||ℓ1 LS and ℓ2 MMSE-based Hybrid Channel Estimation for Intermittent Wireless Connections
Takano, Yasuhiro ,
Juntti, MarkkuMatsumoto, Tad
IEEE Transactions on Wireless Communications
328 , 2015-08-25 , Institute of Electrical and Electronics Engineers (IEEE)
Broadband wireless channels observed at a receiver cannot fully exhibit dense nature in a low to moderate signalto- noise ratio (SNR) regime, if the channels follow a typical propagation scenario such as Vehicular-A or Pedestrian-B. It is hence expected that ℓ1 regularized channel estimation methods can improve channel estimation performance in the broadband wireless channels. However, it is well-known that the ℓ2 multiburst (MB) channel estimation achieves the Cram´er-Rao bound (CRB) asymptotically. This is because the ℓ2 MB technique formulated as a minimum mean square error (MMSE) problem improves the mean squared error (MSE) performance by utilizing the subspace projection. Performance analysis shows that ℓ1 regularized channel estimation does not improve the MSE performance significantly over the ℓ2 MB technique so far as the subspace channel model assumption is correct. We demonstrate, however, a receiver with ℓ1 regularized channel estimation can improve bit error rate (BER) performance if the assumption is not always correct. For this purpose, we focus on intermittent transmission (TX) scenario which is defined as a generalized TX sequence having arbitrary length interruption between two continuous TX bursts. A receiver with the ℓ2 MB method suffers from BER deterioration in an intermittent TX scenario having abrupt channel changes. As a solution to the problem, we propose a new algorithm which is a hybrid of ℓ1 regularized least square (LS) and ℓ2 MMSE channel estimation techniques. Simulation results show that the receiver with the proposed algorithm achieves a significant BER gain over that of the ℓ2 MB technique in the intermittent TX scenario.