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A Dynamic Pilot and Data Power Allocation for TDD Massive MIMO Systems

In this paper, we propose a joint dynamic pilot and data power allocation scheme for time division duplex (TDD) massive multiple-input multiple-output (MIMO) systems, so as to both adaptively mitigate pilot contamination and balance the mutual interference. Due to the unknown of instant channel state information before pilots, we exploit the Gauss-Markov process of temporally-correlated channels and use the Kalman filter to not only filter out the pilot contamination but also provide the priori estimation values. Subsequently, the deterministic approximation of the rate is derived as a function of the priori channel estimation and the priori estimate errors, and accordingly the rate-profile maximization to achieve max-min fairness is formulated. To deal with this optimization coupled across the pilot power and data power as well as the users, we give an iterative alternating rate-suboptimal algorithm composed of two sub-problems, both of which are further solved by introducing the successive convex approximation (SCA) methods and slack variables. Numerical results confirm the improved rate provided by the proposed scheme.