CNN-based Analog CSI Feedback in FDD MIMO-OFDM Systems
This addresses the challenge of CSI feedback overhead for FDD massive MIMO systems, offering a low-latency alternative, though it is incremental as it builds on existing CNN-based compression methods.
The paper tackles the problem of high feedback overhead degrading spectral efficiency in FDD massive MIMO systems by proposing a CNN-based analog feedback scheme called AnalogDeepCMC, which directly maps downlink CSI to uplink channel input and outperforms existing digital schemes in downlink rate while simplifying operations.
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.