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. Index Terms-IEEE 802.11ac, link adaptation, machine learning, multiuser multiple input multiple output (MIMO)-orthogonal frequency division multiplexing (OFDM).
In another study REF , the authors illustrate a data-driven approach to link adaptation, where a machine learning classifier is employed to determine the optimal modulation and coding scheme for multiple input-multiple output (MIMO) systems.
Learning-Based Adaptive Transmission for Limited Feedback Multiuser MIMO-OFDM
IEEE Transactions on Wireless Communications
IEEE Transactions on Wireless Communications
Mathematics Computer Science