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

Pablo Pascual Campo, Lauri Anttila, Vesa Lampu, Yan Guo, Neng Wang, Mikko Valkama

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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This paper studies closed-loop digital predistortion systems, with special focus on linearization of mmW active antenna arrays. Considering the beam-dependent nonlinear distortion and very high DPD processing rates, a modified self-orthogonalized (SO) learning solution is proposed, which is capable of reducing the computational complexity compared to other similar solutions, while at the same time obtaining a comparable linearization performance. The modified SO consists of a novel method for efficiently calculating the inverse of the input data covariance matrix. Thorough RF measurement results at 28 GHz 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-complexity trade-offs. Altogether, the results show that the proposed methods can facilitate efficient mmW active antenna array linearization.
Original languageEnglish
Title of host publication2021 IEEE MTT-S International Wireless Symposium (IWS)
Number of pages3
ISBN (Electronic)978-1-6654-3527-7
ISBN (Print)978-1-6654-3528-4
Publication statusPublished - 2021
Publication typeA4 Article in conference proceedings
EventIEEE International Wireless Symposium - Nanjing, China
Duration: 23 May 202126 May 2021


ConferenceIEEE International Wireless Symposium

Publication forum classification

  • Publication forum level 1


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