Interleaved Training for Massive MIMO Downlink via Exploring Spatial Correlation
CoRR(2023)
摘要
Interleaved training has been studied for single-user and multi-user massive
MIMO downlink with either fully-digital or hybrid beamforming. However, the
impact of channel correlation on its average training overhead is rarely
addressed. In this paper, we explore the channel correlation to improve the
interleaved training for single-user massive MIMO downlink. For the beam-domain
interleaved training, we propose a modified scheme by optimizing the beam
training codebook. The basic antenna-domain interleaved training is also
improved by dynamically adjusting the training order of the base station (BS)
antennas during the training process based on the values of the already trained
channels. Exact and simplified approximate expressions of the average training
length are derived in closed-form for the basic and modified beam-domain
schemes and the basic antenna-domain scheme in correlated channels. For the
modified antenna-domain scheme, a deep neural network (DNN)-based approximation
is provided for fast performance evaluation. Analytical results and simulations
verify the accuracy of our derived training length expressions and explicitly
reveal the impact of system parameters on the average training length. In
addition, the modified beam/antenna-domain schemes are shown to have a shorter
average training length compared to the basic schemes.
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关键词
Massive MIMO,interleaved training,spatial correlation,conditional distribution,training overhead
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