Hashing Beam Training for Integrated Ground-Air-Space Wireless Networks
arxiv(2024)
摘要
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
of the exhaustive beam training method and greatly reduce the training
overhead.
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