Abstract:

Massive multiple-input multiple-output (MIMO) systems have the advantages of high spatial resolution,high spectral efficiency,and wide coverage.Utilizing one-bit digital-to-analog converters (DACs) can significantly reduce the power consumption,hardware complexity,and deployment costs in massive MIMO systems.However,due to the quantized noise introduced by the one-bit DACs,the symbols after linear precoding suffer from inevitable distortions.The system bit error rate (BER) performance will significantly fall and reach saturation prematurely with the increase of the signal-to-noise rate (SNR).Unlike linear precoding,nonlinear precoding considers the effect of one-bit quantization,and directly designs the quantized symbols,which can significantly improve the BER.Considering that the symbols after one-bit quantization belong to a finite set with a few elements,we modeled the nonlinear precoding problem from the perspective of combinatorial optimization,and a cross-entropy based algorithm was proposed.The proposed algorithm adaptively updates the probability distribution of each element in the precoding vector at each iteration by minimizing the cross entropy,and can quickly converge to obtain the precoding vector.Meanwhile,the proposed algorithm can be readily extended to the systems with multi-bit DACs.Simulation results show that the proposed algorithm outperforms existing algorithms based on convex optimization in terms of BER under a high SNR,and is robust to channel estimation errors.Besides,the applicability of the proposed scheme to multi-bit DACs is also verified by simulation.

Key words: massive multi-input multi-output, one-bit digital-to-analog converter, precoding, cross-entropy algorithm