A new tensor-structured algorithm with variational free energy inference predicts non-stationary massive MIMO channels better than five established benchmarks in QuaDRiGa simulations.
Parametric Schemes for Prediction of Wideband MIMO Wireless Channels
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abstract
Information on the future state of time varying frequency selective channels can significantly enhance the effectiveness of feedback in adaptive and limited feedback MIMO-OFDM systems. This paper investigates the parametric extrapolation of wideband MIMO channels using variations of the double directional MIMO model. We propose three predictors which estimate parameters of the channel using 4D, 3D and 2D extensions of the ESPRIT algorithm and predict future states of the channel using the models. Furthermore, using the vector formulation of the Cramer Rao lower bound for functions of parameters, we derive a bound on the prediction error in wideband MIMO channels. Numerical simulations are used to evaluate the performance of the proposed algorithms under different channel and transmission conditions, and a comparison is made with the derived error bound.
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A Tensor-Structured Approach to Dynamic Channel Prediction for Massive MIMO Systems with Temporal Non-Stationarity
A new tensor-structured algorithm with variational free energy inference predicts non-stationary massive MIMO channels better than five established benchmarks in QuaDRiGa simulations.