Link performance estimation techniques for MIMO-OFDM systems with maximum likelihood receiver

Sung Hyun Moon, Kyoung Jae Lee, Jihoon Kim, Inkyu Lee

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Link adaptation allows a communication system to adapt its transmission modes according to channel conditions. Although a maximum likelihood (ML) receiver for multiple-input multiple-output (MIMO) systems provides optimal performance, estimating its link performance has been a difficult problem. In this paper, we propose a new link performance abstraction technique for MIMO orthogonal frequency-division multiplexing systems with the ML receiver. The performance of ML detection (MLD) is estimated by employing capacity bounds of two simple linear receivers. Then, we give a simple parametrization to compute the desired per-stream signal-to-noise ratio (SNR) values, which can be applied for both vertically and horizontally coded MIMO systems. Based on the derived per-stream SNR estimates, the block error rate is obtained using the received-bit information rate metrics. We also examine the effect of imperfect channel estimation as well as spatial correlations among antennas. Finally, extensive simulation results show that the proposed method provides superior estimation accuracy in the MIMO-MLD link evaluation with very low computational complexity.

Original languageEnglish
Article number6177980
Pages (from-to)1808-1816
Number of pages9
JournalIEEE Transactions on Wireless Communications
Issue number5
Publication statusPublished - 2012 May

Bibliographical note

Funding Information:
This work was supported in part by the IT R&D program of MKE/KEIT, Republic of Korea (No. 10038765, Development of B4G Mobile Communication Technologies for Smart Mobile Services), and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (NRF-2011-357-D00165 and No. 2010-0017909).


  • Link adaptation
  • PHY abstraction
  • maximum-likelihood receiver

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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