Impact of Residual Transmit RF Impairments on Training-Based MIMO Systems
Paper in proceeding, 2014

Radio-frequency (RF) impairments, that exist inti- mately in wireless communications systems, can severely degrade the performance of traditional multiple-input multiple-output (MIMO) systems. Although compensation schemes can cancel out part of these RF impairments, there still remains a certain amount of impairments. These residual impairments have fun- damental impact on the MIMO system performance. However, most of the previous works have neglected this factor. In this paper, a training-based MIMO system with residual transmit RF impairments (RTRI) is considered. In particular, we derive a new channel estimator for the proposed model, and find that RTRI can create an irreducible estimation error floor. Moreover, we show that, in the presence of RTRI, the optimal training sequence length can be larger than the number of transmit antennas, especially in the low and high signal-to-noise ratio (SNR) regimes. An increase in the proposed approximated achievable rate is also observed by adopting the optimal training sequence length. When the training and data symbol powers are required to be equal, we demonstrate that, at high SNRs, systems with RTRI demand more training, whereas at low SNRs, such demands are nearly the same for all practical levels of RTRI.

hardware impairments

channel training

optimal pilot length

estimation

MIMO

Author

Xinlin Zhang

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Michail Matthaiou

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Mikael Coldrey

Ericsson

Emil Björnson

Nokia

Royal Institute of Technology (KTH)

IEEE International Conference on Communications, ICC 2014, Sydney, Australia

4741-4746 6884070
978-147992003-7 (ISBN)

Areas of Advance

Information and Communication Technology

Subject Categories

Telecommunications

Signal Processing

Computer Systems

DOI

10.1109/ICC.2014.6884070

ISBN

978-147992003-7

More information

Latest update

11/19/2018