This paper investigates the joint optimization of power allocation and antenna activation in sparse extremely large aperture array systems operating under power amplifier non-linearities. We first derive an analytical expression for the achievable spectral efficiency (SE) of point-to-point MIMO channels affected by non-linear distortions using the Bussgang decomposition. To address the combinatorial and non-convex nature of the energy-efficiency (EE) maximization problem, we employ an unsupervised deep neural network (DNN) that learns the non-linear mapping between the channel state information and the optimal EE operating point. The DNN jointly predicts distortion-aware power allocation, total transmit power scaling, and modular sub-array activation based on singular-value and geometric channel features. Numerical results demonstrate that the proposed DNN-based arrays achieve significant EE gains over the conventional sparse arrays.

翻译：本文研究了在功率放大器非线性条件下，稀疏超大孔径阵列系统中功率分配与天线激活的联合优化问题。首先，利用Bussgang分解，推导了受非线性失真影响的点对点MIMO信道可达频谱效率的解析表达式。针对能效最大化问题的组合与非凸特性，我们采用无监督深度神经网络来学习信道状态信息与最优能效工作点之间的非线性映射。该深度神经网络基于奇异值与几何信道特征，联合预测失真感知功率分配、总发射功率缩放及模块化子阵列激活。数值结果表明，与常规稀疏阵列相比，所提出的基于深度神经网络的阵列可显著提升能效增益。