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For uplink large-scale MIMO systems, minimum mean square error (MMSE) algorithm is near-optimal but involves matrix inversion with high complexity. In this paper, we propose to exploit the Gauss-Seidel (GS) method to iteratively realize the MMSE algorithm without the complicated matrix inversion. To further accelerate the convergence rate and reduce the complexity, we propose a diagonal-approximate initial solution to the GS method, which is much closer to the final solution than the traditional zero-vector initial solution. We also propose a approximated method to compute log-likelihood ratios (LLRs) for soft channel decoding with a negligible performance loss. The analysis shows that the proposed GS-based algorithm can reduce the computational complexity from O(K 3 ) to O(K 2 ), where K is the number of users. Simulation results verify that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm, and achieves the near-optimal performance of the classical MMSE algorithm with a small number of iterations.Index Terms-Large-scale MIMO, signal detection, minimum mean square error (MMSE), Gauss-Seidel (GS) method, low complexity.

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Quality-guided phase unwrapping is a widely used technique with different quality definitions and guiding strategies reported. It is thus necessary to do a detailed comparison of these approaches to choose the optimal quality map and guiding strategy. For quality maps, in the presence of noise, transform-based methods are found to be the best choice. However in the presence of discontinuities, phase unwrapping is itself unresolved and hence quality-guided phase unwrapping is not sufficient. For guiding strategies, classical, two-section, and stack-chain guiding strategies are chosen for comparison. If accuracy is the foremost criterion then the classical guiding strategy with a data structure of indexed interwoven linked list is best. If speed is of essence then the stack-chain guiding strategy is the one to use.

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Furfural from lignocellulosic hydrolysates is the prevalent inhibitor to microorganisms during cellulosic ethanol production, but the molecular mechanisms of tolerance to this inhibitor in Zymomonas mobilis are still unclear. In this study, genome-wide transcriptional responses to furfural were investigated in Z. mobilis using microarray analysis. We found that 433 genes were differentially expressed in response to furfural. Furfural up- or down-regulated genes related to cell wall/membrane biogenesis, metabolism, and transcription. However, furfural has a subtle negative effect on Entner-Doudoroff pathway mRNAs. Our results revealed that furfural had effects on multiple aspects of cellular metabolism at the transcriptional level and that membrane might play important roles in response to furfural. This research has provided insights into the molecular response to furfural in Z. mobilis, and it will be helpful to construct more furfural-resistant strains for cellulosic ethanol production.