Rate splitting multiple access (RSMA) is a promising non-orthogonal transmission strategy for next-generation wireless networks. It has been shown to outperform existing multiple access schemes in terms of spectral and energy efficiency when suboptimal beamforming schemes are employed. In this work, we fill the gap between suboptimal and truly optimal beamforming schemes and conclusively establish the superior spectral and energy efficiency of RSMA. To this end, we propose a successive incumbent transcending (SIT) branch and bound (BB) algorithm to find globally optimal beamforming solutions that maximize the weighted sum rate or energy efficiency of RSMA in Gaussian multiple-input single-output (MISO) broadcast channels. Numerical results show that RSMA exhibits an explicit globally optimal spectral and energy efficiency gain over conventional multi-user linear precoding (MU-LP) and power-domain non-orthogonal multiple access (NOMA). Compared to existing globally optimal beamforming algorithms for MU-LP, the proposed SIT BB not only improves the numerical stability but also achieves faster convergence. Moreover, for the first time, we show that the spectral/energy efficiency of RSMA achieved by suboptimal beamforming schemes (including weighted minimum mean squared error (WMMSE) and successive convex approximation) almost coincides with the corresponding globally optimal performance, making it a valid choice for performance comparisons. The globally optimal results provided in this work are imperative to the ongoing research on RSMA as they serve as benchmarks for existing suboptimal beamforming strategies and those to be developed in multi-antenna broadcast channels.
翻译:在这项工作中,我们填补了亚最佳和真正最佳的波束成型计划之间的空白,最终建立了俄罗斯马亚的高级光谱和能源效率。为此,我们提议了一个连续的现任者超越(SIT)分支和约束(BB)对照算法,以找到全球最佳的波形成型解决方案,在高斯多输出单输出(MISO)广播频道中最大限度地提高RSMA的加权总和或能源效率。数字结果显示,RSMA在常规多用户线性预编码(MU-LAMP)和超超光谱多存取(NOMA)。与目前全球最佳的MU-LP(SIT BB)比,拟议的SITBB不仅在高斯多输出单输出(MIO)广播频道中使RSMA的加权总和速率或能源效率最大化。 数字模型显示,全球最优化的周期性能(MIMA)的当前最佳性能和最优化的比比值计算法(NOMA ) 与全球最佳的比值比值基准比较,拟议的SITBB不仅在高输出多输出(IMA) 将使得IMA 最接近的正统的周期的运行的周期化结果更接近地显示全球的周期性效率。