论文标题
大量的MIMO作为一台极端学习机器
Massive MIMO As an Extreme Learning Machine
论文作者
论文摘要
这项工作表明，具有低分辨率类似于数字转换器（ADC）的大量多输入多输出（MIMO）系统形成了自然的极端学习机（ELM）。基站的接收天线充当榆树的隐藏节点，低分辨率ADC充当ELM激活函数。通过向接收的信号添加随机偏差并优化ELM输出权重，系统可以有效地应对硬件障碍，例如功率放大器的非线性和低分辨率ADC。此外，ELM的快速自适应能力允许设计自适应接收器来解决MIMO通道的时变效果。模拟证明了与常规接收器相比，在处理硬件障碍方面，基于ELM的接收器的表现令人鼓舞。
This work shows that a massive multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs) forms a natural extreme learning machine (ELM). The receive antennas at the base station serve as the hidden nodes of the ELM, and the low-resolution ADCs act as the ELM activation function. By adding random biases to the received signals and optimizing the ELM output weights, the system can effectively tackle hardware impairments, such as the nonlinearity of power amplifiers and the low-resolution ADCs. Moreover, the fast adaptive capability of ELM allows the design of an adaptive receiver to address time-varying effects of MIMO channels. Simulations demonstrate the promising performance of the ELM-based receiver compared to conventional receivers in dealing with hardware impairments.
