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Large-scale multi-user multiple-input multiple-output (MU-MIMO) systems andcloud radio access networks (C-RANs) are considered promising technologies forthe fifth generation (5G) of wireless networks. In these technologies, the useof low-resolution analog-to-digital converters (ADCs) is key for energyefficiency and for complying with constrained fronthaul links. Processingsignals with few bits implies a significant performance loss and, therefore,techniques that can compensate for quantization distortion are fundamental. Inwireless systems, an automatic gain control (AGC) precedes the ADCs to adjustthe input signal level in order to reduce the impact of quantization. In thiswork, we propose the joint optimization of the AGC, which works in the remoteradio heads (RRHs), and a low-resolution aware (LRA) linear receive filterbased on the minimum mean square error (MMSE), which works in the cloud unit(CU), for large-scale MU-MIMO systems with coarsely quantized signals. Wedevelop linear and successive interference cancellation (SIC) receivers basedon the proposed joint AGC and LRA MMSE (AGC-LRA-MMSE) approach. An analysis ofthe achievable sum rates along with a computational complexity study is alsocarried out. Simulations show that the proposed AGC-LRA-MMSE design providessubstantial gains in bit error rates and achievable information rates overexisting techniques.

Joint AGC and Receiver Design for Large-Scale MU-MIMO Systems Using Low-Resolution Signal Processing in C-RANs