As a key technology for future wireless networks, massive multiple-input multiple-output (MIMO) can significantly improve the energy efficiency (EE) and spectral efficiency (SE), and the performance is highly dependant on the degree of the available channel state information (CSI). While most existing works on massive MIMO focused on the case where the instantaneous CSI at the transmitter (CSIT) is available, it is usually not an easy task to obtain precise instantaneous CSIT. In this paper, we investigate EE-SE tradeoff in single-cell massive MIMO downlink transmission with statistical CSIT. To this end, we aim to optimize the system resource efficiency (RE), which is capable of striking an EE-SE balance. We first figure out a closed-form solution for the eigenvectors of the optimal transmit covariance matrices of different user terminals, which indicates that beam domain is in favor of performing RE optimal transmission in massive MIMO downlink. Based on this insight, the RE optimization precoding design is reduced to a real-valued power allocation problem. Exploiting the techniques of sequential optimization and random matrix theory, we further propose a low-complexity suboptimal two-layer water-filling-structured power allocation algorithm. Numerical results illustrate the effectiveness and near-optimal performance of the proposed statistical CSI aided RE optimization approach.

中文翻译：

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具有统计 CSIT 的大规模 MIMO 下行链路传输中的频谱效率和能量效率权衡

作为未来无线网络的关键技术，大规模多输入多输出（MIMO）可以显着提高能量效率（EE）和频谱效率（SE），其性能高度依赖于信道状态信息的可用程度(CSI)。虽然大多数现有的大规模 MIMO 工作都集中在发射机处的瞬时 CSI (CSIT) 可用的情况下，但获得精确的瞬时 CSIT 通常并非易事。在本文中，我们研究了具有统计 CSIT 的单小区大规模 MIMO 下行链路传输中的 EE-SE 权衡。为此，我们的目标是优化系统资源效率 (RE)，它能够达到 EE-SE 平衡。我们首先对不同用户终端的最优传输协方差矩阵的特征向量求出一个闭式解，这表明波束域有利于在大规模 MIMO 下行链路中进行 RE 优化传输。基于这种见解，RE优化预编码设计被简化为实值功率分配问题。利用序列优化和随机矩阵理论的技术，我们进一步提出了一种低复杂度的次优两层注水结构功率分配算法。数值结果说明了所提出的统计 CSI 辅助 RE 优化方法的有效性和接近最佳的性能。我们进一步提出了一种低复杂度的次优两层注水结构功率分配算法。数值结果说明了所提出的统计 CSI 辅助 RE 优化方法的有效性和接近最佳的性能。我们进一步提出了一种低复杂度的次优两层注水结构功率分配算法。数值结果说明了所提出的统计 CSI 辅助 RE 优化方法的有效性和接近最佳的性能。