论文标题
全双工毫米波系统的基于学习的混合波束形成设计
Learning Based Hybrid Beamforming Design for Full-Duplex Millimeter Wave Systems
论文作者
论文摘要
相对于半双链体的毫米波（MMWAVE）通信具有提高频谱效率的潜力。但是，来自FD和MMWave信号固有的高路径的残留自我解干（SI）可能会降低系统性能。同时，混合波束形成（HBF）是一种有效的技术，可增强通道增益并减轻对合理复杂性的干扰。但是，FD MMWave系统的常规HBF方法基于优化过程，这些过程过于复杂或非常依赖于通道状态信息（CSI）的质量。我们提出了两个学习方案，以设计用于FD MMWave系统的HBF，即基于极端学习机器的HBF（ELM-HBF）和基于卷积神经网络的HBF（CNN-HBF）。具体而言，我们首先提出了一种基于乘数（ADMM）算法的交替方向方法，以实现SI取消波束形成，然后使用基于大型化最小化（MM）算法进行关节传输和接收HBF优化。为了训练学习网络，我们将嘈杂的通道模拟为输入，并选择由建议的算法计算的杂种光束器作为目标。结果表明，与正交匹配追踪（OMP）算法相比，这两种基于学习的方案均可提供更强大的HBF性能，并至少提高光谱效率22.1％。此外，基于学习的方案的在线预测时间几乎是OMP方案的20倍。此外，ELM-HBF的训练时间比CNN-HBF的训练时间约为64倍，具有64次传输和接收天线。
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.
