Due to large amounts of available spectrum at high frequencies, millimeter-wave (mmWave) technology has gained extensive research attention in 5G communications, whereas mmWave links suffer from severe free space attenuation. Codebook-based beamforming techniques with multiple antennas can effectively alleviate this challenge with low computational complexity and low hardware cost. However, small delay and high-speed communications with beamforming techniques require beam alignment with small overhead so as to establish the wireless link quickly. In this context, this paper proposes a deep learning-based low overhead analog beam selection scheme by virtue of the super-resolution technology. To be concrete, deep neural networks are employed to conduct beam quality estimation based on partial beam measurements. Our proposed scheme can cover all the directions of arriving signals with low overhead by utilizing codebooks with different beam widths. Furthermore, for the purpose of further reducing the overhead, we formulate the beam quality prediction model based on the past beam sweepings. With these beam quality estimation and prediction model, the beam that achieves large signal-to-noise-power-ratio (SNR) can be selected based on partial beam measurements. Simulation results show that the proposed scheme can accurately estimate beam qualities and give high probability of optimal beam selections with low overhead.

中文翻译：

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mmWave通信中基于深度学习的低开销波束选择

由于在高频下有大量可用频谱，毫米波（mmWave）技术已在5G通信中获得了广泛的研究关注，而mmWave链路遭受了严重的自由空间衰减。具有多个天线的基于码本的波束成形技术可以以较低的计算复杂度和较低的硬件成本有效缓解这一挑战。但是，使用波束成形技术的小延迟和高速通信需要开销较小的波束对齐，以便快速建立无线链路。在这种情况下，本文借助超分辨率技术提出了一种基于深度学习的低开销模拟波束选择方案。具体而言，深度神经网络被用于基于部分光束测量进行光束质量估计。通过利用具有不同波束宽度的码本，我们提出的方案可以以较低的开销覆盖到达信号的所有方向。此外，出于进一步减少开销的目的，我们基于过去的波束扫描来制定波束质量预测模型。利用这些光束质量估计和预测模型，可以基于部分光束测量结果来选择达到较大信噪比（SNR）的光束。仿真结果表明，该方案能够准确估计光束质量，并以较低的开销提供最佳的光束选择概率。利用这些光束质量估计和预测模型，可以基于部分光束测量结果来选择达到较大信噪比（SNR）的光束。仿真结果表明，该方案能够准确估计光束质量，并以较低的开销提供较高的最优光束选择概率。利用这些光束质量估计和预测模型，可以基于部分光束测量结果来选择达到较大信噪比（SNR）的光束。仿真结果表明，该方案能够准确估计光束质量，并以较低的开销提供最佳的光束选择概率。