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User clustering based on correlation in 5G using semidefinite programming

For 5G cellular networks, non-orthogonal multiple access (NOMA) has been recognized as a promising technique. NOMA allows multiple users to access the same channel by adapting successive interference cancellation (SIC) and multiplexing in power domain. This causes the clustering problem to become critical as higher channel gain users need to cancel out the signals of lower channel gain users while the latter has to receive less interference. In this paper, we formulate the user clustering as a correlation clustering problem. We transform the problem by relaxing the clustering variables, and solve using semidefinite programming (SDP). Moreover, a simple iterative power allocation algorithm is nominated. In the simulation results, it can be seen that the correlation clustering using semidefinite programming outperforms the random clustering of users.