Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive
MIMO-OFDM Systems with Implicit CSI
- URL: http://arxiv.org/abs/2201.06778v1
- Date: Tue, 18 Jan 2022 07:21:00 GMT
- Title: Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive
MIMO-OFDM Systems with Implicit CSI
- Authors: Zhen Gao, Minghui Wu, Chun Hu, Feifei Gao, Guanghui Wen, Dezhi Zheng,
Jun Zhang
- Abstract summary: We propose a data-driven deep learning-based unified hybrid beamforming framework for time division duplex and frequency division duplex systems.
For TDD systems, the proposed DL-based approach jointly models the uplink pilot combining and downlink hybrid beamforming modules as an E2E neural network.
While for FDD systems, we jointly model the downlink pilot transmission, uplink CSI feedback, and downlink hybrid beamforming modules as an E2E neural network.
- Score: 29.11998008894847
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In an aerial hybrid massive multiple-input multiple-output (MIMO) and
orthogonal frequency division multiplexing (OFDM) system, how to design a
spectral-efficient broadband multi-user hybrid beamforming with a limited pilot
and feedback overhead is challenging. To this end, by modeling the key
transmission modules as an end-to-end (E2E) neural network, this paper proposes
a data-driven deep learning (DL)-based unified hybrid beamforming framework for
both the time division duplex (TDD) and frequency division duplex (FDD) systems
with implicit channel state information (CSI). For TDD systems, the proposed
DL-based approach jointly models the uplink pilot combining and downlink hybrid
beamforming modules as an E2E neural network. While for FDD systems, we jointly
model the downlink pilot transmission, uplink CSI feedback, and downlink hybrid
beamforming modules as an E2E neural network. Different from conventional
approaches separately processing different modules, the proposed solution
simultaneously optimizes all modules with the sum rate as the optimization
object. Therefore, by perceiving the inherent property of air-to-ground massive
MIMO-OFDM channel samples, the DL-based E2E neural network can establish the
mapping function from the channel to the beamformer, so that the explicit
channel reconstruction can be avoided with reduced pilot and feedback overhead.
Besides, practical low-resolution phase shifters (PSs) introduce the
quantization constraint, leading to the intractable gradient backpropagation
when training the neural network. To mitigate the performance loss caused by
the phase quantization error, we adopt the transfer learning strategy to
further fine-tune the E2E neural network based on a pre-trained network that
assumes the ideal infinite-resolution PSs. Numerical results show that our
DL-based schemes have considerable advantages over state-of-the-art schemes.
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