Sparse joint representation for massive MIMO satellite uplink and downlink based on dictionary learning

Abstract

We address the challenge of jointly representing uplink (UL) and downlink (DL) channels for a massive multiple-input multiple-output satellite system. We employ dictionary learning for sparse representation with the goal of minimizing the number of UL/DL pilots and improving accuracy. Additionally, by considering the angular reciprocity, a common dictionary support can be established to enhance the performance. However, what type of dictionary model is suited for UL/DL channel representation remains an unknown field. Previous research has utilized predefined dictionaries, such as DFT or ODFT bases, which are unable to adapt to dynamic scenarios. Training dictionaries have demonstrated the potential to significantly improve accuracy; however, a lack of analysis regarding dictionary constraints exists. To address this issue, we analyze the conditional constraints of the dictionary for joint UL/DL channel representation, aiming to quantify the maximum boundary while proposing a constrained dictionary learning algorithm with singular value decomposition to obtain an effective representation and conduct an adaptability analysis in dynamic satellite communication scenarios.