Massive Random Access of Machine-to-Machine Communications in LTE Networks: Modeling, Throughput Maximization and Delay Minimization


Student thesis: Doctoral Thesis

View graph of relations


Related Research Unit(s)


Awarding Institution
Award date6 Sept 2019


Machine-to-Machine (M2M) communications is a key enabling technology for the emerging Internet of Things paradigm, which offers pervasive wireless connectivity for autonomous devices with minimum human intervention. It has been identified by the Third-Generation Partnership Project (3GPP) as a new service type to be supported by the Long Term Evolution (LTE) networks. However, due to the explosive growth of M2M markets, thousands of Machine-Type Devices (MTDs), e.g., sensors and actuators, will be deployed in each LTE cell. With many MTDs attempting to initiate connections with the network, the deluge of access requests will cause severe congestion with intolerably low access efficiency. How to efficiently accommodate the access of a massive number of MTDs has become a significant challenge for supporting M2M communications over LTE networks.

A great deal of works have been done for modeling and evaluating the access performance of M2M communications in LTE networks. The existing models, however, either ignore the queueing behavior of each MTD or become unscalable in the massive access scenario, making it extremely difficult to further study how to optimize the access performance by properly tuning system parameters, e.g., backoff parameters. The crucial effect of data transmissions on the access performance of MTDs is also little understood.

This thesis is devoted to modeling and optimizing the access throughput and access delay performance of massive random access of M2M communications in LTE networks. The study begins by proposing a novel double-queue model for each MTD, which can both incorporate the queueing behavior of each MTD and be scalable in the massive access scenario. The proposed model also captures the essence of the connection-based random access in LTE networks, where a connection is first established between a device and the base station before the device starts to transmit its data packets.

Based on the proposed double-queue model, the access throughput performance and the access delay performance are further characterized and optimized with the assumption that the data transmission rate is sufficiently large, i.e., each MTD with a successful access request can always clear its data queue in one time slot. The maximum access throughput and corresponding optimal backoff parameters, including the access class barring (ACB) factor and the uniform backoff (UB) window size, are derived as explicit functions of key system parameters. Explicit expressions of the minimum mean access delay and the corresponding optimal ACB factor are also obtained. The analysis shows that with a sufficiently large data transmission rate, the maximum access throughput is solely determined by the number of preambles, while the minimum mean access delay further depends on the network size and the traffic input rate of each MTD.

To further evaluate the effect of limited data transmission resources on the access performance, the analysis is extended to incorporate a finite data transmission rate, with which it may take more than one time slot for MTDs to clear their data queues. The maximum access throughput and the corresponding optimal ACB factor are both obtained as explicit functions of the data transmission rate. The analysis reveals that the maximum access throughput is a monotonic increasing function of the data transmission rate, which becomes zero if the data transmission rate is too small. It is also shown that the crucial resource tradeoff between access and data transmission is determined by the time slot length. To further optimize the access throughput performance, the optimal time slot length for maximizing the normalized maximum access throughput is characterized. The analysis in this thesis sheds important light on the practical system design for supporting massive access of M2M communications in LTE.