OFDM-based Massive MIMO Channel Estimation using Gaussian Mixture Learning and Compressed Sensing Methods
T. Ravi Babu1, C. Dharma Raj2, V. Adinarayana3
1T. Ravi Babu, Research Scholar, Department of ECE, GITAM Institute of Technology, GIT GITAM, Visakhapatnam (Andhra Pradesh), India.
2C. Dharma Raj, Professor, Department of ECE, GITAM Institute of Technology, GIT GITAM, Visakhapatnam (Andhra Pradesh), India.
3V. Adinarayana, Professor, Department of ECE, Avanthi Institute of Engineering and Technology AIET, Vizianagaram (Andhra Pradesh), India.
Manuscript received on 22 November 2019 | Revised Manuscript received on 10 December 2019 | Manuscript Published on 30 December 2019 | PP: 7-13 | Volume-9 Issue-2S3 December 2019 | Retrieval Number: B10031292S319/2019©BEIESP | DOI: 10.35940/ijitee.B1003.1292S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Massive MIMO-OFDM system is proved to be an effective and most sustainable technology to forthcoming applications of 5G wireless communications. It furnished significant gains that facilitate a higher number of user connections at high data rates with improved latency and reliability. To achieve accurate channel knowledge, lessen pilot overhead is necessary. To resolve this problem, one of the favorite approaches is compressed sensing. Sparse channel estimation develops the essential sparsity between the communicating channels that can be improved by the channel estimation efficacy with lower pilot overhead. To achieve this, non-zero vector distribution can be taking into consideration the Gaussian mixture accordingly, learn their characteristics towards the expectation-maximization procedure. The results of simulation have proved the performance of proposed estimation approach of channel keeping with minimum pilot overhead and developed exceptional symbol error rate (SER) performance of the system.
Keywords: Massive MIMO-OFDM, Gaussian Mixture, Approximate Message Passing, Channel Estimation Compressed Sensing.
Scope of the Article: Innovative Sensing Cloud and Systems