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- W4307354329 abstract "Massive multiple-input multiple-output (MIMO) is a promising approach for cellular communication due to its energy efficiency and high achievable data rate. These advantages, however, can be realized only when channel state information (CSI) is available at the transmitter. Since there are many antennas, CSI is too large to feed back without compression. To compress CSI, prior work has applied compressive sensing (CS) techniques and the fact that CSI can be sparsified. The adopted sparsifying bases fail, however, to reflect the spatial correlation and channel conditions or to be feasible in practice. In this paper, we propose a new sparsifying basis that reflects the long-term characteristics of the channel, and needs no change as long as the spatial correlation model does not change. We propose a new reconstruction algorithm for CS, and also suggest dimensionality reduction as a compression method. To feed back compressed CSI in practice, we propose a new codebook for the compressed channel quantization assuming no other-cell interference. Numerical results confirm that the proposed channel feedback mechanisms show better performance in point-to-point (single-user) and point-to-multi-point (multi-user) scenarios." @default.
- W4307354329 created "2022-10-31" @default.
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- W4307354329 date "2015-03-31" @default.
- W4307354329 modified "2023-09-26" @default.
- W4307354329 title "Compressed Channel Feedback for Correlated Massive MIMO Systems" @default.
- W4307354329 doi "https://doi.org/10.48550/arxiv.1503.09002" @default.
- W4307354329 hasPublicationYear "2015" @default.
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