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- W3021156279 abstract "Massive multiple-input multiple-output (MIMO) is an enabling technology for next-generation wireless systems because it provides significant improvements in data rates compared to existing small-scale MIMO systems. However, the increased number of antennas results in high computational complexity for data detection, and requires more efficient detection algorithms. In this paper, we propose a new data detector based on a box-constrained complex-valued dichotomous coordinate descent (BCC-DCD) algorithm for large-scale MIMO systems. The proposed detector involves two steps. First, a transmitted data vector is detected using the BCC-DCD algorithm with a large number of iterations and high solution precision. Second, an improved soft output is generated by reapplying the BCC-DCD algorithm, but with a considerably smaller number of iterations and 1-bit solution precision. Numerical results demonstrate that the proposed method outperforms existing advanced detectors while possessing lower complexity. Specifically, the proposed method provides significantly better detection performance than a BCC-DCD algorithm with similar complexity. The performance advantage increases as the signal-to-noise ratio and the system size increase." @default.
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- W3021156279 date "2020-11-01" @default.
- W3021156279 modified "2023-09-26" @default.
- W3021156279 title "Efficient Detection for Large-Scale MIMO Systems Using Dichotomous Coordinate Descent Iterations" @default.
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- W3021156279 doi "https://doi.org/10.1587/transcom.2019ebp3223" @default.
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