Subspace based blind sparse channel estimation

The paper proposes a subspace based blind sparse channel estimation method using ℓ
₁
-ℓ
₂
optimization by replacing the ℓ
₂
-norm minimization in the conventional subspace based method by the ℓ
₁
-norm minimization problem. Numerical results confirm that the proposed method can significantly improve the estimation accuracy for the sparse channel, while achieving the same performance as the conventional subspace method when the channel is dense. Moreover, the proposed method enables us to estimate the channel response with unknown channel order if the channel is sparse enough.