On High Performance MIMO Communications with Imperfect Channel Knowledge

Abstract

This paper compares the performance of a MIMO system applying a Mismatched Maximum Likelihood Detector (MMLD) with that applying a Generalized Likelihood Ratio Detector (GLRD) in the presence of channel estimation errors. Two sources of error are considered: errors due to noise and errors due to time variations induced by Doppler effects. The MMLD relies on the available channel estimate whereas the GLRD combines the channel estimate with a block of received data and uses a joint channel-symbol estimation approach to solve for the transmitted symbols. The GLRD is shown to outperform the MMLD whenever errors in channel estimation are incurred and the performance gap depends on the amount of error. Furthermore, the GLRD provides a unified receiver structure which can also be applied to differentially encoded MIMO systems and to situation where no channel estimate is available as in blind MIMO. The improved performance of the GLRD comes with increased computational complexity. This problem is addressed through search efficient algorithms based on the branch-estimate-bound optimization framework. We analyze the performance of the GLRD and provide a tight upper bound on the BER of the system at high SNR. Simulation results are shown for a 2 × 2 MIMO system. Index Terms-Multiple-input multiple-output, channel estimation error, correlated block fading, mismatched maximum likelihood detector, generalized likelihood ratio detector, orderedbranch-estimate-bound algorithm.

A unified receiver for MIMO communication with imperfect channel knowledge

“…Considering noisier measurement first leads to savings in computations because several backtracks can be avoided as the search proceeds. In [6], we validate the above argument through simulations and show that the ordering step achieves additional significant computational reduction compared with the BEB algorithm in [11], especially at low SNR and/or large block sizes.…”

“…The process is repeated for 10 6 scenarios at each SNR value considered, and the average BER is computed. The ML and the MMLD are implemented using SD [5], whereas the UGLRD is implemented using the OBEB algorithm [6] as discussed in Section IV. Figure 1 illustrates the performance of the MMLD with N p = 2 for different values of f D τ p corresponding to different values of σ 2 wD .…”

“…In this case, the OBEB algorithm offers a computationally efficient implementation for the UGLRD. It provides significant computational reduction and comes with no loss in performance as compared to brute force search [6].…”

“…Due to space limitation, we refer the reader to [6], [11] for the main idea and the details of the branch-estimate-bound optimization framework. We only highlight here the key modifications that are applied to the BEB algorithm in [11] in order to implement the OBEB algorithm [6]. First, unlike the BEB algorithm, the OBEB algorithm applies to space-time coded MIMO systems.…”

“…Although the Sphere Decoding (SD) algorithm [5] readily implements the MMLD, it can only implement the UGLRD when orthogonal space-time codes are utilized. For non-orthogonal space-time codes, the OrderedBranch-Estimate-Bound (OBEB) algorithm [6] can be used instead as mentioned in Section IV.…”