Enhanced cooperative access class barring and traffic adaptive radio resource management for M2M communications over LTE-A

Abstract

We propose enhanced cooperative access class barring (ECACB) and traffic adaptive radio resource management (TARRM) for M2M communications over LTE-A. We use the number of Machine-Type Communication (MTC) devices that attach to an eNB, which is the base station of LTE-A, as a criterion to determine the probability that an MTC device may access the eNB. In this way, we can have a better set of access class barring parameters than CACB, which is the best available related work, so as to reduce random access delay experienced by an MTC device or user equipment (UE). After an MTC device successfully accesses an eNB, the eNB allocates radio resources for the MTC device based on the random access rate of the MTC device and the amount of data uploaded or downloaded by the MTC device. In addition, we use the concept from cognitive radio networks that when there are unused physical resource blocks (PRBs) of UEs, the eNB can schedule MTC devices to use these PRBs to enhance network throughput. Simulation results show that the proposed ECACB's average (worst) access delay of UEs is 33.19% (29.89%) lower than CACB's. Its average (worst) access delay of MTC devices is 12.15% (15.1%) lower than that of CACB. Its average (worst) throughput from UEs is 20.93% (26.44%) higher than that of CACB. Its average (worst) throughput from MTC devices is 19.95% (12.25%) higher than that of CACB. The proposed ECACB+TARRM's average (worst) throughput from UEs is 26.16% (31.42%) higher than CACB's. Its average (worst) throughput from MTC devices is 25.11% (20.76%) higher than that of CACB. To the best of our knowledge, no existing approach integrates access class barring with radio resource management for M2M communications over LTE-A.