Doctor of Philosophy

dissertationThe continuous growth of wireless communication use has largely exhausted the limited spectrum available. Methods to improve spectral efficiency are in high demand and will continue to be for the foreseeable future. Several technologies have the potential to make large improvements to spectral efficiency and the total capacity of networks including massive multiple-input multiple-output (MIMO), cognitive radio, and spatial-multiplexing MIMO. Of these, spatial-multiplexing MIMO has the largest near-term potential as it has already been adopted in the WiFi, WiMAX, and LTE standards. Although transmitting independent MIMO streams is cheap and easy, with a mere linear increase in cost with streams, receiving MIMO is difficult since the optimal methods have exponentially increasing cost and power consumption. Suboptimal MIMO detectors such as K-Best have a drastically reduced complexity compared to optimal methods but still have an undesirable exponentially increasing cost with data-rate. The Markov Chain Monte Carlo (MCMC) detector has been proposed as a near-optimal method with polynomial cost, but it has a history of unusual performance issues which have hindered its adoption. In this dissertation, we introduce a revised derivation of the bitwise MCMC MIMO detector. The new approach resolves the previously reported high SNR stalling problem of MCMC without the need for hybridization with another detector method or adding heuristic temperature scaling terms. Another common problem with MCMC algorithms is an unknown convergence time making predictable fixed-length implementations problematic. When an insufficient number of iterations is used on a slowly converging example, the output LLRs can be unstable and overconfident, therefore, we develop a method to identify rare, slowly converging runs and mitigate their degrading effects on the soft-output information. This improves forward-error-correcting code performance and removes a symptomatic error floor in bit-error-rates. Next, pseudo-convergence is identified with a novel way to visualize the internal behavior of the Gibbs sampler. An effective and efficient pseudo-convergence detection and escape strategy is suggested. Finally, the new excited MCMC (X-MCMC) detector is shown to have near maximum-a-posteriori (MAP) performance even with challenging, realistic, highly-correlated channels at the maximum MIMO sizes and modulation rates supported by the 802.11ac WiFi specification, 8x8 256 QAM. Further, the new excited MCMC (X-MCMC) detector is demonstrated on an 8-antenna MIMO testbed with the 802.11ac WiFi protocol, confirming its high performance. Finally, a VLSI implementation of the X-MCMC detector is presented which retains the near-optimal performance of the floating-point algorithm while having one of the lowest complexities found in the near-optimal MIMO detector literature

Performance evaluation of detection algorithms for MOMI OFDM systems

Includes abstract.Includes bibliographical references (leaves 79-86).Introduction of Multi Input Multi Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) as the base air interface method for Next Generation Network (NGN) will face a number of challenges from hostile channel conditions to interference from other users. This would result in an increase of detection complexity required for mobile systems. Complex detection will reduce the battery life of mobile devices because of the many calculations that have to be done to decode the signal. Very powerful detection algorithms exist but they introduce high detection complexity. NGN will employ different MIMO systems, but this research will consider spatially multiplexed MIMO which is used to improve the data rate and network capacity. In NGN different multi access modulation schemes will be used for uplink and downlink but they both have OFDM as the basic building block. In this work performance of MIMO OFDM is investigated in different channels models and detection algorithms. A low complexity detection scheme is proposed in this research to improve performance of MIMO OFDM. The proposed detection scheme is investigated for different channel characteristics. Realistic channels conditions are introduced to evaluate the performance of the proposed detection scheme. We analyze weaknesses of existing linear detectors and the enhancements that can be done to improve their performance in different channel conditions. Performance of the detectors is evaluated by comparison of Bit Error Rate (BER) and Symbol Error Rate (SER) against signal to noise ratio (SNR). This thesis proposes a detector which shows a higher complexity than linear detectors but less than Maximum Likelihood Detector (MLD). The proposed detector shows significant BER improvement in all channel conditions. For better performance evaluation this work also investigates performance of MIMO OFDM detectors in realistic channels like Kronecker and Weichselberger channel models

Unified bit-based probabilistic data association aided MIMO detection for high-order QAM

A unified Bit-based Probabilistic Data Association (B-PDA) detection approach is proposed for Multiple-Input Multiple-Output (MIMO) systems employing high-order Quadrature Amplitude Modulation (QAM). The new approach transforms the symbol detection process of QAM to a bit-based process by introducing a Unified Matrix Representation (UMR) of QAM. Both linear natural and nonlinear Gray bit-to-symbol mapping schemes are considered. Our analytical and simulation results demonstrate that the linear natural mapping based B-PDA approach attains an improved detection performance, despite dramatically reducing the computational complexity in contrast to the conventional symbol-based PDA aided MIMO detector. Furthermore, it is shown that the linear natural mapping based B-PDA method is capable of approaching the lower bound performance provided by the nonlinear Gray mapping based B-PDA MIMO detector. Since the linear natural mapping based scheme is simpler and more applicable in practice than its nonlinear Gray mapping based counterpart, we conclude that in the context of the uncoded B-PDA MIMO detector it is preferable to use the linear natural bit-to-symbol mapping, rather than the nonlinear Gray mapping

Combined adaptive lattice reduction-aided detection and antenna shuffling for DSTTD-OFDM systems

In this paper, we consider lattice reduction (LR) aided linear detection in a DSTTD-OFDM (double space-time transmit diversity- orthogonal frequency division multiplexing) system with antenna shuffling. We first derive an antenna shuffling criterion for the LR-aided DSTTD-OFDM system. Next, we propose a combined reduced-feedback and adaptive LR algorithm by exploiting the correlation between OFDM subcarriers in the frequency domain. The LR-aided DSTTD OFDM system with this algorithm requires low computational effort for the LR operation and small feedback information. Simulation results show that a significant improvement could be achieved in the proposed system compared to previous (non-LR-aided) systems under spatially correlated channels. Also, the proposed complexity-reduced approach could greatly lower the system complexity while exhibiting a slight performance loss