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Contact : +91 7053938407

Article Abstract

International Journal of Advance Research in Multidisciplinary, 2025;3(3):338-343

Efficient Deep Learning for Massive Mimo Channel State Estimation

Author : Anubhav Pandey and Dr. Vinod Kumar Suman

Abstract

Future wireless communications networks with the aid of the massive MIMO technologies will be able to significantly enhance the spectrum efficiency. This will be made possible because a base station (BS) that will have numerous antennas. can service many users’ equipment (UE) terminals. Evidence from information theory suggests that such multiantenna arrays allow for high-capacity communications using beamforming. Massive MIMO networks can't perform as intended without precise predictions of the downlink channel state information (CSI) in the trans-matter to precoding, which is required by the base station. Receivers can usually estimate CSI with the use of known pilot signals. The BS is able to measure the down-link CSI using pilots in uplink broadcasts in the special case of time-division duplex (TDD) mode, courtesy of channel reciprocity. The BS uses UE feedback to approximate downlink CSI in frequency division duplex (FDD) mode because the channel reciprocity of the uplink channel and the downlink channel is less than 100 rather limited. One important consideration is how to reduce feedback bandwidth while keeping downlink CSI predictions correct; this is known as the CSI feedback scheme. We demonstrate that this approach may reduce computing complexity while preserving similar estimate accuracy.

Keywords

Deep Learning, MIMO, networks, communications and technologies