Synchronization of transceiver chains is a major challenge in the practical realization of massive MIMO and especially distributed massive MIMO. While frequency synchronization is comparatively easy to achieve, estimating the carrier phase and sampling time offsets of individual transceivers is challenging. However, under the assumption of phase and time offsets that are constant over some duration and knowing the positions of several transmit and receive antennas, it is possible to estimate and compensate for these offsets even in scattering environments with multipath propagation components. The resulting phase and time calibration is a prerequisite for applying classical antenna array processing methods to massive MIMO arrays and for transferring machine learning models either between simulation and deployment or from one radio environment to another. Algorithms for phase and time offset estimation are presented and several investigations on large datasets generated by an over-the-air-synchronized channel sounder are carried out.
翻译:虽然频率同步比较容易实现,但估计承运人阶段和单个收发报机取样时间的抵消是具有挑战性的,但是,如果假设阶段和时间的抵消在一段时间内保持不变,并且了解若干传输和接收天线的位置,那么即使在带有多路传播组件的分散环境中,也有可能估计和补偿这些抵消。因此产生的阶段和时间校准是将古型天线阵列处理方法应用于大型IMMO阵列以及将机器学习模型在模拟和部署之间或从一个无线电环境转移到另一个无线电环境之间的一个先决条件。