Sparse joint representation for massive MIMO satellite uplink and downlink based on dictionary learning - Qing-Yang Guan; Shuang Wu; Zhuang Miao (ETRI Journal)
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 mini-mizing the number of UL/DL pilots and improving accuracy. Additionally, byconsidering the angular reciprocity, a common dictionary support can beestablished to enhance the performance. However, what type of dictionarymodel is suited for UL/DL channel representation remains an unknown field.Previous research has utilized predefined dictionaries, such as DFT or ODFTbases, which are unable to adapt to dynamic scenarios. Training dictionarieshave demonstrated the potential to significantly improve accuracy; however, alack of analysis regarding dictionary constraints exists. To address this issue,we analyze the conditional constraints of the dictionary for joint UL/DLchannel representation, aiming to quantify the maximum boundary while pro-posing a constrained dictionary learning algorithm with singular value decom-position to obtain an effective representation and conduct an adaptabilityanalysis in dynamic satellite communication scenarios.