DEFORM：一种实用、通用的深波束形成系统
DEFORM: A Practical, Universal Deep Beamforming System
摘要：
我们介绍，设计和评估一组通用的接收机波束形成技术。我们的方法和系统DEFORM（一种基于深度学习（DL）的RX波束成形）实现了多天线RF接收器的显著增益，同时对所发射的信号特征（例如，调制或带宽）。众所周知，组合来自多个天线的相干RF信号导致与接收元件的数量成比例的波束成形增益。然而，在实践中，这种方法严重依赖于显式信道估计技术，这些技术是链路特定的，并且需要将显著的通信开销发送到接收机。DEFORM通过利用卷积神经网络来估计信道特性（特别是与天线元件的相对相位）来解决这一挑战。它专门设计用于解决无线信号复杂样本的独特功能，例如模糊的$2π$相位不连续性和链路位出错率的高灵敏度。信道预测随后用于最大比合并算法中以实现接收信号的最优组合。在固定的基本RF设置上进行训练时，我们证明了DEFORM DL模型是通用的，在广泛的实验中，两个天线接收机的SNR增益高达3 dB，演示了调制，带宽和信道的各种设置。DEFORM的通用性通过LoRa（Chirp扩频调制）和ZigBee信号的联合波束形成中继来证明，相对于传统的放大和转发，实现了丢包率/传输率的显著改善（LoRa PLR降低了23倍，ZigBee PDR增加了8倍）。
英文摘要：
We introduce, design, and evaluate a set of universal receiver beamforming techniques. Our approach and system DEFORM, a Deep Learning (DL) based RX beamforming achieves significant gain for multi antenna RF receivers while being agnostic to the transmitted signal features (e.g., modulation or bandwidth). It is well known that combining coherent RF signals from multiple antennas results in a beamforming gain proportional to the number of receiving elements. However in practice, this approach heavily relies on explicit channel estimation techniques, which are link specific and require significant communication overhead to be transmitted to the receiver. DEFORM addresses this challenge by leveraging Convolutional Neural Network to estimate the channel characteristics in particular the relative phase to antenna elements. It is specifically designed to address the unique features of wireless signals complex samples, such as the ambiguous $2π$ phase discontinuity and the high sensitivity of the link Bit Error Rate. The channel prediction is subsequently used in the Maximum Ratio Combining algorithm to achieve an optimal combination of the received signals. While being trained on a fixed, basic RF settings, we show that DEFORM DL model is universal, achieving up to 3 dB of SNR gain for a two antenna receiver in extensive experiments demonstrating various settings of modulations, bandwidths, and channels. The universality of DEFORM is demonstrated through joint beamforming relaying of LoRa (Chirp Spread Spectrum modulation) and ZigBee signals, achieving significant improvements to Packet Loss/Delivery Rates relatively to conventional Amplify and Forward (LoRa PLR reduced by 23 times and ZigBee PDR increased by 8 times).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
