LEMO: Learn to Equalize for MIMO-OFDM Systems with Low-Resolution ADCs
This addresses a hardware efficiency problem for wireless communication systems, but it is incremental as it builds on existing deep learning approaches for equalization.
The paper tackles equalization in massive MIMO-OFDM systems with low-resolution ADCs, which reduces hardware complexity but blinds channel information, by proposing a deep learning framework with a new activation function and unsupervised loss, achieving superior performance over existing methods, especially when channel statistics are unclear.
This paper develops a new deep neural network optimized equalization framework for massive multiple input multiple output orthogonal frequency division multiplexing (MIMOOFDM) systems that employ low-resolution analog-to-digital converters (ADCs) at the base station (BS). The use of lowresolution ADCs could largely reduce hardware complexity and circuit power consumption, however, it makes the channel station information almost blind to the BS, hence causing difficulty in solving the equalization problem. In this paper, we consider a supervised learning architecture, where the goal is to learn a representative function that can predict the targets (constellation points) from the inputs (outputs of the low-resolution ADCs) based on the labeled training data (pilot signals). Especially, our main contributions are two-fold: 1) First, we design a new activation function, whose outputs are close to the constellation points when the parameters are finally optimized, to help us fully exploit the stochastic gradient descent method for the discrete optimization problem. 2) Second, an unsupervised loss is designed and then added to the optimization objective, aiming to enhance the representation ability (so-called generalization). Lastly, various experimental results confirm the superiority of the proposed equalizer over some existing ones, particularly when the statistics of the channel state information are unclear.