论文标题
DeepBeam：MMWave网络中无协调梁管理的深层学习
DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks
论文作者
论文摘要
高方向毫米波（MMWave）收音机需要执行束管理以建立和维护可靠的链接。为此，现有解决方案主要依赖于发射机（TX）和接收器（RX）之间的明确协调，这大大降低了可用于通信的通话时间，并使网络协议设计更加复杂。本文通过呈现Deepbeam来推进艺术的状态，Deepbeam是一个不需要TX的试验序列的框架，也不需要从RX扫描或同步的任何光束。这是通过（i）梁的到达角度（aOA）和（ii）发射器通过波形级别深度学习对TX向其他接收器之间持续的传输进行的深度学习来实现的。通过这种方式，RX可以将信噪比（SNR）级别与光束相关联，而无需与TX显式配位。这是可能的，因为不同的光束模式会引入波形的不同障碍，随后可以通过卷积神经网络（CNN）学习。我们进行了广泛的实验数据收集活动，在60.48 GHz的（i）4个分阶段的阵列天线，（ii）2个代码簿中，我们收集超过4 TB的MMWave波形，其中包含24张一维光束和12个二维光束； （iii）3个接收器获得； （iv）3种不同的AOA； （v）多个TX和RX位置。此外，我们使用两个自定义设计的MMWave软件定义的无线电收集波形数据，其中具有58 GHz的完全数字波束形成架构。结果表明，DeepBeam（i）分别使用5束，12梁和24束梁代码簿实现了高达96％，84％和77％的精度； （ii）相对于5G NR初始光束扫描，在默认配置和12梁码本中最多将延迟减少到7倍。波形数据集和完整的DeepBeam代码存储库可公开可用。
Highly directional millimeter wave (mmWave) radios need to perform beam management to establish and maintain reliable links. To do so, existing solutions mostly rely on explicit coordination between the transmitter (TX) and the receiver (RX), which significantly reduces the airtime available for communication and further complicates the network protocol design. This paper advances the state of the art by presenting DeepBeam, a framework for beam management that does not require pilot sequences from the TX, nor any beam sweeping or synchronization from the RX. This is achieved by inferring (i) the Angle of Arrival (AoA) of the beam and (ii) the actual beam being used by the transmitter through waveform-level deep learning on ongoing transmissions between the TX to other receivers. In this way, the RX can associate Signal-to-Noise-Ratio (SNR) levels to beams without explicit coordination with the TX. This is possible because different beam patterns introduce different impairments to the waveform, which can be subsequently learned by a convolutional neural network (CNN). We conduct an extensive experimental data collection campaign where we collect more than 4 TB of mmWave waveforms with (i) 4 phased array antennas at 60.48 GHz, (ii) 2 codebooks containing 24 one-dimensional beams and 12 two-dimensional beams; (iii) 3 receiver gains; (iv) 3 different AoAs; (v) multiple TX and RX locations. Moreover, we collect waveform data with two custom-designed mmWave software-defined radios with fully-digital beamforming architectures at 58 GHz. Results show that DeepBeam (i) achieves accuracy of up to 96%, 84% and 77% with a 5-beam, 12-beam and 24-beam codebook, respectively; (ii) reduces latency by up to 7x with respect to the 5G NR initial beam sweep in a default configuration and with a 12-beam codebook. The waveform dataset and the full DeepBeam code repository are publicly available.
