论文标题
基于GAN的联合活动检测和无授予随机访问的渠道估计
Gan-Based Joint Activity Detection and Channel Estimation For Grant-free Random Access
论文作者
论文摘要
无授予随机访问的联合活动检测和渠道估计（JADCE）是一个关键问题，需要解决以支持IoT网络中的大规模连通性。但是，现有的无模型学习方法只能实现活动检测或通道估计，但不能两者兼而有之。在本文中，我们提出了一种基于生成对抗网络（GAN）的新型无模型学习方法，以解决JADCE问题。我们采用U-NET体系结构来构建生成器，而不是标准GAN体系结构，其中包含活动信息的预估计值被用作发电机的输入。通过利用伪源的属性，通过使用仿射投影和跳过连接来完善发电机，以确保发电机的输出与测量值一致。此外，我们构建了一个两层完全连接的神经网络，以设计降低接收器噪声影响的试验矩阵。仿真结果表明，该提出的方法在高SNR制度中的现有方法优于现有方法，因为数据一致性投影和Pilot矩阵优化都提高了学习能力。
Joint activity detection and channel estimation (JADCE) for grant-free random access is a critical issue that needs to be addressed to support massive connectivity in IoT networks. However, the existing model-free learning method can only achieve either activity detection or channel estimation, but not both. In this paper, we propose a novel model-free learning method based on generative adversarial network (GAN) to tackle the JADCE problem. We adopt the U-net architecture to build the generator rather than the standard GAN architecture, where a pre-estimated value that contains the activity information is adopted as input to the generator. By leveraging the properties of the pseudoinverse, the generator is refined by using an affine projection and a skip connection to ensure the output of the generator is consistent with the measurement. Moreover, we build a two-layer fully-connected neural network to design pilot matrix for reducing the impact of receiver noise. Simulation results show that the proposed method outperforms the existing methods in high SNR regimes, as both data consistency projection and pilot matrix optimization improve the learning ability.
