A Deep Learning-Based Power Control and Consensus Performance of Spectrum Sharing in the CR Network

Muhammad Muzamil Aslam,Liping Du,Hassan Azeem,Zahoor Ahmed,Muhammad Nauman Irshad,Longzhe Han

doi:10.1155/2021/7125482

Abstract

The cognitive radio network (CRN) is aimed at strengthening the system through learning and adjusting by observing and measuring the available resources. Due to spectrum sensing capability in CRN, it should be feasible and fast. The capability to observe and reconfigure is the key feature of CRN, while current machine learning techniques work great when incorporated with system adaption algorithms. This paper describes the consensus performance and power control of spectrum sharing in CRN. (1) CRN users are considered noncooperative users such that the power control policy of a primary user (PU) is predefined keeping the secondary user (SU) unaware of PU’s power control policy. For a more efficient spectrum sharing performance, a deep learning power control strategy has been developed. This algorithm is based on the received signal strength at CRN nodes. (2) An agent-based approach is introduced for the CR user’s consensus performance. (3) All agents reached their steady-state value after nearly 100 seconds. However, the settling time is large. Sensing delay of 0.4 second inside whole operation is identical. The assumed method is enough for the representation of large-scale sensing delay in the CR network.

Highlights

Intelligent processing is one of the major advantages of cognitive radio network (CRN) [1]
Machine learning techniques are linked with CR technology
The cognitive radio system which consists of primary user (PU) and secondary user (SU) is well studied; in this paper, we explained the problem of spectrum sharing of PU and SU in CRNs, and we introduced sensing delay in communication

Summary

Introduction

Intelligent processing is one of the major advantages of CRN [1]. Due to this feature, these systems possess the capability to learn their environment, increase awareness, and reconfigure themselves . Intelligent [19] wireless communication devices must be adaptive to their environment to be able to perceive their surroundings This can be achieved through the optimization of working limitations and dynamic spectrum access. (i) The proposed method is different from the traditional method in the sense that there is a performance criterion of sharing, and consensus of CRN has been improved in the presence of sensing delay (communication delay) through the communication topology (ii) An agent-based approach is introduced for the CR user’s consensus performance for the first time (iii) Besides, a deep learning power control strategy (reinforcement learning) has been developed along with an agent-based approach altogether for a more efficient performance of sharing control. In the rest of the paper, we discussed the following: overview of CRN, intelligent power control of spectrum sharing, delay performance using a primary sensor network, and CRN consensus criteria under communication delay

An Overview of CRN

A Sensor

Results and Discussion

Conclusions