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The cloud/centralized radio access network (C-RAN) architecture is recognized as a strong candidate for the next generation wireless standards, potentially reducing the total cost of ownership. Spatial modulation (SM) is a cost-effective multiple-input-multiple-output (MIMO) solution, where only a single-radio frequency chain is required for transmission. In this context, we propose analogue radio over fiber (A-RoF) aided MIMO techniques for learning assisted adaptive C-RAN system, where SM combined with space-time block coding is optically processed relying on the optical frequency indices in the central unit of the C-RAN, which also is capable of tuning the connected remote radio heads (RRHs). Furthermore, to improve the spectral efficiency, we invoke our proposed flexible C-RAN architecture for implementing learning assisted transceiver adaptation, where the number of RRHs connected to a single user and its modulation techniques employed are controlled using the K-nearest neighbor algorithm. The simulation results show that the bit error ratio performance of our A-RoF system is only marginally degraded compared to that operating without the A-RoF link, while benefiting from our energy- and cost-efficient C-RAN design. Moreover, we show that the learning assisted adaptation is capable of outperforming the classic threshold-based adaptation in terms of the system's achievable rate.