Compressive Sensing Based Channel Estimation for Massive MIMO Communication Systems

Algorithm 1

Proposed SUCoSaMPrecovery algorithm.

Input: Sensing matrix and noisy measurement vector

Output: An sparse estimation of channels

Step 1 (Initialization)

1.

Trivial initial approximation

2.

Current samples = input samples

3.

Iterative index

4.

Initial sparsity level

Step 2 Solve the structure sparse vector to (15)

Repeat

1.

2.

Make the signal proxy

3.

Identify large components

4.

Merge supports

5.

Signal estimation by least squares

6.

7.

Prune to get next approximation

8.

Update the current samples

if

9. Iteration with fixed sparsity level

else

10. Update sparsity level ; ;

end if

Until stopping criterion true

Step 3 Obtain channels and obtain estimation of channels according to (11)-(15)