User Selection and Widely Linear Multiuser Precoding for One-dimensional Signalling

TL;DR

This paper develops widely linear precoding and user selection techniques for one-dimensional modulated signals in multiuser MISO systems, significantly increasing the number of users supported.

Contribution

It introduces closed-form solutions for widely linear precoding methods and a user selection algorithm that doubles the capacity compared to traditional approaches.

Findings

Widely linear processing can double the number of simultaneous users.

Proposed user selection algorithm doubles the number of users compared to conventional methods.

Closed-form solutions for various WL precoding schemes are derived.

Abstract

Massive deployment of low data rate Internet of things and ehealth devices prompts us to develop more practical precoding and user selection techniques that comply with these requirements. Moreover, it is known that when the data is real-valued and the observation is complex-valued, widely linear (WL) estimation can be employed in lieu of linear estimation to improve the performance. With these motivations, in this paper, we study the transmit precoding (beamforming) in multiuser multiple-input single-output communications systems assuming the transmit signal is one-dimensionally modulated and widely linear estimation is performed at the receivers. Closed-form solutions for widely linear maximum ratio transmission (MRT), WL zero-forcing (ZF), WL minimum mean square error (MMSE), and WL maximum signal to leakage and noise ratio (MSLNR) precoding are obtained. It is shown that widely linear processing can potentially double the number of simultaneous users compared to the linear processing of one-dimensionally modulated signals. Furthermore, to deal with the increasing number of communications devices a user selection algorithm compatible with widely linear processing of one-dimensionally modulated signals is proposed. The proposed user selection algorithm can double the number of simultaneously selected users compared to conventional user selection methods.