Abstract
In this paper, a new methodology for the identification and equalization of massive Multiple-Input Multiple-Output (MIMO) channels in fifth generation (5G) wireless communications is proposed. Channel equalization is performed in state-space, having estimated the channel using the fractional-order Multivariable Output Error State Space (MOESP) system identification algorithm. The proposed fractional-order algorithm is an improvement over its integer-order counterpart that is currently used for state-space channel identification/estimation and state-space channel equalization. When dealing with fractional-order calculus our work adopts the Riemann-Liouville definition. Our numerical results of channel identification having used a chirp signal to excite our massive MIMO system show a lower mean square error (MSE) in channel estimation using the proposed fractional-order MOESP identification algorithm when compared to integer-order MOESP identification algorithm of increased order. Furthermore, following equalization, transmission using Binary phase shift keying (BPSK), Quadrature phase shift keying (QPSK) and 256-Quadrature amplitude modulation (256-QAM) signals show that the symbol error rate (SER) as a function of signal to noise ratio (SNR) performance of the fractional-order equalization algorithm compares to that of the integer-order equalization algorithm. The proposed channel identification algorithm also provides a more parsimonious solution for modelling multipath fading, thus enabling the design of fractional-order equalizers. In addition, they may be used in other applications where high order filtering and more complex control algorithms which are difficult to tune would be needed. Finally, the work has other technological applications where there is a requirement for modelling and control of propagation processes in dispersive media.
Highlights
Digital communication using massive multiple transmit and multiple receive antennas known as massive Multiple-Input Multiple-Output (MIMO) has been one of the most important technical developments in modern day communication
The use of the fractional-order modelling approach in identifying the massive MIMO frequency-selective wireless channels is motivated by its advantage of smaller residual errors for the identified model compared to the integer-order model, and this further enables the adoption of a fractional-order equalization approach
We propose the use of the Poisson Moment Functional (PMF) approach, details of which are presented in Appendix C
Summary
Digital communication using massive multiple transmit and multiple receive antennas known as massive Multiple-Input Multiple-Output (MIMO) has been one of the most important technical developments in modern day communication. The fractional-order time-derivatives of the input-output data are generally not measured meaning that the input-output matrices are not known, as a result the classical subspace methods originally developed for the identification of discrete-time models cannot be directly adapted for the identification of continuous-time fractional-order models. To address this problem the Poisson moment functional (PMF) approach is used when dealing with continuous-time fractional-order system identification, where the input-output data is first filtered using the PMF filter after which the MOESP identification algorithm outlined for discrete-time system identification is applied to the PMF filtered input-output data.
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