Rate splitting (RS) systems can better deal with imperfect channel state information at the transmitter (CSIT) than conventional approaches. However, this requires an appropriate power allocation that often has a high computational complexity, which might be inadequate for practical and large systems. To this end, adaptive power allocation techniques can provide good performance with low computational cost. This work presents novel robust and adaptive power allocation technique for RS-based multiuser multiple-input multiple-output (MU-MIMO) systems. In particular, we develop a robust adaptive power allocation based on stochastic gradient learning and the minimization of the mean-square error between the transmitted symbols of the RS system and the received signal. The proposed robust power allocation strategy incorporates knowledge of the variance of the channel errors to deal with imperfect CSIT and adjust power levels in the presence of uncertainty. An analysis of the convexity and stability of the proposed power allocation algorithms is provided, together with a study of their computational complexity and theoretical bounds relating the power allocation strategies. Numerical results show that the sum-rate of an RS system with adaptive power allocation outperforms RS and conventional MU-MIMO systems under imperfect CSIT.

中文翻译：

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用于基于速率拆分的 MU-MIMO 系统的稳健和自适应功率分配技术

与传统方法相比，速率分离 (RS) 系统可以更好地处理发射机 (CSIT) 处的不完美信道状态信息。然而，这需要适当的功率分配，通常具有很高的计算复杂性，这对于实际和大型系统可能是不够的。为此，自适应功率分配技术可以以低计算成本提供良好的性能。这项工作为基于 RS 的多用户多输入多输出 (MU-MIMO) 系统提出了新颖的鲁棒和自适应功率分配技术。特别是，我们开发了一种基于随机梯度学习和最小化 RS 系统的传输符号与接收信号之间的均方误差的鲁棒自适应功率分配。所提出的鲁棒功率分配策略结合了信道误差方差的知识，以处理不完善的 CSIT 并在存在不确定性的情况下调整功率电平。提供了对所提出的功率分配算法的凸性和稳定性的分析，以及对它们的计算复杂性和与功率分配策略相关的理论界限的研究。数值结果表明，在不完美 CSIT 下，具有自适应功率分配的 RS 系统的和速率优于 RS 和传统的 MU-MIMO 系统。连同对它们的计算复杂性和与功率分配策略相关的理论界限的研究。数值结果表明，在不完美 CSIT 下，具有自适应功率分配的 RS 系统的和速率优于 RS 和传统的 MU-MIMO 系统。连同对它们的计算复杂性和与功率分配策略相关的理论界限的研究。数值结果表明，在不完美 CSIT 下，具有自适应功率分配的 RS 系统的和速率优于 RS 和传统的 MU-MIMO 系统。