首页|Reinforcement Learning Algorithm for Improving Spectral Energy Efficiency Using Large Intelligent Surfaces
Reinforcement Learning Algorithm for Improving Spectral Energy Efficiency Using Large Intelligent Surfaces
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Wiley
The Spectral Energy Efficiency (SEE) is the concrete feature of future generations of wireless systems. It is in turn dependentupon the System User-Achievable-DataRate (SAR). The SAR of the current generation systems can be enhanced by use of LargeIntelligent Surfaces (LIS). They implement a pane of reflecting antennas made up of meta-materials.These panels are mountedon any architectural structure like apartments, schools/colleges etc. The beauty of LIS is that they can be trained by means ofmachine learning models to reflect the incoming electro-magneticsignal towards the required direction that can increase the receivedsignal strength at the receiver. This increased signal strength at the receiver further boosts the Signal to Noise ratio (SNR)and SAR. This paper implements a Reinforcement Learning (RiL) based customized loss model in a Recurrent Neural Network(RNN) model to enhance the SEE of the LIS based systems. The dataset required for training and validation of DL model isproduced from the publicly available ray tracing based DeepMIMO generator. The simulation findings demonstrate that thesuggested RNN-RiLmodel exhibits an enhancement of 1.14 bps/Hz in SAR, and an improvement of 2.75 Mbits/J enhancementin the SEE when compared to the baseline technique. This rise in the SEE can be useful in inculcating more number of users persec while maintaining the Quality of Service (QoS) thus enabling energy harvesting in LIS.
DeepMIMOlarge intelligent surfaces (LIS)next generation wireless systemsreinforcement learningspectral energy efficiency
Jai A. Desai、Shriram D. Markande
展开 >
G H Raisoni College of Engineering and Management, SPPU, Pune, India||Sinhgad College of Engineering, SPPU, Pune, India
Sinhgad Institute ofTechnology and Science, Pune, India||SPPU, Pune, India
NETL
NSTL
Wiley
