Impact of Residual Transmit RF Impairments on Training-Based MIMO Systems
2015 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 63, no 8, 2899-2911 p.Article in journal (Refereed) Published
Radio-frequency (RF) impairments, which intimately exist in wireless communication systems, can severely limit the performance of multiple-input-multiple-output (MIMO) systems. Although we can resort to compensation schemes to mitigate some of these impairments, a certain amount of residual impairments always persists. In this paper, we consider a training-based point-to-point MIMO system with residual transmit RF impairments (RTRI) using spatial multiplexing transmission. Specifically, we derive a new linear channel estimator for the proposed model, and show that RTRI create an estimation error floor in the high signal-to-noise ratio (SNR) regime. Moreover, we derive closed-form expressions for the signal-to-noise-plus-interference ratio (SINR) distributions, along with analytical expressions for the ergodic achievable rates of zero-forcing, maximum ratio combining, and minimum mean-squared error receivers, respectively. In addition, we optimize the ergodic achievable rates with respect to the training sequence length and demonstrate that finite dimensional systems with RTRI generally require more training at high SNRs than those with ideal hardware. Finally, we extend our analysis to large-scale MIMO configurations, and derive deterministic equivalents of the ergodic achievable rates. It is shown that, by deploying large receive antenna arrays, the extra training requirements due to RTRI can be eliminated. In fact, with a sufficiently large number of receive antennas, systems with RTRI may even need less training than systems with ideal hardware.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2015. Vol. 63, no 8, 2899-2911 p.
Hardware impairments; large-scale MIMO; pilot optimization; random matrix theory; training-based channel estimation
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:liu:diva-121134DOI: 10.1109/TCOMM.2015.2432761ISI: 000359535700014OAI: oai:DiVA.org:liu-121134DiVA: diva2:852292
Funding Agencies|Swedish Governmental Agency for Innovation Systems (VINNOVA) within VINN Excellence Center Chase; ELLIIT; CENIIT2015-09-082015-09-082016-02-21