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Full-Duplex MIMO Precoding for Sum-Rate Maximization With Sequential Convex Programming

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PROJECT TITLE :

Full-Duplex MIMO Precoding for Sum-Rate Maximization With Sequential Convex Programming

ABSTRACT:

This paper focuses on precoding design for total-rate maximization while considering the consequences of residual self-interference for multiuser multiple-input–multiple-output (MU-MIMO) full-duplex (FD) systems. The problem formulation results in a nonconvex matrix-variable optimization downside, where we tend to develop two economical total-rate maximization algorithms using sequential convex programming (SCP), specifically, the distinction of convex functions (DC)-based and the sequential convex approximations for matrix-variable programming (SCAMP) algorithms. In addition, we have a tendency to derive the achievable sum rate under the impact of residual self-interference. Simulation results show that, even in cases of high self-interference and high estimation error, the SCAMP algorithm provides approximately twenty%–thirty% add-rate improvements over both standard optimized 0.5-duplex (HD) transmission and the prevailing state-of-the-art FD algorithm in a wide selection of eventualities. Finally, the convergence results indicate that the DC-based algorithm tends to initially provide the most effective performance; but, at convergence, the SCAMP algorithm tends to considerably outperform the opposite algorithms.


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Full-Duplex MIMO Precoding for Sum-Rate Maximization With Sequential Convex Programming - 4.5 out of 5 based on 2 votes

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