This dissertation, "Non-linear Detection Algorithms for MIMO Multiplexing Systems" by Wei, Peng, 彭薇, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled "Non-linear Detection Algorithms for MIMO Multiplexing Systems" Submitted by Peng Wei for the degree of Doctor of Philosophy at The University of Hong Kong in November 2007 The multiple input multiple output (MIMO) technique has attracted a lot of interest due to its potential use in future high speed wireless communications. This thesis focuses on non-linear detection algorithms for MIMO multiplexing systems. The performance of maximum likelihood (ML) detection and successive interference cancellation (SIC) detection are analyzed, and a low-complexity adaptive QR decomposition associated M (QRD-M) algorithm is proposed. A novel method is proposed for the performance analysis of ML detection. In this method, the symbol error probability (SEP) of one transmitted signal is first expressed in terms of the SEPs conditioned on a set of error events corresponding to the other transmitted signals and the probabilities of those error events. By analyzing the post-detection signal to noise ratio (SNR), the conditional SEPs are expressed in closed-form and the SEPs are finally obtained by solving a set of equations. The effects of imperfect channel estimation and power allocation scheme (equal and unequal power allocations) are investigated. The accuracy of the proposed method is demonstrated by Monte-Carlo simulations. It is shown that the analytical results match the simulation ones irrespective of the SNR, which is an advantage over the existing methods where a significant gap generally exists between the analytical and simulation results in the low SNR region. The problem of performance analysis for zero-forcing (ZF) SIC detection is addressed. A method is presented to derive the SEP of the signals detected at each stage. First, the SEPs conditioned on the decision errors at the previous stages are determined in closed-form by analyzing the post-detection SNR and the statistics of the QR decomposed channel matrix. Then, the average SEP at each detection stage is given as the sum of the weighted conditional SEPs. Practical issues including channel estimation errors and the propagation of the decision errors from one detection stage to the next are taken into account. The accuracy of the analytical results is demonstrated by Monte-Carlo simulations. Finally, an adaptive low-complexity QRD-M algorithm is proposed. In the proposed algorithm, the number of candidates for each transmitted signal and the number of surviving paths at each stage are adaptively and independently controlled by an adjustable parameter according to the instantaneous channel conditions and the noise power. The adjustable parameter enables the system designer to compromise between system performance and computational complexity. By Monte-Carlo simulations, it is shown that the proposed algorithm can achieve comparable performance to that of the existing QRD-M algorithms with significantly reduced complexity, especially when modulation with large constellation size is utilized. The number of words: 417 DOI: 10.5353/th_b3955856 Subjects: Demodulation (Electronics)AlgorithmsMIMO systemsWireless communication systems