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In this letter, we investigate the downlink performance of massive multiple-input multiple-output (MIMO) systems where the base station is equipped with one-bit analog-to-digital/digital-to-analog converters (ADC/DACs). We assume that the base station employs the linear minimum mean-squared-error channel estimator and treats the channel estimate as the true channel to precode the data symbols. We...
This paper considers training-based transmissions in massive multi-input multi-output (MIMO) systems with one-bit analog-to-digital converters (ADCs). We assume that each coherent transmission block consists of a pilot training stage and a data transmission stage. The base station (BS) first employs the linear minimum mean-square-error (LMMSE) method to estimate the channel and then uses the maximum-ratio...
This paper investigates optimal resource allocation scheme for a typical uplink single-cell massive MIMO system over spatially correlated fading channels. To reduce the pilot contamination effect, we first propose the orthogonal user grouping strategy to partition the terminals into serval groups and assume the users in the same group reuse an identical orthogonal pilot sequence. Employing this strategy,...
Dimensionality reduction has been demonstrated to be an effective way for feature extraction in the pattern recognition task. In this paper, a new manifold learning algorithm, Local Discriminant Space Alignment (LDSA), is developed for nonlinear dimensionality reduction. In LDSA, the discriminant structure and the local geometry of data manifold is learned by constructing a local space for each data...
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