论文标题
硬件障碍下的渠道估计：贝叶斯方法与深度学习
Channel Estimation under Hardware Impairments: Bayesian Methods versus Deep Learning
论文作者
论文摘要
本文考虑了多个Antenna基站和用户设备对上行链路性能的一般硬件障碍的影响。首先，当使用有限大小的信号星座时，有效通道是分析得出的失真感知接收器。接下来，设计和训练了深层喂食神经网络，以估计有效的渠道。将其性能与最先进的失真感和不知道的贝叶斯线性最小均时误差（LMMSE）估计器进行了比较。提出的深度学习方法通过利用损伤特征来提高估计质量，而LMMSE方法将失真视为噪声。
This paper considers the impact of general hardware impairments in a multiple-antenna base station and user equipments on the uplink performance. First, the effective channels are analytically derived for distortion-aware receivers when using finite-sized signal constellations. Next, a deep feedforward neural network is designed and trained to estimate the effective channels. Its performance is compared with state-of-the-art distortion-aware and unaware Bayesian linear minimum mean-squared error (LMMSE) estimators. The proposed deep learning approach improves the estimation quality by exploiting impairment characteristics, while LMMSE methods treat distortion as noise.
