Abstract
In this paper, by considering multiple slices, a downlink transmission of a sparse code multiple access (SCMA) based cloud-radio access network (C-RAN) is investigated. In this setup, by assuming multiple-input and single-output (MISO) transmission mode, a novel robust radio resource allocation is proposed where considering uncertain channel state information, the worst case approach is applied. We consider a radio resource allocation problem with the objective to maximize the total sum rate of users subject to a minimum required rate of each slice and practical limitations of C-RAN and SCMA. To solve the proposed optimization problem in an efficient manner, an iterative method is deployed where beamforming and joint codebook allocation and user association subproblems are sequentially solved. By introducing auxiliary variables, the joint codebook allocation and user association subproblem is transformed into an integer linear programming, and to solve the beamforming optimization problem, minorization-maximization algorithm is applied. Via numerical results, the performance of the proposed algorithm is investigated versus different uncertainty level for different system parameters.
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