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In massive multiple-input multiple-output (mMIMO) systems, the technique of using low-resolution analog-to-digital converters (ADCs) to quantize received signals can, significantly reduce the receiver hardware cost as well as circuit power consumption., However, it incurs quantization noise in the digital domain which degrades the quality, of signal reception. In particular, for 1- to 3-bit resolution quantizers, communication, systems become very nonlinear and non-Gaussian. Quantization noise can be correlated to the received signal as well as the thermal noise. These are critical issues that, can fundamentally change the way of receiver design and optimization, and thus they, must be rigorously studied., The first contribution of this thesis lies in the use of Hermite polynomials to study, the linear approximation model of low-resolution quantized mMIMO signal reception., Unlike other linear approximation models in the literature that assume the quantization, noise to be white Gaussian and uncorrelated to the received signal, the proposed model, termed second-order Hermite expansion (SOHE), uses the second-order Hermite kernel, to describe the quantization noise and the first-order Hermite kernel for the signal, linear response. Novel mathematical theorems are established to explain key stochastic, characteristics of the quantization noise. The SOHE theory results in an enhanced, LMMSE channel equalizer which considerably improves the equalization performance, particularly for mMIMO receivers using 2-bit quantizers., Another key contribution lies in an intensive study of the well-known dither effect, of low-resolution quantizers, which explains the phenomenon of constructive noise in, the mMIMO signal reconstruction. This study leads to a fundamental rethinking of, mMIMO signal transmission strategy, operating signal-to-noise ratio (SNR) setup and, more fundamentally the threshold optimization for low-resolution signal quantization., All of these provide theoretical support for the optimization of low-resolution quantized, mMIMO., Finally, an outlook of major technical challenges and future direction of the lowresolution mMIMO research is presented.