Enhancing performance of M2M random access in 3GPP LTE networks

The proliferation of smart devices that can autonomously connect and exchange information over the internet has given rise to a new dynamic area of research, Machine-to-Machine (M2M) communications. M2M refers to networks, which connect devices together and enable them to make smart decisions based on the generated and/or transferred data. The most viable option of implementing M2M communications is over cellular networks. These networks are designed for Human-to-Human (H2H) communication, which is characterized by smaller number of connection requests and longer connection times as compared to the thousands of M2M that will try to connect simultaneously to transmit only a minimum amount of data. When such enormous number of devices is introduced, the existing network will be exposed to high collision rate and extreme connection delays. In LTE, the uplink resources for random access (RA) procedure will be insufficient, and the number of successfully connected devices will bottom out. 3GPP has proposed some solutions to overcome the congestion during the random access procedure. Access Class Barring (ACB) is the most efficient method which increases the access success rate, but it has a disadvantage in terms of access delay. In this paper, we propose to control the ACB parameters adaptively in order to maximize the use of RA resources. Different from other existing methods, our method is based on predicting the number of accessing devices at the beginning of each RA slot and then adjusting the ACB parameters accordingly. Our results show that, the proposed scheme improves delay up to nearly 70% and converges to optimal throughput.