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C-GRBFnet: A Physics-Inspired Generative Deep Neural Network for Channel Representation and Prediction
IEEE Journal on Selected Areas in Communications ( IF 16.4 ) Pub Date : 2022-06-08 , DOI: 10.1109/jsac.2022.3180800
Zhuoran Xiao 1 , Zhaoyang Zhang 1 , Chongwen Huang 1 , Xiaoming Chen 1 , Caijun Zhong 1 , Merouane Debbah 2
Affiliation  

In this paper, we aim to efficiently and accurately predict the static channel impulse response (CIR) with only the user’s position information and a set of channel instances obtained within a certain wireless communication environment. Such a problem is by no means trivial since it needs to reconstruct the high-dimensional information (here the CIR everywhere) from the extremely low-dimensional data (here the location coordinates), which often results in overfitting and large prediction error. To this end, we resort to a novel physics-inspired generative approach. Specifically, we first use a forward deep neural network to infer the positions of all possible images of the source reflected by the surrounding scatterers within that environment, and then use the well-known Gaussian Radial Basis Function network (GRBF) to approximate the amplitudes of all possible propagation paths. We further incorporate the most recently developed sinusoidal representation network (SIREN) into the proposed network to implicitly represent the highly dynamic phases of all possible paths, which usually cannot be well predicted by the conventional neural networks with non-periodic activators. The resultant framework of Cosine-Gaussian Radial Basis Function network (C-GRBFnet) is also extended to the MIMO channel case. Key performance measures including prediction accuracy, convergence speed, network scale and robustness to channel estimation error are comprehensively evaluated and compared with existing popular networks, which show that our proposed network is much more efficient in representing, learning and predicting wireless channels in a given communication environment.

中文翻译:

C-GRBFnet:用于通道表示和预测的受物理启发的生成深度神经网络

在本文中,我们的目标是仅使用用户的位置信息和在特定无线通信环境中获得的一组信道实例来有效和准确地预测静态信道脉冲响应 (CIR)。这样的问题绝非易事,因为它需要从极低维的数据(这里是位置坐标)中重建高维信息(这里是到处的 CIR),这往往会导致过拟合和较大的预测误差。为此,我们采用了一种新颖的受物理启发的生成方法。具体来说,我们首先使用前向深度神经网络来推断该环境中周围散射体反射的所有可能的源图像的位置,然后使用著名的高斯径向基函数网络(GRBF)来近似所有可能传播路径的幅度。我们进一步将最新开发的正弦表示网络 (SIREN) 合并到所提出的网络中,以隐式表示所有可能路径的高度动态相位,这通常不能被具有非周期性激活器的传统神经网络很好地预测。余弦-高斯径向基函数网络(C-GRBFnet)的最终框架也扩展到了 MIMO 信道情况。对预测精度、收敛速度、网络规模和对信道估计误差的鲁棒性等关键性能指标进行了综合评估,并与现有流行网络进行了比较,这表明我们提出的网络在表示、
更新日期:2022-06-08
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