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This letter studies a cloud radio access network (C-RAN) with multipleintelligent reflecting surfaces (IRS) deployed between users and remote radioheads (RRH). Specifically, we consider the uplink transmission where each RRHquantizes the received signals from the users by either point-to-pointcompression or Wyner-Ziv compression and then transmits the quantization bitsto the BBU pool through capacity limited fronthhual links. To maximize theuplink sum rate, we jointly optimize the passive beamformers of IRSs and thequantization noise covariance matrices of fronthoul compression. An jointfronthaul compression and passive beamforming design is proposed by exploitingthe Arimoto-Blahut algorithm and semidefinte relaxation (SDR). Numericalresults show the performance gain achieved by the proposed algorithm.

Fronthaul Compression and Passive Beamforming Design for Intelligent Reflecting Surface-aided Cloud Radio Access Networks