Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are capable of improving spectral efficiency by employing far more antennas than conventional massive MIMO at the base station (BS). However, beam training in multiuser XL-MIMO systems is challenging. To tackle these issues, we conceive a three-phase graph neural network (GNN)-based beam training scheme for multiuser XL-MIMO systems. In the first phase, only far-field wide beams have to be tested for each user and the GNN is utilized to map the beamforming gain information of the far-field wide beams to the optimal near-field beam for each user. In addition, the proposed GNN-based scheme can exploit the position-correlation between adjacent users for further improvement of the accuracy of beam training. In the second phase, a beam allocation scheme based on the probability vectors produced at the outputs of GNNs is proposed to address the above beam-direction conflicts between users. In the third phase, the hybrid TBF is designed for further reducing the inter-user interference. Our simulation results show that the proposed scheme improves the beam training performance of the benchmarks. Moreover, the performance of the proposed beam training scheme approaches that of an exhaustive search, despite requiring only about 7% of the pilot overhead.

翻译：极大规模多输入多输出（XL-MIMO）系统通过在基站部署远超传统大规模MIMO的天线数量，能够有效提升频谱效率。然而，多用户XL-MIMO系统中的波束训练面临严峻挑战。为解决这些问题，本文提出了一种基于图神经网络（GNN）的三阶段波束训练方案。第一阶段仅需为每个用户测试远场宽波束，并利用GNN将远场宽波束的波束赋形增益信息映射至每个用户的最优近场波束。此外，所提出的GNN方案可通过利用相邻用户间的空间位置相关性进一步提升波束训练精度。第二阶段基于GNN输出的概率向量提出波束分配方案，以解决用户间的波束方向冲突问题。第三阶段设计混合波束赋形技术以进一步降低用户间干扰。仿真结果表明，所提方案提升了基准方案的波束训练性能。同时，尽管仅需约7%的导频开销，该方案的波束训练性能仍能逼近穷举搜索的性能。