In integrated ground-air-space (IGAS) wireless networks, numerous services require sensing knowledge including location, angle, distance information, etc., which usually can be acquired during the beam training stage. On the other hand, IGAS networks employ large-scale antenna arrays to mitigate obstacle occlusion and path loss. However, large-scale arrays generate pencil-shaped beams, which necessitate a higher number of training beams to cover the desired space. These factors motivate our investigation into the IGAS beam training problem to achieve effective sensing services. To address the high complexity and low identification accuracy of existing beam training techniques, we propose an efficient hashing multi-arm beam (HMB) training scheme. Specifically, we first construct an IGAS single-beam training codebook for the uniform planar arrays. Then, the hash functions are chosen independently to construct the multi-arm beam training codebooks for each AP. All APs traverse the predefined multi-arm beam training codeword simultaneously and the multi-AP superimposed signals at the user are recorded. Finally, the soft decision and voting methods are applied to obtain the correctly aligned beams only based on the signal powers. In addition, we logically prove that the traversal complexity is at the logarithmic level. Simulation results show that our proposed IGAS HMB training method can achieve 96.4% identification accuracy of the exhaustive beam training method and greatly reduce the training overhead.

翻译：在天地一体化无线网络中，众多业务需要获取包括位置、角度、距离等在内的感知信息，这些信息通常可在波束训练阶段获得。另一方面，天地一体化网络采用大规模天线阵列以克服障碍物遮挡和路径损耗。然而，大规模阵列会产生笔状波束，这需要更多的训练波束以覆盖目标空间。这些因素促使我们对天地一体化网络的波束训练问题进行研究，以实现有效的感知服务。针对现有波束训练技术复杂度高、识别准确率低的问题，我们提出了一种高效的哈希多臂波束训练方案。具体而言，我们首先为均匀平面阵列构建了一个天地一体化单波束训练码本。随后，独立选择哈希函数为每个接入点构建多臂波束训练码本。所有接入点同时遍历预定义的多臂波束训练码字，并记录用户在接收端叠加的多接入点信号。最后，应用软判决与投票方法，仅基于信号功率即可获得正确对准的波束。此外，我们从逻辑上证明了该方案的遍历复杂度处于对数级别。仿真结果表明，我们所提出的天地一体化哈希多臂波束训练方法能够达到穷举波束训练方法96.4%的识别准确率，并大幅降低了训练开销。