CRDN: Cognitive Radio with Deep Network for Proficient Spectrum Sharing in Massive MIMO Systems

Abstract

The basic objective of cognitive radio networks (CRNs) are to effectively utilize the limited spectrum by strategically exploiting the unoccupied bands of frequencies or by pooling the available frequencies with other networks. The two approaches that have the potential to boost spectral efficiency of next-generation wireless communication networks are massive multiple-input multiple-output (mMIMO) and CRN. In this paper, we proposed a novel spectrum sharing technique for cognitive radio with mMIMO system using 3D space data gathering and learning based on Deep Learning method. Newfangled layer architecture is designed to train the primary network information assemblage named as Deep Learning Based Environment Training (DeepEnvNet). Also using mMIMO structure in cognitive radio base station (CRBS), we angular facts of CR user equipment (UE) by utilizing the spatial resolution of CBS. Iterative Hard Thresholding (IHT) can be used for direction of Arrival (DoA) estimation. The main 3D space spectrum coverage is accomplished via contemplation of two CBS for each cell in the network at the process of spectrum prophecy. Once the spectrum sensing is performed via DeepEnvNet, the greedy spectrum scheduling is performed via the two strategy: to maximize the CR coverage, to maximize the average sum rate of the transmission in CRN mMIMO. The experimental results are analyzed with the metrics of Average Achieved Sum Rate (AASR) and Average Scheduling Number (ASN). As reducing deep learning rate and batch size, highest AASR achieved in our proposed model than the earlier works of CRN spectrum sharing.

Keywords:
Cognitive radio networks; Massive multiple-input multiple-output; Iterative Hard Thresholding

HIGHLIGHTS

CRNs are to effectively utilize the limited spectrum by pooling the available frequencies with other networks.

To maximize the CR coverage and the average sum rate of the transmission in CRN mMIMO.

Reducing deep learning rate and batch size, highestAASR achieved in our proposed model.