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Machine learning (ML) applications for wireless communications have gainedmomentum on the standardization discussions for 5G advanced and beyond. One ofthe biggest challenges for real world ML deployment is the need for labeledsignals and big measurement campaigns. To overcome those problems, we proposethe use of untrained neural networks (UNNs) for MIMO channelrecreation/estimation and low overhead reporting. The UNNs learn thepropagation environment by fitting a few channel measurements and we exploittheir learned prior to provide higher channel estimation gains. Moreover, wepresent a UNN for simultaneous channel recreation for multiple users, ormultiple user equipment (UE) positions, in which we have a trade-off betweenthe estimated channel gain and the number of parameters. Our results show thattransfer learning techniques are effective in accessing the learned prior onthe environment structure as they provide higher channel gain for neighbouringusers. Moreover, we indicate how the under-parameterization of UNNs can furtherenable low-overhead channel state information (CSI) reporting.

Transfer Learning Capabilities of Untrained Neural Networks for MIMO CSI Recreation