The great success of deep learning (DL) has inspired researchers to develop more accurate and efficient symbol detectors for multi-input multi-output (MIMO) systems. Existing DL-based MIMO detectors, however, suffer several drawbacks. To address these issues, in this paper, we develop a modeldriven DL detector based on variational Bayesian inference. Specifically, the proposed unrolled DL architecture is inspired by an inverse-free variational Bayesian learning framework which circumvents matrix inversion via maximizing a relaxed evidence lower bound. Two networks are respectively developed for independent and identically distributed (i.i.d.) Gaussian channels and arbitrarily correlated channels. The proposed networks, referred to as VBINet, have only a few learnable parameters and thus can be efficiently trained with a moderate amount of training samples. The proposed VBINet-based detectors can work in both offline and online training modes. An important advantage of our proposed networks over state-of-the-art MIMO detection networks such as OAMPNet and MMNet is that the VBINet can automatically learn the noise variance from data, thus yielding a significant performance improvement over the OAMPNet and MMNet in the presence of noise variance uncertainty. Simulation results show that the proposed VBINet-based detectors achieve competitive performance for both i.i.d. Gaussian and realistic 3GPP MIMO channels.
翻译:深层次学习的巨大成功激励了研究人员为多种投入的多产出(MIMO)系统开发更准确、更高效的符号探测器,但现有的基于DL的MIMO探测器存在若干缺点。为了解决这些问题,我们在本文件中根据不同贝叶西亚的推论,开发了一个模型驱动DL探测器。具体地说,拟议的无滚动的Bayesian 结构受到一个反向变异的Bayesian学习框架的启发,该框架通过最大限度地减少放松证据约束,绕过矩阵反转。两个网络分别为独立和同样分布的(i.d.)高频频道和任意关联渠道开发了两个网络。拟设的网络称为VBINet,仅有少量可学习的参数,因此,可以用适度的培训样本进行高效培训。拟议的VBINet探测器可以在离线和在线培训模式上运作。我们提议的网络超越基于现状的MIMO检测网络(OAMNet和MMMNet)的一个重要优势是,VBINet可以自动从S-MINBA的升级性表现中看出SIMA的升级性结果。