Link-level performance abstraction for mimo receivers using artificial neural network

This paper presents a novel framework for link-level performance abstraction for multiple input multiple output (MIMO) receivers using a neural network model. The link-level performance abstraction is widely used to predict the receiver performances through a lookup table (LUT). As opposed to the classical LUT-based techniques, in the proposed neural network-based approach, the dataset for different channels and receivers is generated from the link-level simulations in order to train the neural network. The output performance values for MIMO wireless system are defined in terms of various features extracted from the input received codewords, derived primarily from the received post-detection signal to noise ratio (SNR) values. The redundant features are removed before training the neural network. Finally, the neural network model is incorporated into the link-level simulation chain, replacing the receiver. The performance of the proposed framework is then evaluated for different channel conditions. Experimental results provide a good close link-level approximation for different receivers subjected to various modulation and coding schemes. We show that the neural network-based link-level performance abstraction outperforms the classical LUT-based link-level abstraction technique with exponential mapping function under various modulation and coding schemes.