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Rate splitting (RS) systems can better deal with imperfect channel stateinformation at the transmitter (CSIT) than conventional approaches. However,this requires an appropriate power allocation that often has a highcomputational complexity, which might be inadequate for practical and largesystems. To this end, adaptive power allocation techniques can provide goodperformance with low computational cost. This work presents novel robust andadaptive power allocation technique for RS-based multiuser multiple-inputmultiple-output (MU-MIMO) systems. In particular, we develop a robust adaptivepower allocation based on stochastic gradient learning and the minimization ofthe mean-square error between the transmitted symbols of the RS system and thereceived signal. The proposed robust power allocation strategy incorporatesknowledge of the variance of the channel errors to deal with imperfect CSIT andadjust power levels in the presence of uncertainty. An analysis of theconvexity and stability of the proposed power allocation algorithms isprovided, together with a study of their computational complexity andtheoretical bounds relating the power allocation strategies. Numerical resultsshow that the sum-rate of an RS system with adaptive power allocationoutperforms RS and conventional MU-MIMO systems under imperfect CSIT.%\vspace{-0.75em}

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