The conventional power allocation strategy via water-filling relies on the premise that the power amplifier (PA) operates sufficiently below saturation such that a linear RF chain model holds. This work integrates the PA nonlinearity directly into the power allocation formulation, thereby removing the linearity assumption altogether and enabling operation in regimes where distortion noise is non-negligible. Leveraging the Bussgang theorem, we establish a statistical linearization of the PA's hard-limiting model to characterize the trade-off between signal gain and power-dependent distortion. We propose a projected gradient descent algorithm that optimizes power allocation while identifying an optimal spatial back-off strategy. We also derive a closed-form thermal noise variance threshold that separates the noise-limited and distortion-limited operating regimes as a function of the distortion noise variance and the channel Frobenius norm. Numerical simulations validate that our amplifier-aware strategy provides significant capacity gains in the saturation regime compared to standard water-filling.

翻译：传统基于注水算法的功率分配策略依赖于功率放大器（PA）在线性射频链路模型下充分低于饱和状态运行的假设。本研究直接将PA非线性特性纳入功率分配公式，彻底摒弃线性假设，使系统可在失真噪声不可忽略的机制中运行。基于Bussgang定理，我们建立了PA硬限幅模型的统计线性化方法，以表征信号增益与功率相关失真之间的权衡关系。我们提出一种投影梯度下降算法，在优化功率分配的同时确定最优空间退避策略。同时推导出热噪声方差闭式阈值，该阈值可依据失真噪声方差与信道Frobenius范数，将噪声受限与失真受限运行机制区分开。数值仿真验证表明，与标准注水算法相比，所提出的功放感知策略在饱和机制下能显著提升信道容量。