Deep Learning based Channel Extrapolation for Large-Scale Antenna Systems: Opportunities, Challenges and Solutions

Abstract

With the depletion of spectrum, wireless communication systems turn to exploit large antenna arrays to achieve the degree of freedom in space domain, such as millimeter wave massive multi-input multioutput (MIMO), reconfigurable intelligent surface assisted communications and cell-free massive MIMO.In these systems, how to acquire accurate channel state information (CSI) is difficult and becomes a bottleneck of the communication links. In this article, we introduce the concept of channel extrapolation that relies on a small portion of channel parameters to infer the remaining channel parameters. Since the substance of channel extrapolation is a mapping from one parameter subspace to another, we can resort to deep learning (DL), a powerful learning architecture, to approximate such mapping function. Specifically, we first analyze the requirements, conditions and challenges for channel extrapolation.Then, we present three typical extrapolations over the antenna dimension, the frequency dimension, and the physical terminal, respectively. We also illustrate their respective principles, design challenges and DL strategies. It will be seen that channel extrapolation could greatly reduce the transmission overhead and subsequently enhance the performance gains compared with the traditional strategies. In S. Zhang and Y. Liu are with the

Deep Learning Based RIS Channel Extrapolation with Element-grouping

“…Fortunately, for a given RIS structure, there exists deterministic mapping between the partial and the full channel information. As mentioned in [8], we can exploit the universal approximation capability of neural networks (NNs), adopt deep leaning (DL) to characterize the above mapping, and extrapolate the full RIS channel information from the partial one. In fact, DL has been utilized for channel estimation over RIS-assisted systems.…”