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This paper investigates the transmission design in thereconfigurable-intelligent-surface (RIS)-assisted downlink system. The channelstate information (CSI) is usually difficult to be estimated at the basestation (BS) when the RIS is not equipped with radio frequency chains. In thispaper, we propose a downlink transmission framework with unknown CSI viaBayesian optimization. Since the CSI is not available at the BS, we treat theunknown objective function as the black-box function and take the beamformer,the phase shift, and the receiving filter as the input. Then the objectivefunction is decomposed as the sum of low-dimension subfunctions to reduce thecomplexity. By re-expressing the power constraint of the BS in sphericalcoordinates, the original constraint problem is converted into an equivalentunconstrained problem. The users estimate the sum MSE of the training symbolsas the objective value and feed it back to the BS. We assume a Gaussian priorof the feedback samples and the next query point is updated by minimizing theconstructed acquisition function. Furthermore, this framework can also beapplied to the power transfer system and fairness problems. Simulation resultsvalidate the effectiveness of the proposed transmission scheme in the downlinkdata transmission and power transfer.

Reconfigurable-intelligent-surface-assisted Downlink Transmission Design via Bayesian Optimization