基于连续时间预测的低开销移动毫米波信道波束对齐
Low Overhead Beam Alignment for Mobile Millimeter Channel Based on Continuous-Time Prediction
AI总结：
针对毫米波移动通信中波束训练开销大的问题，提出自适应在线波束对齐（AOBA）方案，结合少量候选波束跟踪与全波束扫描切换，并利用ODE-LSTM预测最优波束，实现低开销高精度对齐。
摘要：
在毫米波（mmWave）通信中，基于波束成形的定向传输对于补偿高路径损耗至关重要。为了维持期望的定向传输增益，通常考虑波束扫描，即发射机在所有可用波束方向上发送导频信号以找到最优波束。或者，使用部分波束的波束跟踪可以通过统计算法如统计模型和卡尔曼滤波器（KF）来节省波束训练开销。不幸的是，现有的波束跟踪解决方案局限于固定的波束变化模式。在这项工作中，我们提出了一种自适应在线波束对齐（AOBA）方案，旨在减少训练开销并针对任意移动轨迹实现精确的波束对齐。所提出的AOBA周期性地使用少量但精心选择的候选波束进行波束跟踪，并根据给定的切换规则切换到使用所有可用波束的波束扫描。在没有导频信号的间隔期间，借助最近提出的常微分方程（ODE）-长短期记忆（LSTM）模型，预测任意时刻的最优波束。进行了大量仿真，以评估所提出的AOBA与几种现有波束对齐方案相比的性能。
英文摘要：
In millimeter-wave (mmWave) communications, directional transmission based on beamforming is important to compensate for high pathloss. To maintain the desired direction transmission gain, beam scanning that involves the transmitter sending the pilot signal over all available beam directions to find the optimal beam is often considered. Alternatively, beam tracking using partial beams can save the beam training overhead through algorithms such as statistical analysis models and kalman filter (KF). Unfortunately, existing beam tracking solutions are limited to a fixed beam variation pattern. In this work, we propose an adaptive online beam alignment (AOBA) scheme, which aims to reduce training overhead and achieve accurate beam alignment for any movement profile. The proposed AOBA periodically performs beam tracking using a small amount but carefully selected candidate beams and switches to beam scanning using all available beams based on a given switching rule. During the interval without the pilot signal, the optimal beam at an arbitrary time instant is predicted with the aid of the recently proposed ordinary differential equation (ODE)-long short-term memory (LSTM) model. Extensive simulations are conducted to evaluate the performance of the proposed AOBA in comparison with several existing beam alignment schemes.
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