These rising adoption of Internet of Things (IoT) devices in various applications have placed significant demands on cellular networks, including LTE/5G because they have to provide not only the regular services for human traffic but also meet the tra...
These rising adoption of Internet of Things (IoT) devices in various applications have placed significant demands on cellular networks, including LTE/5G because they have to provide not only the regular services for human traffic but also meet the traffic requirements for these IoT devices. Multiple devices desire to use network resources at the same time during peak hours and overload conditions, generating collisions and reducing the network's effective throughput. The access class barring (ACB) method was offered as the best potential approach in the LTE communication network to overcome this problem. Our suggested approach improves the ACB scheme, by splitting the time of a random-access sensing frame into a number of mini-slots and computing the value of the effective ACB factor within each mini-slot. Simulation results obtained show significant improvement in the reduction of the number of collisions, thereby increasing the throughput of the network. The proposed algorithm shows better performance from conventional ACB algorithms in terms of total service time (TST) which is the time required by all IoT devices in the system to get success. In particular, our proposed algorithm shows 75% improvement in TST, and the simulation results support our claim.