Millimeter Wave (mmWave) communications with full-duplex (FD) have the potential of increasing the spectral efficiency, relative to those with half-duplex. However, the residual self-interference (SI) from FD and high pathloss inherent to mmWave signals may degrade the system performance. Meanwhile, hybrid beamforming (HBF) is an efficient technology to enhance the channel gain and mitigate interference with reasonable complexity. However, conventional HBF approaches for FD mmWave systems are based on optimization processes, which are either too complex or strongly rely on the quality of channel state information (CSI). We propose two learning schemes to design HBF for FD mmWave systems, i.e., extreme learning machine based HBF (ELM-HBF) and convolutional neural networks based HBF (CNN-HBF). Specifically, we first propose an alternating direction method of multipliers (ADMM) based algorithm to achieve SI cancellation beamforming, and then use a majorization-minimization (MM) based algorithm for joint transmitting and receiving HBF optimization. To train the learning networks, we simulate noisy channels as input, and select the hybrid beamformers calculated by proposed algorithms as targets. Results show that both learning based schemes can provide more robust HBF performance and achieve at least 22.1% higher spectral efficiency compared to orthogonal matching pursuit (OMP) algorithms. Besides, the online prediction time of proposed learning based schemes is almost 20 times faster than the OMP scheme. Furthermore, the training time of ELM-HBF is about 600 times faster than that of CNN-HBF with 64 transmitting and receiving antennas.

中文翻译：

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基于学习的全双工毫米波系统混合波束成形设计

与半双工通信相比，全双工 (FD) 毫米波 (mmWave) 通信具有提高频谱效率的潜力。然而，来自 FD 的残余自干扰 (SI) 和毫米波信号固有的高路径损耗可能会降低系统性能。同时，混合波束成形（HBF）是一种以合理复杂度提高信道增益和减轻干扰的有效技术。然而，用于 FD 毫米波系统的传统 HBF 方法基于优化过程，这些过程要么过于复杂，要么强烈依赖于信道状态信息 (CSI) 的质量。我们提出了两种学习方案来设计FD mmWave系统的HBF，即基于极限学习机的HBF（ELM-HBF）和基于卷积神经网络的HBF（CNN-HBF）。具体来说，我们首先提出了一种基于乘法器（ADMM）算法的交替方向方法来实现SI抵消波束成形，然后使用基于majorization-minimization（MM）的算法进行联合发射和接收HBF优化。为了训练学习网络，我们将噪声通道模拟为输入，并选择由所提出算法计算的混合波束形成器作为目标。结果表明，与正交匹配追踪 (OMP) 算法相比，两种基于学习的方案都可以提供更稳健的 HBF 性能，并实现至少 22.1% 的更高频谱效率。此外，所提出的基于学习的方案的在线预测时间几乎比 OMP 方案快 20 倍。此外，ELM-HBF 的训练时间比具有 64 个发射和接收天线的 CNN-HBF 快 600 倍左右。