Deep Learning-based Massive MIMO CSI Acquisition for 5G Evolution and 6G

TL;DR

This paper proposes deep learning schemes for CSI acquisition in 5G and 6G networks, demonstrating significant spectrum efficiency gains and feasibility for real-world deployment, especially with end-to-end AI designs for future 6G systems.

Contribution

It introduces two AI-based CSI acquisition schemes tailored for 5G NR, showing their effectiveness and potential for 6G air interface design, with extensive evaluations on modeled and real channels.

Findings

25% spectrum efficiency gain with DL-based receiver at moderate feedback overhead

Additional 6-26% spectrum efficiency improvement with end-to-end DL-based CSI

Feasibility of deploying DL-based CSI schemes in current 5G networks

Abstract

Recently, inspired by successful applications in many fields, deep learning (DL) technologies for CSI acquisition have received considerable research interest from both academia and industry. Considering the practical feedback mechanism of 5th generation (5G) New radio (NR) networks, we propose two implementation schemes for artificial intelligence for CSI (AI4CSI), the DL-based receiver and end-to-end design, respectively. The proposed AI4CSI schemes were evaluated in 5G NR networks in terms of spectrum efficiency (SE), feedback overhead, and computational complexity, and compared with legacy schemes. To demonstrate whether these schemes can be used in real-life scenarios, both the modeled-based channel data and practically measured channels were used in our investigations. When DL-based CSI acquisition is applied to the receiver only, which has little air interface impact, it provides approximately 25\% SE gain at a moderate feedback overhead level. It is feasible to deploy it in current 5G networks during 5G evolutions. For the end-to-end DL-based CSI enhancements, the evaluations also demonstrated their additional performance gain on SE, which is 6% -- 26% compared with DL-based receivers and 33% -- 58% compared with legacy CSI schemes. Considering its large impact on air-interface design, it will be a candidate technology for 6th generation (6G) networks, in which an air interface designed by artificial intelligence can be used.