Iterative receivers with channel estimation for multi-user MIMO-OFDM: complexity and performance

Research output: Contribution to journalArticle


Abstract in Undetermined
A family of iterative receivers is evaluated in terms of complexity and performance for the case of an uplink multi-user (MU) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system. The transmission over block fading channels is considered. The analyzed class of receivers is performing channel estimation inside the iterative detection loop, which has been shown to improve estimation performance. As part of our results we illustrate the ability of this type of receiver to reduce the required amount of pilot symbols. A remaining question to ask is which combinations of estimation and detection algorithms that provide the best trade-off between performance and complexity. We address this issue by considering MU detectors and channel estimators, with varying algorithm complexity. For MU detection, two algorithms based on parallel interference cancellation (PIC) are considered and compared with the optimal symbol-wise maximum a-posteriori probability (MAP) detector. For channel estimation, an algorithm performing joint minimum-mean-square-error (MMSE) estimation is considered along with a low complexity replica making use of a Krylov subspace method. An estimator based on the space alternating generalized expectation-maximization (SAGE) algorithm is also considered. Our results show that low-complexity algorithms provide the best tradeoff, even though more receiver iterations are needed to reach a desired performance.


Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Electrical Engineering, Electronic Engineering, Information Engineering


  • MIMO, OFDM, multi-user detection, iterative receiver, complexity, channel estimation
Original languageEnglish
Pages (from-to)1-17
JournalEurasip Journal on Wireless Communications and Networking
Publication statusPublished - 2012
Publication categoryResearch