FIELD

The present disclosure relates generally to accurate channel state information (CSI) feedback, and more particularly, to an apparatus and method for using a multi-layer perceptron (MLP) algorithm to map channel features to a rank indicator (RI) and/or a channel quality indicator (CQI) for CSI feedback.

BACKGROUND

In order to efficiently utilize available resources and maximize throughput while minimizing a block error rate (BLER), a base station, e.g., a gNodeB (gNB), needs accurate CSI feedback. Conventionally, a table is used to map a channel feature to a candidate CQI and a candidate RI. The table includes a set of thresholds for the channel feature for different CQIs and RIs, and is designed for only one channel, usually an additive white Gaussian noise (AWGN) channel. Therefore, the table must be updated frequently according to the different channel conditions.

While the UE is being used in other channels, the table is being updated to further improve the CQI and RI decisions. Processes for updating the table, however, are often slow and inaccurate, which may result in a significant loss in performance.

Additionally, using multiple tables, i.e., one for each channel, cannot feasibly be done in practice as there are many different channel types.

Accordingly, a need exists for an improved method and apparatus for accurate CSI feedback.

SUMMARY

The disclosure is made to address at least the problems and/or disadvantages described above and to provide at least the advantages described below.

An aspect of the disclosure is to improve the quality of CSI feedback; specifically, RI and/or CQI.

Another aspect of the disclosure is to provide a system and method that utilize an MLP network to map channel features to the RI and/or the CQI. The MLP network can estimate RI, CQI, or both RI and CQI simultaneously.

Another aspect of the disclosure is to utilize reinforcement learning (RL) to train the MLP network.

Another aspect of the disclosure is to provide an online adaptation (OA) algorithm to refine MLP network decisions.

In accordance with an aspect of the disclosure a method is provided for feeding back CSI. The method includes estimating a channel in the communication network for a signal; extracting at least one channel feature related to the estimated channel; determining rank indicator (RI) and channel quality indicator (CQI) pairs; inputting, to a multi-layer perceptron (MLP) network, the extracted at least one channel feature and the RI and CQI pairs; receiving, for each of the RI and CQI pairs, an output of the MLP network, wherein the outputs of the MLP network indicate throughput or spectral efficiency for the electronic device; and selecting an RI and CQI pair of the RI and CQI pairs based on the received outputs.

In accordance with another aspect of the disclosure an apparatus is provided for feeding back CSI. The apparatus includes a transceiver; a multi-layer perceptron (MLP) network; and a processor configured to estimate a channel in the communication network for a signal, extract at least one channel feature related to the estimated channel, determine rank indicator (RI) and channel quality indicator (CQI) pairs, input, to the MLP network, the extracted at least one channel feature and the RI and CQI pairs, receive, for each of the RI and CQI pairs, an output of the MLP network, wherein the outputs of the MLP network indicate throughput or spectral efficiency for the electronic device, and select an RI and CQI pair of the RI and CQI pairs based on the received outputs.

