M2M communication performance for a noisy channel based on latency-aware source-based LTE network measurements

The phrase "Machine-to-Machine" (M2M) communication has gained widespread usage owing to the growing understanding of the Internet of Things. In the upcoming years, it is anticipated that the number of M2M devices would rise significantly due to the growth in M2M applications. Enhancing cellular networks to manage M2M and human-to-human (H2H) conversations is an ongoing endeavor. Integrating M2M communication into regular H2H communication is a key objective because of the steady increase in the number of M2M devices that exhibit the unique properties of M2M traffic. It is anticipated that H2H and M2M communications will make use of the same LTE resource in order to maximize its efficiency. Therefore, in order to manage an LTE network system that includes M2M devices and H2H users, an effective resource scheduler is required. Using a priority queuing model, this study develops analytical formulas to assess the suggested programs' performance based on average waiting times, average system delays, and average system numbers for M2M and H2H users. Based on the data analysis, it is feasible to maintain suitable levels of quality of service (QoS) for H2H clients while providing good QoS to M2M consumers. Users in the M2M queue have their demands addressed by using a relaxation strategy. Quality of Service for H2H consumers' delay-sensitive traffic a system is utilized to classify H2H traffic into two basic types. Only Level 2 H2H should apply the relax technique. The findings demonstrate that the suggested solutions improve M2M performance and efficiently manage QoS for H2H services.