Channel Estimation for Massive MIMO: A Semiblind Algorithm Exploiting QAM Structure

• Published in: 2019 53rd Asilomar Conference on Signals, Systems, and Computers pp. 2077 - 2081
• Main Authors: Yilmaz, Baki Berkay, Erdogan, Alper T.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-11-2019
• Subjects: Channel Estimation; Compressed Training; Massive MIMO
• ISSN: 2576-2303
• DOI: 10.1109/IEEECONF44664.2019.9048774

We introduce a new channel matrix estimation algorithm for Massive MIMO systems to reduce the required pilot symbols. The proposed method is based on Maximum A Posteriori estimation where the density of QAM transmission symbols are approximated with continuous uniform pdf. Under this simplification, joint channel source estimation problem can be posed as an optimization problem whose objective is quadratic in each channel and source symbol matrices, separately. Also, the source symbols are constrained to lie in an ℓ ∞ -norm ball. The resulting framework serves as the channel estimation counterpart of the recently introduced compressed training based adaptive equalization framework. Numerical examples demonstrate that the proposed approach significantly reduces the required pilot length to achieve desired bit error rate performance.