Unsupervised Learning of Adaptive Codebooks for Deep Feedback Encoding in FDD Systems

TL;DR

This paper introduces an unsupervised deep learning approach to create adaptive codebooks for efficient feedback encoding in FDD systems, leveraging UL data to eliminate the need for training at mobile terminals.

Contribution

It presents a novel joint adaptive codebook construction and feedback generation scheme using deep learning, exploiting UL-DL CSI equivalence for the first time.

Findings

Demonstrates high performance of the proposed method in simulations.

Enables offloading feedback encoding to mobile terminals without training.

Reduces complexity and overhead in FDD system feedback mechanisms.

Abstract

In this work, we propose a joint adaptive codebook construction and feedback generation scheme in frequency division duplex (FDD) systems. Both unsupervised and supervised deep learning techniques are used for this purpose. Based on a recently discovered equivalence of uplink (UL) and downlink (DL) channel state information (CSI) in terms of neural network learning, the codebook and associated deep encoder for feedback signaling is based on UL data only. Subsequently, the feedback encoder can be offloaded to the mobile terminals (MTs) to generate channel feedback there as efficiently as possible, without any training effort at the terminals or corresponding transfer of training and codebook data. Numerical simulations demonstrate the promising performance of the proposed method.