Efficient Inverse Covariance Matrix Estimation for Low-Complexity Closed-Loop DPD Systems

This paper studies closed-loop digital predistorti on systems, with special focus on linearization of mmW active antenna arrays. Considering the beam-dependent nonlinear tortion and very high DPD processing rates, a modified orthogonalized (SO) learning solution is proposed, which capable of reducing the computational complexity compa to other similar solutions, while at the same time obtainin comparable linearization performance. The modified SO consists of a novel method for efficiently calculating the inverse of input data covariance matrix. Thorough RF measurement res is at 28 Gib band featuring a state-of-the-art 64 element active array and channel bandwidths up to 800 MHz, are reported, A complexity analysis is also carried out which, together with the obtained results, allow to asses the performance-complexly trade-offs. Altogether, the results show that the proposed methods can facilitate efficient mmW active antenna array linearization.