We address the issue of reducing the peak to average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM)-based massive multi-user (MU) multiple-input multiple-output (MIMO) downlink systems. Taking advantage of the massive degrees-of-freedom available in large-scale MIMO antenna arrays, we tackle the PAPR-aware precoding, which formulates MU precoding, OFDM modulation, and PAPR reduction into a convex optimization problem. Then the accelerated proximal gradient algorithm (APGM) is developed to solve the above optimization problems. The numerical results indicate that the proposed APGM algorithm has comparable advantages over the existing method in terms of PAPR reduction, symbol error rate (SER), and computational complexity.

中文翻译：

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用于大规模 MIMO-OFDM 下行链路系统的低复杂度 PAPR 感知预编码


我们解决了降低基于正交频分复用（OFDM）的大规模多用户（MU）多输入多输出（MIMO）下行链路系统的峰均功率比（PAPR）的问题。利用大规模 MIMO 天线阵列中的巨大自由度，我们解决了 PAPR 感知预编码问题，它将 MU 预编码、OFDM 调制和 PAPR 降低转化为凸优化问题。然后开发了加速近端梯度算法（APGM）来解决上述优化问题。数值结果表明，所提出的 APGM 算法在 PAPR 降低、符号错误率 (SER) 和计算复杂度方面与现有方法相比具有相当的优势。