The joint employment of Cloud Radio Access Networks (C-RANs) and large-scale multiple-input multiple-output (MIMO) systems is a key solution to fulfill the requirements of the fifth generation (5G) of wireless networks. However, some challenges are still open to be overcome such as the high power consumption of large-scale MIMO systems, which employ a large number of analog-to-digital converters (ADCs), the capacity bottleneck of the fronthaul links and the system cost reduction. Although it often affects the system performance, the low-resolution quantization is a possible solution for these problems. Therefore, techniques that improve the performance of coarsely quantized systems are needed. In mobile applications, the ADCs are usually preceded by an automatic gain control (AGC). The AGC works shaping the received signal amplitude within the quantizer range to efficiently use the ADC resolution. Then, the optimization of an AGC is especially important. In order to present possible solutions for these issues, this thesis presents a joint optimization of the AGC, which works in the remote radio heads (RRHs), and a low-resolution aware (LRA) linear receive filter based on the minimum mean square error (MMSE), which works in the cloud unit (CU), for coarsely quantized large-scale MIMO with CRAN systems. We develop linear and successive interference cancellation (SIC) receivers based on the proposed joint AGC and LRA MMSE (AGCLRAMMSE) approach. An analysis of the achievable sum rates along with a computational complexity study is also carried out. Simulations show that the proposed design provides improved error rates and higher achievable rates than existing techniques.