Study of Robust Adaptive Power Allocation Techniques for Rate Splitting based MU-MIMO systems

TL;DR

This paper introduces a robust, low-complexity adaptive power allocation method for rate splitting MU-MIMO systems that improves sum-rate performance under imperfect channel knowledge.

Contribution

It proposes a novel stochastic gradient-based adaptive power allocation algorithm that accounts for channel error variance, enhancing robustness and practicality.

Findings

Outperforms traditional MU-MIMO in sum-rate under imperfect CSIT

Reduces computational complexity compared to existing methods

Provides theoretical analysis of stability and bounds

Abstract

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. %\vspace{-0.75em}