for high-mobility and high data rate in wireless communications results in constraints and problems in the limited radio spectrum, multipath fading, and delay spread. The multiple-input multiple-output (MIMO) system has been generally considered as one of the key technologies for the next generation wireless communication systems. MIMO systems which utilize multiple antennas in both the transmit side and the receive side can overcome the abovementioned challenges since they are able to increase the channel capacity and the spectrum usage efficiency without the need for additional channel bandwidth. The detection algorithm is a big bottleneck in MIMO systems. Generally, it is expected to fulfill two main goals simultaneously: low computational complexity and good error rate performance. However, the existing detection algorithms are either too complicated or suffering from very bad error-rate performance. The purpose of this thesis is to comprehensively investigate the detection algorithms of MIMO systems, and based on that, to develop new methods which can reduce the computational complexity while retain good system performance. Firstly, the background and the principle of MIMO systems and the previous work on the MIMO decoding algorithms conducted by other researchers are thoroughly reviewed. Secondly, the geometrical analysis of the signal detection is investigated, and a geometric decoding algorithm which can offer the optimum BLER performance is proposed. Thirdly, the semidefinite relaxation (SDR) detection algorithms are extended to high-order modulation MIMO systems, and a novel SDR detector for 256-QAM constellations is proposed. The theoretical analysis on the tightness and the complexity are conducted. It demonstrates that the proposed SDR detector can offer better BLER performance, while its complexity is in between those of its two counterparts. Fourthly, we combine the SDR detection algorithms with the sphere decoding. This is helpful for reducing the computational complexity of the traditional sphere decoding since shorter initial radius of the hyper sphere can be obtained. Finally, the novel lattice-reduction-aided SDR detectors are proposed. They can provide near-optimum error rate performance and achieve the full diversity gain with very little computational complexity added compared with the stand-alone SDR detectors. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy