CAnet: Uplink-aided Downlink Channel Acquisition in FDD Massive MIMO using Deep Learning

TL;DR

This work considers the entire downlink CSI acquisition process, including the downlink pilot design, channel estimation, and feedback, and proposes an adaptive pilot design module by exploiting the correlation in magnitude among bidirectional channels in the angular domain to improve channel estimation.

Abstract

In frequency-division duplexing systems, the downlink channel state information (CSI) acquisition scheme leads to high training and feedback overheads. In this paper, we propose an uplink-aided downlink channel acquisition framework using deep learning to reduce these overheads. Unlike most existing works that focus only on channel estimation or feedback modules, to the best of our knowledge, this is the first study that considers the entire downlink CSI acquisition process, including downlink pilot design, channel estimation, and feedback. First, we propose an adaptive pilot design module by exploiting the correlation in magnitude among bidirectional channels in the angular domain to improve channel estimation. Next, to avoid the bit allocation problem during the feedback module, we concatenate the complex channel and embed the uplink channel magnitude to the channel reconstruction at the base station. Lastly, we combine the above two modules and compare two popular downlink channel acquisition frameworks. The former framework estimates and feeds back the channel at the user equipment subsequently. The user equipment in the latter one directly feeds back the received pilot signals to the base station. Our results reveal that, with the help of uplink, directly feeding back the pilot signals can save approximately 20% of feedback bits, which provides a guideline for future research.

Figures (14)

• Figure 1: Diagram of downlink CSI acquisition in FDD massive MIMO systems. It mainly consists of three steps: pilot transmission, channel estimation, and channel feedback.
• Figure 2: Illustration of signal propagation in a typical massive MIMO system.
• Figure 3: Illustration of the DL-based CSI feedback framework, where the encoder at the UE compresses downlink CSI and the decoder at the BS reconstructs downlink CSI.
• Figure 4: Illustration of the DL-based joint pilot design and channel estimation framework. With reference to 8861085, two FC layers at the BS represent the real and imaginary parts of the pilot signals.
• Figure 5: Illustration of the DL-based uplink-aided downlink pilot design and channel estimation framework, UpAid-PEnet.