Low-Complexity Massive MIMO Tensor Precoding

We present a novel and low-complexity massive multiple-input multiple-output (MIMO) precoding strategy based on novel findings concerning the subspace separability of Rician fading channels. Considering a uniform rectangular array at the base station, we show that the subspaces spanned by the channe...

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Veröffentlicht in:Conference record - Asilomar Conference on Signals, Systems, & Computers S. 348 - 355
Hauptverfasser: Ribeiro, Lucas N., Schwarz, Stefan, de Almeida, Andre L. F., Haardt, Martin
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2020
Schlagworte:
Computer simulation
Massive MIMO
MIMO
Precoding
Rician channels
Runtime
Tensors
ISSN:2576-2303
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Zusammenfassung:We present a novel and low-complexity massive multiple-input multiple-output (MIMO) precoding strategy based on novel findings concerning the subspace separability of Rician fading channels. Considering a uniform rectangular array at the base station, we show that the subspaces spanned by the channel vectors can be factorized as a tensor product between two lower dimensional subspaces. Based on this result, we formulate tensor maximum ratio transmit and zero-forcing precoders. We show that the proposed tensor precoders exhibit lower computational complexity and require less instantaneous channel state information than their linear counterparts. Finally, we present computer simulations that demonstrate the applicability of the proposed tensor precoders in practical communication scenarios.
ISSN:2576-2303
DOI:10.1109/IEEECONF51394.2020.9443492