This thesis investigates the uplink scenario in millimetre-wave (mmWave) mas sive multiple-input multiple-output (MIMO) communication systems characterized by dense, uniform linear arrays (ULAs) of antenna elements tightly packed within a con fined space and equipped with low-resolution Analog-to-Digital Converters (ADCs). The primary focus of our study is to address the critical challenges of power con sumption reduction and hardware simplification while simultaneously improving the performance of quantized systems by exploring spatial oversampling. Due to the con sideration of subwavelength inter-element spacing in the ULA, extrinsic spatial thermal noise correlations arise due to significant coupling between adjacent antenna terminals. In addition to this correlated noise, the noise figure caused by hardware imperfections profoundly impacts signal recovery and cannot be dismissed as a negligible factor in system performance analysis. To tackle the problem of signal recovery in such high density ULAs with low-resolution ADCs, we propose a non-linear inference method based on Vector Approximate Message Passing (VAMP) and Belief Propagation. Addi tionally, we employ a state evolution analysis to investigate the algorithm’s asymptotic behaviour. The main objective of this algorithm is to reconstruct transmitted signals from the quantized measurements obtained by the coupled antennas. In this work, we demonstrate that employing oversampling techniques in the context of low-bit quan tized systems can substantially enhance system performance, bringing it closer to the ideal scenario with infinite-resolution ADCs. Remarkably, this performance improve ment persists even when the system is oversampled, emphasizing the potential of spatial oversampling as an effective strategy for enhancing the performance of low-resolution ADCs. We also analyze how the noise figure impacts the recovery in the context of oversampling, highlighting its significance in system design considerations.