Green and Intelligent Signal Processing: Deep Learning Approaches for Energy-Efficient MIMO Systems

Abstract

Due to the massive expansion of wireless applications, the ever-increasing demands for mobile technology, and the development of Internet of Things technology, wireless networks are confronting an increase in data traffic and resource management issues. Due to their capacity for data storage and spectrum efficiency, fifth-generation cellular networks have attracted a lot of interest. In order to meet a variety of user needs, multiple-input multiple-output (MIMO) networks are dependable solutions for data storage and capacity problems. MIMO systems are crucial for achieving revolutionary improvements in energy efficiency (EE) and area throughput. EE is most economical and one of the simplest strategies to fight global warming, energy reduction bills, and improvize competitive performance. EE and area throughput can be greatly enhanced by Deep Learning (DL). In 5G wireless communication systems, it is essential. The suggested model considered the total energy utilization of the circuit elements and the power amplifier of the BS, as well as the user equipment (UE) with a single antenna. In this paper, Green and Intelligent Signal Processing: Deep Learning Approaches for Energy-Efficient MIMO Systems (GISPDL-EEMIMO). The proposed GISPDL-EEMIMO technique undergoes data collection, data preprocessing, feature extraction, and prediction. A set of experiments has been performed to demonstrate the promising performance of the GISPDL-EEMIMO technique. The comparative findings showed that, in terms of distinct measures, the GISPDL-EEMIMO technique outperforms other existing models.