# Robust Radio Resource Allocation in MISO-SCMA Assisted C-RAN in 5G Networks

## TL;DR

This paper proposes a robust radio resource allocation method for MISO-SCMA assisted C-RAN in 5G, optimizing system sum rate under uncertain CSI and multiple constraints using iterative algorithms.

## Contribution

It introduces a novel robust resource allocation framework considering CSI uncertainty, combining beamforming, codebook allocation, and user association in a joint optimization.

## Key findings

- Improved sum rate performance under CSI uncertainty.
- Efficient iterative solution with MMA and ILP techniques.
- Performance validated across various system parameters and channel models.

## 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 regard, by supposing multiple input and single output (MISO) transmission technology, a novel robust radio resource allocation is proposed where considering uncertain channel state information (CSI), the worst case approach is applied. The main goal of the proposed radio resource allocation is to, maximize the system sum rate with maximum available power at radio remote head (RRH), minimum rate requirement of each slice, maximum frounthaul capacity of each RRH, user association, and SCMA constraints. To solve the proposed optimization problem in an efficient manner, an iterative method is deployed where in each iteration, beamforming and joint codebook allocation and user association subproblem are solved separately. By introducing some 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 (MMA) is applied. Via numerical results, the performance of the proposed system model versus different system parameters and for different channel models are investigated.