||Performance of an ℓ1 Regularized Subspace-based MIMO Channel Estimation with Random Sequences
Takano, Yasuhiro ,
Juntti, MarkkuMatsumoto, Tad
IEEE Wireless Communications Letters
2015-12-04 , Institute of Electrical and Electronics Engineers (IEEE)
The conventional ℓ2 multi-burst (MB) channel estimation can achieve the Cramer-Rao bound asymptotically by using the subspace projection. However, the ℓ2 MB technique suffers from the noise enhancement problem if the training sequences (TSs) are not ideally uncorrelated. We clarify that the problem is caused by an inaccurate noise whitening process. The ℓ1 regularized MB channel estimation can, however, improve the problem by a channel impulse response length constraint. Asymptotic performance analysis shows that the ℓ1 MB can improve channel estimation performance significantly over the ℓ2 MB technique in a massive multiple-input multiple-output system when the TSs are not long enough and not ideally uncorrelated.