Deep-LaRGE: Higher-Order SVD and Deep Learning for Model Order Selection in MIMO OFDM Systems

Abstract:
Despite the large volume of research on the field of model order selection, finding the correct rank number can still be challenging. Propagation environments with many scatters may generate channel multipath components (MPCs) which are closely spaced. This clustering of MPCs in addition to noise makes the model order selection task difficult for wireless channels which can directly impact user equipment (UE) throughput, e.g., wrong lower rank approximation for channel estimation via Unitary ESPRIT. In this paper, we exploit the multidimensional characteristics of MIMO orthogonal frequency division multiplexing (OFDM) systems and propose an artificial intelligence and machine learning (AI/ML) method capable of determining the number of MPCs with a higher accuracy than state of the art methods in almost coherent scenarios. Moreover, our results show that our proposed AI/ML method has an enhanced reliability as the threshold for signal singular value selection is 80 %.