Algorithm

Engineering

Frequency-division multiplexing

MIMO

Overhead (engineering)

Real-time computing

User equipment

Operating system

Telecommunications

Computer science

Computer network

Multiplexing

Channel state information

Electronic engineering

Orthogonal frequency-division multiplexing

Artificial intelligence

Channel (broadcasting)

Wireless

Compressed sensing

Base station

Telecommunications link

IEEE Transactions on Vehicular Technology

To fully utilize multiplexing and array gains of massive multiple-input multiple-output (MIMO), the downlink channel state information (CSI) must be acquired at the base station (BS). In frequency division duplexing (FDD) massive MIMO systems, the downlink CSI is generally estimated at the user equipment (UE) and then fed back to the BS. The huge number of antennas at the BS leads to overwhelming feedback overhead. To address this issue, we propose a framework, named CS-ReNet. In this framework, the CSI is first compressed at the UE based on the compressive sensing (CS) technology and then reconstructed at the BS using a deep learning (DL)-based signal recovery solver, named ReNet. We analyze the CSI quality at the BS in terms of the normalized mean-squared error (NMSE) and cosine similarity. Simulation results demonstrate that the proposed method outperforms the existing CS-based and some DL-based methods.

Deep Learning and Compressive Sensing-Based CSI Feedback in FDD Massive MIMO Systems