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Recently, multi-user multiple input multiple output (MU-MIMO) systems withlow-resolution digital-to-analog converters (DACs) has received considerableattention, owing to the capability of dramatically reducing the hardware cost.Besides, it has been shown that the use of low-resolution DACs enable greatreduction in power consumption while maintain the performance loss withinacceptable margin, under the assumption of perfect knowledge of channel stateinformation (CSI). In this paper, we investigate the precoding problem for thecoarsely quantized MU-MIMO system without such an assumption. The channeluncertainties are modeled to be a random matrix with finite second-orderstatistics. By leveraging a favorable relation between the multi-bit DACsoutputs and the single-bit ones, we first reformulate the original complexprecoding problem into a nonconvex binary optimization problem. Then, using theS-procedure lemma, the nonconvex problem is recast into a tractable formulationwith convex constraints and finally solved by the semidefinite relaxation (SDR)method. Compared with existing representative methods, the proposed precoder isrobust to various channel uncertainties and is able to support a MUMIMO systemwith higher-order modulations, e.g., 16QAM.

Robust Precoding Design for Coarsely Quantized MU-MIMO Under Channel Uncertainties