Collision Probability Estimation in 5G Networks

Abstract

5G and Cognitive Radio (CR) are rising technologies to meet mobile data traffic of upcoming wireless networks, 5G networks have been generally mounted with the help of Cognitive base station (BTS) and Cognitive radio terminals (CRT). In 5G networks band is divided into sub band for efficient utilization, providing better services towards user. The term cognitive radio terminals (CRT) are responsible for efficient utilization of radio band with the collaboration with cognitive base station (BTS). Cognitive terminals consist of dual channel, Data channel (DCH) and Reporting channel (RCH). To meet the traffic growth all over the next phase, SG mobile networks are likely to attain higher capacity increases matched to previous networks, with significantly higher-speed data rates. This aim can be achieved with cell intelligent utilization of the band and reduction in collision and loss probability, active and sleep mode is helpful the requirement of 5G networks. CBS is responsible to configure the cognitive terminal for active and sleep mode base on their decision for improve energy efficiency and system performs. The terminals which work actively is well-known as Quality base activation (QBA). Collision probability is a one of the key challenges in 5G networks which degrades the preforms of network, reporting time of packet length must be synchronous with transmitter and receiver time span may improve preforms, threshold and feedback-error is defined to overcome the collision loss probability with cognitive terminals. Node selection approach works on Active and sleep mode of cognitive terminals may improve system performs, which improves the collision probability loss and provides the significant improvement in throughput of system. Software such as MATLAB and (Google Colabatory name as "Google colab") demonstrate the outcome. Machine learning method such as "Support Vector Machine" (SVM) platform is used to find the threshold value for cognitive terminal for optimal result of reporting loss probability for effective system.