An Area-Efficient On-Chip Memory System for Massive MIMO Using Channel Data Compression

Liu, Yangxurui; Liu, Liang; Edfors, Ove; Öwall, Viktor

doi:10.1109/tcsi.2018.2859361

Cited by 2 publications

(1 citation statement)

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“…In this section, we will introduce two applications of our proposed tensor z-transform, namely, "tensor filters." Ten-sor filters can be widely adopted to characterize massive MIMO (multi-input multioutput) linear-time-invariant (LTI) systems [29][30][31][32][33].…”

Section: Applications Of Tensor Z-transformmentioning

confidence: 99%

Tensor z‐Transform

Chang,

2024

Journal of Applied Mathematics

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The multi-input multioutput (MIMO) systems involving multirelational signals generated from distributed sources have been emerging as the most generalized model in practice. The existing work for characterizing such a MIMO system is to build a corresponding transform tensor, each of whose entries turns out to be the individual z-transform of a discrete-time impulse response sequence. However, when a MIMO system has a global feedback mechanism, which also involves multirelational signals, the aforementioned individual z-transforms of the overall transfer tensor are quite difficult to formulate. Therefore, a new mathematical framework to govern both feedforward and feedback MIMO systems is in crucial demand. In this work, we define the tensor z-transform to characterize a MIMO system involving multirelational signals as a whole rather than the individual entries separately, which is a novel concept for system analysis. To do so, we extend Cauchy’s integral formula and Cauchy’s residue theorem from scalars to arbitrary-dimensional tensors, and then, to apply these new mathematical tools, we establish to undertake the inverse tensor z-transform and approximate the corresponding discrete-time tensor sequences. Our proposed new tensor z-transform in this work can be applied to design digital tensor filters including infinite-impulse-response (IIR) tensor filters (involving global feedback mechanisms) and finite-impulse-response (FIR) tensor filters. Finally, numerical evaluations are presented to demonstrate certain interesting phenomena of the new tensor z-transform.

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