Abstract
We propose a novel equivalent model for a quantizer with noisy input (the desired signal corrupted by measurement noise). It presents the quantizer output as a sum of the desired signal after it passes through a nonlinear element with a known equivalent transfer function and an equivalent additive white noise. The equivalent transfer function takes the form of a conditional expectation of the quantizer output given the desired signal portion of its input. The proposed model proves to be effective for the analysis and design of MIMO systems employing low-resolution quantizers (analog to digital and digital to analog converters, ADCs and DACs, respectively). We also demonstrate the efficacy of the model through several example applications for 1) the design of digital dither that mitigates the effect of DAC quantization error in a MIMO transmitter and significantly reduces the DAC resolution requirement; 2) the determination of the minimal ADC resolution required for operation of conventional MIMO receivers designed for infinite-resolution ADC arrays, without incurring significant performance degradation; and 3) the design of simple MIMO receivers (ML and MMSE) that mitigate the effect of insufficient ADC resolution, thereby extending the receiver SNR operating range without an undue complexity increase.
Highlights
Massive MIMO is an emerging technology capable of improving the spectral efficiency of wireless communication by orders of magnitude [1]
We propose a simple DAC dithering scheme based on the conditional expectation model (CEM) applied to a MIMO transmitter that significantly reduces DAC resolution and/or transmit error vector magnitude (EVM) seen by a receiver. - We propose an ADC resolution determination methodology based on the CEM applied to MIMO systems, which provides a worst-case performance guarantee for a MIMO system employing naïve receivers that do not consider the effect of quantization
The design rules are articulated in the lexicon familiar to practical system designers, e.g., the noise figure, making them accessible to practicing engineers and complementary to information-theoretic guidelines. - We demonstrate the efficacy and accuracy of the model by devising simple MIMO receivers that compensate for low-resolution-induced distortion, resulting in near-optimal performance
Summary
Massive MIMO is an emerging technology capable of improving the spectral efficiency of wireless communication by orders of magnitude [1]. - We demonstrate the efficacy and accuracy of the model by devising simple MIMO receivers that compensate for low-resolution-induced distortion, resulting in near-optimal performance This simplification is a direct consequence of the CEM presenting instantaneous NLD as a function of signal/noise statistics, but only the desired part of the instantaneous input signal without its VOLUME 8, 2020. - Section III presents the application of our model for the design of digital dither that mitigates the effect of quantization error in the downlink MIMO transmitter and significantly reduces requirements to its DAC resolution. Since the quantization error of complex DACs with optimal dither is a white process with variance 2 3, the EVM of the signal that arrives at user k is always equal to:. For a fixed EVM requirement, the array of M DACs with optimal dither needs log M 2 fewer bits than the same array with conventional DACs
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