Learning-Based Adaptive Transmission for Limited Feedback Multiuser MIMO-OFDM

TL;DR

This paper introduces a machine learning-based link adaptation method for multiuser MIMO-OFDM systems that efficiently selects modulation, coding, and user scheduling using limited feedback, improving performance under practical constraints.

Contribution

It proposes a data-driven approach with a new interference approximation and a greedy algorithm for user and mode selection, tailored for IEEE 802.11ac standards.

Findings

Effective modulation and coding scheme selection based on SNR inputs.

Accurate interuser interference estimation with minimal feedback overhead.

Enhanced user scheduling and transmission adaptation in multiuser MIMO-OFDM.

Abstract

Performing link adaptation in a multiantenna and multiuser system is challenging because of the coupling between precoding, user selection, spatial mode selection and use of limited feedback about the channel. The problem is exacerbated by the difficulty of selecting the proper modulation and coding scheme when using orthogonal frequency division multiplexing (OFDM). This paper presents a data-driven approach to link adaptation for multiuser multiple input mulitple output (MIMO) OFDM systems. A machine learning classifier is used to select the modulation and coding scheme, taking as input the SNR values in the different subcarriers and spatial streams. A new approximation is developed to estimate the unknown interuser interference due to the use of limited feedback. This approximation allows to obtain SNR information at the transmitter with a minimum communication overhead. A greedy algorithm is used to perform spatial mode and user selection with affordable complexity, without resorting to an exhaustive search. The proposed adaptation is studied in the context of the IEEE 802.11ac standard, and is shown to schedule users and adjust the transmission parameters to the channel conditions as well as to the rate of the feedback channel.