We develop a novel technique to accelerate minorization-maximization (MM) procedure for the non-orthogonal multiple access (NOMA) weighted sum rate maximization problem. Specifically, we exploit the Lipschitz continuity of the gradient of the objective function to adaptively update the MM algorithm. With fewer additional analysis variables and low complexity second-order cone program (SOCP) to solve in each iteration of the MM algorithm, the proposed approach converges quickly at a small computational cost. By numerical simulation results, our algorithm is shown to greatly outperform known solutions in terms of achieved sum rates and computational complexity.

中文翻译：

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用于下行链路 NOMA 系统波束成形设计的快速自适应最小化-最大化程序

我们开发了一种新技术来加速非正交多址 (NOMA) 加权和速率最大化问题的最小化-最大化 (MM) 过程。具体来说，我们利用目标函数梯度的 Lipschitz 连续性来自适应地更新 MM 算法。由于在 MM 算法的每次迭代中需要求解的额外分析变量较少和低复杂度的二阶锥程序 (SOCP)，所提出的方法以较小的计算成本快速收敛。通过数值模拟结果，我们的算法在实现的总和率和计算复杂度方面明显优于已知的解决方案。