OFDM Channel Estimation Using Compressed Sensing L1-Regularized Least Square Problem Solver

Compressed Sensing is an emerging methodology to reconstruct signals with smaller number of projections. Nyquist rate yields too many samples, which is high for broadband signals that are used in many applications. The proposed method unveils the application of compressed sensing in the channel estimation of Orthogonal Frequency Division Multiplexing (OFDM). ℓ1-regularized Least square problem solver method is used as compressive sensing algorithm. Existing methods like Least square (LS) estimator and Minimum Mean Square Error (MMSE) estimator implementation has more complex formulations and utilizes many samples making the implementation cost of sensor to increase drastically. Results of the proposed method is compared with the existing MMSE method. The proposed compressed sensing approach in OFDM channel estimation results in good accuracy and less implementation cost.