论文标题
DOA估计的深度神经网络量化信号重建
Deep Neural Network-Based Quantized Signal Reconstruction for DOA Estimation
论文作者
论文摘要
对于配备有射频（RF）链的智能反射表面（IRS）的大规模多输入 - 多输出输出（MIMO）系统，与被动IRS相比，多通道RF链的昂贵，尤其是在每个RF通道中使用高分辨率和高速类似物（ADC）时。在此字母中，IRS中的低成本ADC研究了角度（DOA）估计问题的方向，我们提出了一个深神经网络（DNN）作为低分辨率采样信号的恢复方法。与高斯噪声的现有denoising卷积神经网络（DNCNN）不同，带有完全连接的DNN（FC）层估计ADC引起的量化噪声。然后，对DOA信号进行DOA估计，并且通过DOA估计评估量化信号的恢复性能。仿真结果表明，在相同的训练条件下，提出的网络比最新方法实现更好的重建性能。使用1位ADC的DOA估计的性能改善了使用2位ADC超过该ADC的性能。
For a massive multiple-input-multiple-output (MIMO) system using intelligent reflecting surface (IRS) equipped with radio frequency (RF) chains, the multi-channel RF chains are expensive compared to passive IRS, especially, when the high-resolution and high-speed analog to digital converters (ADC) are used in each RF channel. In this letter, a direction of angle (DOA) estimation problem is investigated with low-cost ADC in IRS, and we propose a deep neural network (DNN) as a recovery method for the low-resolution sampled signal. Different from the existing denoising convolutional neural network (DnCNN) for Gaussian noise, the proposed DNN with fully connected (FC) layers estimates the quantization noise caused by the ADC. Then, the denoised signal is subjected to the DOA estimation, and the recovery performance for the quantized signal is evaluated by DOA estimation. Simulation results show that under the same training conditions, the better reconstruction performance is achieved by the proposed network than state-of-the-art methods. The performance of the DOA estimation using 1-bit ADC is improved to exceed that using 2-bit ADC.
