Data detection based on matrix decomposition for massive MIMO systems in realistic channel scenarios
Albreem, Mahmoud A.; Juntti, Markku; Shahabuddin, Shahriar; Abdallah, Saeed; Alhabbash, Alaa; Almajali, Eqab (2022-12-31)
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Mahmoud A. Albreem, Markku Juntti, Shahriar Shahabuddin, Saeed Abdallah, Alaa Alhabbash, Eqab Almajali, Data detection based on matrix decomposition for massive MIMO systems in realistic channel scenarios, Physical Communication, Volume 57, 2023, 101982, ISSN 1874-4907, https://doi.org/10.1016/j.phycom.2022.101982
© 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license by http://creativecommons.org/licenses/by-nc-nd/4.0/.
https://creativecommons.org/licenses/by-nc-nd/4.0/
https://urn.fi/URN:NBN:fi-fe202301162950
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Abstract
Massive multiple-input multiple-output (MIMO) is a key technology for modern wireless communication systems. In massive MIMO receivers, data detection is a computationally expensive task. In this paper, we explore the performance and the computational complexity of matrix decomposition based detectors in realistic channel scenarios for different massive MIMO configurations. In addition, data detectors based on decomposition algorithms are compared to the approximate-inversion detection (AID) methods. It is shown that the alternating-direction-method-of-multipliers-based-Infinity-Norm (ADMIN) detection is promising in realistic channel environment and the performance is stable even when the ratio of the base-station (BS) antenna elements to the number of users is small. In addition, this paper studies the performance of several detectors in imperfect channel state information (CSI) and correlated channels. Our work provides valuable insights for massive MIMO systems and very large-scale integration (VLSI) designers to select the appropriate massive MIMO detector based on their specifications.
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