A learning approach in link adaptation for MIMO-OFDM systems

We propose a neural network (NN)-based adaptive modulation and coding (AMC) for link adaptation in MIMO-OFDM systems. The AMC optimizes the best modulation and coding scheme (MCS) under a packet error rate (PER) constraint. In our approach, a NN with a multilayer perceptron (MLP) structure is applied for the AMC and its performance is compared with the k-nearest neighbor (k-NN) algorithm under the frequency-flat (1-tap) and frequency-selective (4-tap) wireless channel conditions. The simulation results show that the NN classifier outperforms the k-NN algorithm, especially in terms of the PER, due to the fact that the MLP guarantees a MCS with a lower data rate by way of the selection of a class label with a lower index number. It has a slightly worse spectral efficiency performance compared to the k-NN. Thus, the MLP approach provides higher communication robustness over the k-NN. It can be concluded from the results that the selection of the AMC classifier depends on a trade-off between the PER and the spectral efficiency, relying on the user's requirements.