A Learning Approach for Optimal Codebook Selection in Spatial Modulation Systems

Abstract

For spatial modulation (SM) systems that utilize multiple transmit antennas/patterns with a single radio front-end, we propose a learning approach to predict the average symbol error rate (SER) conditioned on the instantaneous channel state. We show that the predicted SER can be used to lower the average SER over Rayleigh fading channels by selecting the optimal codebook in each transmission instance. Further by exploiting that feedforward artificial neural networks (ANNs) trained with a mean squared error (MSE) criterion estimate the conditional a posteriori probabilities, we maximize the expected rate for each transmission instance and thereby improve the link spectral efficiency.