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• W2550997788 abstract "Data detection in massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems is among the most critical tasks due to the excessively high implementation complexity. In this paper, we propose a novel, equalization-based soft-output data-detection algorithm and corresponding reference FPGA designs for wideband massive MU-MIMO systems that use orthogonal frequency-division multiplexing (OFDM). Our data-detection algorithm performs approximate minimum mean-square error (MMSE) or box-constrained equalization using coordinate descent. We deploy a variety of algorithm-level optimizations that enable near-optimal error-rate performance at low implementation complexity, even for systems with hundreds of base-station (BS) antennas and thousands of subcarriers. We design a parallel VLSI architecture that uses pipeline interleaving and can be parametrized at design time to support various antenna configurations. We develop reference FPGA designs for massive MU-MIMO-OFDM systems and provide an extensive comparison to existing designs in terms of implementation complexity, throughput, and error-rate performance. For a 128 BS antenna, 8-user massive MU-MIMO-OFDM system, our FPGA design outperforms the next-best implementation by more than $2.6 times $ in terms of throughput per FPGA look-up tables." @default.
• W2550997788 created "2016-11-30" @default.
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• W2550997788 date "2016-12-01" @default.
• W2550997788 modified "2023-10-09" @default.
• W2550997788 title "High-Throughput Data Detection for Massive MU-MIMO-OFDM Using Coordinate Descent" @default.
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• W2550997788 doi "https://doi.org/10.1109/tcsi.2016.2611645" @default.
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