DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks
DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks
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DOI：
10.1145/3466772.3467035
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发表时间：
2020-12
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通讯作者：
Michele Polese;Francesco Restuccia;T. Melodia
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作者：
Michele Polese;Francesco Restuccia;T. Melodia
高度定向的毫米波（mmWave）无线电需要执行波束管理以建立和维护可靠的链路。为了实现这个目标，现有的解决方案主要依赖于发射机（TX）和接收机（RX）之间的显式协调，这显著减少了可用于通信的通话时间，并且进一步使网络协议设计复杂化。本文通过介绍DeepBeam推进了现有技术，DeepBeam是一种用于波束管理的框架，不需要来自TX的导频序列，也不需要来自RX的任何波束扫描或同步。这是通过对TX到其他接收器之间正在进行的传输进行波形级深度学习来推断（i）波束的到达角（AoA）和（ii）发射器正在使用的实际波束来实现的。以这种方式，RX可以将信噪比（SNR）水平与波束相关联，而无需与TX进行显式协调。这是可能的，因为不同的波束图案对波形引入了不同的“损伤”，这可以随后通过卷积神经网络（CNN）来学习。为了证明DeepBeam的通用性，我们进行了广泛的实验数据收集活动，我们收集了超过4 TB的毫米波波形，其中包括：（i）60.48 GHz的4个相控阵天线;（ii）包含24个一维波束和12个二维波束的2个码本;（iii）3个接收器增益;（iv）3个不同的AoA;（v）多个TX和RX位置。此外，我们还使用两款定制设计的毫米波软件定义无线电（具有58 GHz的全数字波束成形架构）收集波形数据。我们还在FPGA中实现了我们的学习模型，以评估延迟性能。结果表明，DeepBeam（i）在5波束、12波束和24波束码本下分别实现高达96%、84%和77%的准确度;（ii）相对于默认配置和12波束码本下的5G NR初始波束扫描，延迟降低高达7倍。波形数据集和完整的DeepBeam代码库是公开的。
Highly directional millimeter wave (mmWave) radios need to perform beam management to establish and maintain reliable links. To achieve this objective, 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). To demonstrate the generality of DeepBeam, 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. We also implement our learning models in FPGA to evaluate latency performance. 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.