Deep Learning Based Energy Efficient Scheme For Massive MIMO
TRV. Anandharajan1, C. Murugalakshmi2, B. Adhitya3, K. Swetha4
1Dr. TRV. Anandharajan, Professor, Department of ECE, Malla Reddy Engineering College for Women Autonomous, Secunderabad (Telangana), India.
2C. Murugalakshmi, Assistant Professor, Einstein College of Engineering, Tirunelveli (Tamil Nadu), India.
3B. Adhitya, Assistant Professor, Department of ECE, Malla Reddy Engineering College for Women Autonomous, Secunderabad (Telangana), India.
4K. Swetha, Assistant Professor, Department of ECE, Malla Reddy Engineering College for Women Autonomous, Secunderabad (Telangana), India.
Manuscript received on 30 September 2019 | Revised Manuscript received on 12 November 2019 | Manuscript Published on 22 November 2019 | PP: 1776-1780 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F13380986S319/19©BEIESP | DOI: 10.35940/ijeat.F1338.0986S319
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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: This paper proposes a Deep Learning Energy Efficient Scheme (DLEE) for a massive multiple input multiple output system (MIMO). Massive MIMO is deployed using large number of antennas for multiple users. The proposed DLEE, learns the relationship between spatial beamforming pattern and the power consumption in a base station. In this work, we design a novel learning method where the spatial correlation across UE antennas are taken as input feature vector and find the output labels which give us the energy efficiency in a BS. Due to multipath propagation, other methods only try to address the energy efficiency problem through the bit rate and the power required for the throughput to be efficient. This paper discusses the unsupervised algorithm DLEE which is similar to an autoencoder by combining the power consumed due to radiation pattern through beamforming and the DL framework to address the energy efficiency to an extent of 12% in a BS.
Keywords: Deep Learning, Massive MIMO, Beamforming.
Scope of the Article: Deep Learning