Low Complexity MIMO Channel Prediction for Fast Time-Variant Vehicular Communications Channels Based on Discrete Prolate Spheroidal Sequences

Farnoosh Talaei,Jinlong Zhan,Xiaodai Dong

doi:10.1109/access.2021.3056297

Farnoosh Talaei, Jinlong Zhan + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3056297

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Abstract

Channel state information (CSI) is required at the transmitter for achieving the maximum potentials of multiple-input multiple-output (MIMO) systems. In fast time-variant vehicular communications channels high data rate feedback lines are required in a frequency division duplex (FDD) transceiver for updating the transmitter with the rapidly changing CSI. Even with high data rate feedback lines, the delay caused by channel estimation and feedback may lead to outdated CSI at the transmitter. To reduce both the feedback load and CSI delay, this paper presents a reduced rank autoregressive (AR) channel predictor based on low dimensional discrete prolate spheroidal (DPS) sequences. The new subframe-wise DPS basis expansion model (DPS-BEM) channel predictor properly exploits the channel's restriction to low dimensional subspaces for reducing the prediction error and the computational complexity. The proposed channel predictor can be applied for updating the precoding matrix in time-variant MIMO systems. Simulation results demonstrate that the proposed channel predictor outperforms the DPS based minimum energy (ME) predictor at different Doppler frequencies and has better performance than the conventional Wiener predictor for slower time-variant channels and almost similar performance for very fast time-variant channels with reduced amount of computational complexity.

Highlights

Multiple-Input Multiple-Output (MIMO) wireless communication attracted high attention during the past decades due to its capability for providing higher capacity and performance gains compared to Single-Input Single-Output (SISO) systems
By using the polynomial basis expansion model (P-basis-expansion model (BEM)), paper [26] proposes a tracking algorithm based on the fact that the changes in the channel path number and path delays are small over a few adjacent orthogonal frequency-division multiplexing (OFDM) symbols
In this paper we develop a new discrete prolate spheroidal (DPS)-BEM channel predicting scheme which can be applied for reduced feedback load and feedback delay precoder design in fast time-variant V2X communications

Summary

INTRODUCTION

Multiple-Input Multiple-Output (MIMO) wireless communication attracted high attention during the past decades due to its capability for providing higher capacity and performance gains compared to Single-Input Single-Output (SISO) systems. By using the polynomial basis expansion model (P-BEM), paper [26] proposes a tracking algorithm based on the fact that the changes in the channel path number and path delays are small over a few adjacent orthogonal frequency-division multiplexing (OFDM) symbols. In this paper we develop a new DPS-BEM channel predicting scheme which can be applied for reduced feedback load and feedback delay precoder design in fast time-variant V2X communications. The proposed channel predictor assumes non-overlapping transmitted frames and applies a sub-frame wise tracking approach for updating the DPS-basis coefficients based on a Q-order AR modelling of the basis coefficients. The CE-BEM tracking schemes of [25], [27] consider time-multiplexed training sessions inside each subframe for updating the basis coefficients, while the proposed frame structure only assumes known channel coefficients at the beginning of each frame. The smallest integer that is greater than or equal to a ∈ R is denoted by a

SYSTEM MODEL

PROPOSED DPS BASED AR PREDICTOR

MEAN SQUARE ERROR OF THE PREDICTOR

10: Repeat 1 to 5

SIMULATION RESULTS

CONCLUSION