  We study over-the-air model aggregation in federated edge learning (FEEL)
systems, where channel state information at the transmitters (CSIT) is assumed
to be unavailable. We leverage the reconfigurable intelligent surface (RIS)
technology to align the cascaded channel coefficients for CSIT-free model
aggregation. To this end, we jointly optimize the RIS and the receiver by
minimizing the aggregation error under the channel alignment constraint. We
then develop a difference-of-convex algorithm for the resulting non-convex
optimization. Numerical experiments on image classification show that the
proposed method is able to achieve a similar learning accuracy as the
state-of-the-art CSIT-based solution, demonstrating the efficiency of our
approach in combating the lack of CSIT.
