Use of low-resolution (1-4 bits) Analog-to-Digital Converters (ADCs) can reduce power consumption in Massive Multiple-Input, Multiple-Output (MIMO) receivers. Ordinary linear beamforming may suffice for low-resolution ADCs under conditions on the Signal-to-Noise Ratio (SNR) and number of antennas that may be called low but sufficient resolution. However, if the SNR increases or number of antennas decreases, an error floor will typically occur. We introduce three low-complexity iterative algorithms to reduce quantization noise in such low but insufficient resolution cases. These algorithms process the raw quantizer outputs prior to detection, achieving up to two orders-of-magnitude reduction in Bit Error Rate (BER). Our algorithms are based on the new ‘equivalent model’ for quantizers developed in our prior work. These algorithms can be applied to any number of bits and any modulation format. We focus on Orthogonal Frequency-Division Multiplexing (OFDM) to show that quantizer distortion can be corrected without going to the frequency domain.

中文翻译：

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减少由于大规模 MIMO 接收器分辨率不足而导致的量化器失真

使用低分辨率（1-4 位）模数转换器 (ADC) 可以降低大规模多输入多输出 (MIMO) 接收器的功耗。在信噪比 (SNR) 和可称为低但足够分辨率的天线数量的条件下，普通线性波束成形可能足以满足低分辨率 ADC 的要求。然而，如果 SNR 增加或天线数量减少，通常会出现错误本底。我们介绍了三种低复杂度迭代算法，以在分辨率低但分辨率不足的情况下减少量化噪声。这些算法在检测之前处理原始量化器输出，实现误码率 (BER) 最多两个数量级的降低。我们的算法基于我们之前工作中开发的量化器的新“等效模型”。这些算法可以应用于任何数量的比特和任何调制格式。我们专注于正交频分复用 (OFDM)，以表明无需进入频域即可校正量化器失真。