## Abstract

The sum rate performance of nonlinier quantized precoding using Gibbs sampling are evaluated in a massive multiuser multipleinput multiple-output (MU-MIMO) system in this paper. Massive MUMIMO is a key technology to handle the growth of data traffic. In a full digital massive MU-MIMO system, however, the resolution of digital-toanalogue converters (DACs) in transmit antenna branches have to be low to yield acceptable power consumption. Thus, a combinational optimization problem is solved for the nonlinier quantized precoding to determine transmit signals from finite alphabets output from low resolution DACs. A conventional optimization criterion minimizes errors between desired signals and received signals at user equipments (UEs). However, the system sum rate may decrease as it increases the transmit power. This paper proposes two optimization criteria that take the transmit power into account in order to maximize the sum rate. Mixed Gibbs sampling is applied to obtain the suboptimal solution of the nonlinear optimization problem. Numerical results obtained through computer simulations show that the two proposed criteria achieve higher sum rates than the conventional criterion. On the other hand, the sum rate criterion achieves the largest sum rate while it leads to less throughputs than the MMSE criterion on approximately 60% of subcarriers.

Original language | English |
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Pages (from-to) | 1299-1306 |

Number of pages | 8 |

Journal | IEICE Transactions on Communications |

Volume | E104B |

Issue number | 10 |

DOIs | |

Publication status | Published - 2021 |

## Keywords

- Gibbs sampling
- Low resolution DACs
- Massive MU-MIMO
- Quantized precoding

## ASJC Scopus subject areas

- Software
- Computer Networks and Communications
- Electrical and Electronic Engineering