Sense-Then-Train: A Novel Beam Training Design for Near-Field MIMO Systems
CoRR(2024)
摘要
A novel sense-then-train (STT) scheme is proposed for beam training in
near-field multiple-input multiple-output (MIMO) systems. Compared to
conventional codebook-based schemes, the proposed STT scheme is capable of not
only addressing the complex spherical-wave propagation but also effectively
exploiting the additional degrees-of-freedoms (DoFs). The STT scheme is
tailored for both single-beam and multi-beam cases. 1) For the single-beam
case, the STT scheme first utilizes a sensing phase to estimate a
low-dimensional representation of the near-field MIMO channel in the wavenumber
domain. Then, in the subsequent training phase, an online learning algorithm is
proposed to obtain the optimal beam pair without predefined codebooks or
training datasets. 2) For the multi-beam case, based on the single-beam STT, a
Gram-Schmidt method is further utilized to guarantee the orthogonality between
beams in the training phase. Numerical results unveil that 1) the proposed STT
scheme can significantly enhance the beam training performance in the near
field compared to the conventional far-field codebook-based schemes, and 2) the
proposed STT scheme can perform fast and low-complexity beam training, while
achieving a near-optimal performance without full channel state information in
both cases.
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