Enhanced data Detection for Massive MIMO with 1-Bit ADCs

Amin Radbord, Italo Atzeni, Antti Tolli
Computer Science, Information Theory, Information Theory (cs.IT), Signal Processing (eess.SP)
2023-12-07 00:00:00
We present new insightful results on the uplink data detection for massive multiple-input multiple-output systems with 1-bit analog-to-digital converters. The expected values of the soft-estimated symbols (i.e., after the linear combining and prior to the data detection) have been recently characterized for multiple user equipments (UEs) and maximum ratio combining (MRC) receiver at the base station. In this paper, we first provide a numerical evaluation of the expected value of the soft-estimated symbols with zero-forcing (ZF) and minimum mean squared error (MMSE) receivers for a multi-UE setting with correlated Rayleigh fading. Then, we propose a joint data detection (JD) strategy, which exploits the interdependence among the soft-estimated symbols of the interfering UEs, along with its low-complexity variant. These strategies are compared with a naive approach that adapts the maximum-likelihood data detection to the 1-bit quantization. Numerical results show that ZF and MMSE provide considerable gains over MRC in terms of symbol error rate. Moreover, the proposed JD and its low-complexity variant provide a significant boost in comparison with the single-UE data detection.
PDF: Enhanced data Detection for Massive MIMO with 1-Bit ADCs.pdf
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