CNN-based Analog CSI Feedback in FDD MIMO-OFDM Systems

TL;DR

This paper introduces AnalogDeepCMC, a CNN-based analog feedback scheme for FDD MIMO-OFDM systems that directly maps downlink CSI to uplink inputs, improving spectral efficiency and reducing latency without quantization.

Contribution

The paper presents a novel CNN-based analog CSI feedback method that outperforms digital schemes and simplifies the feedback process in FDD massive MIMO systems.

Findings

Outperforms existing digital CSI feedback schemes in downlink rate.

Eliminates need for quantization, coding, and modulation.

Provides low-latency feedback suitable for rapidly changing channels.

Abstract

Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to better utilize the available spatial diversity and multiplexing gains. However, in a frequency division duplex (FDD) massive MIMO system, CSI feedback overhead degrades the overall spectral efficiency. Convolutional neural network (CNN)-based CSI feedback compression schemes has received a lot of attention recently due to significant improvements in compression efficiency; however, they still require reliable feedback links to convey the compressed CSI information to the BS. Instead, we propose here a CNN-based analog feedback scheme, called AnalogDeepCMC, which directly maps the downlink CSI to uplink channel input. Corresponding noisy channel outputs are used by another CNN to reconstruct the DL channel estimate. Not only the proposed outperforms existing digital CSI feedback schemes in terms of the achievable downlink rate, but also simplifies the operation as it does not require explicit quantization, coding and modulation, and provides a low-latency alternative particularly in rapidly changing MIMO channels, where the CSI needs to be estimated and fed back periodically.