Over the coming years, it is expected that the number of machine-to-machine (M2M) devices that communicate through LTE-A networks will rise significantly for providing ubiquitous information and services. However, LTE-A was devised to handle human-to-human traffic, and its current design is not capable of handling massive M2M communications. Access Class Barring (ACB) is a congestion control scheme included in the LTE-A standard that aims to spread the accesses of user equipments (UEs) through time so that the signaling capabilities of the eNB are not exceeded. Notwithstanding its relevance, the potential benefits of the implementation of ACB are rarely analyzed accurately. In this paper, we conduct a thorough performance analysis of the LTE-A random access channel (RACH) and ACB as defined in 3GPP specifications. Specifically, we seek to enhance the performance of LTE-A in massive M2M scenarios by modifying certain configuration parameters and by the implementation of ACB.We observed that ACB is appropriate for handling sporadic periods of congestion. Concretely, our results reflect that the access success probability of M2M UEs in the most extreme test scenario suggested by the 3GPP improves from approximately 30%, without any congestion control scheme, to 100% by implementing ACB and setting its configuration parameters properly.

The number of devices that communicate through the cellular system is expected to rise significantly over the coming years. But cellular systems, such as LTE-A, were designed to handle human-to-human traffic. Hence, they are not suitable for managing massive machine-to-machine communications. Therefore, additional congestion control methods must be developed and evaluated. Up to date, access class barring (ACB) and extended access barring (EAB) methods are the preferred solutions for reducing congestion in the access channels of the evolved NodeB. These methods are based on restricting the access of certain classes of UEs, so the system capacity is not exceeded. Due to the high complexity of the LTE-A system, evaluating its performance is not straightforward. Specifically, a large number of variables, coexistent mechanisms, and test scenarios make it difficult to identify the network parameters that enhance performance. In this paper, we analyze the ACB method in highly congested environments. For this, we evaluate the effect of ACB parameters (barring rates and barring times) by means of several key performance indicators (KPI) such as delay, energy consumption (preamble transmission attempts required) and success probability. We observed that ACB is appropriate for handling sporadic congestion intervals in LTE-A networks.

The performance evaluation of the random access (RA) in LTE-A has recently become a major research topic as these networks are expected to play a major role in future 5G networks. Up to now, the key performance indicators (KPIs) of the RA in LTE-A have been obtained either by performing a large number of simulations or by means of analytic models that, oftentimes, sacrifice precision in exchange for simplicity. In this paper, we present an analytical model for the performance evaluation of the LTE-A RA procedure that incorporates the access class barring (ACB) scheme. By means of this model, each and every one of the KPIs suggested by the 3GPP can be obtained with minimal error when compared with results obtained by simulation. To the best of our knowledge, this paper presents the most accurate analytical model, which can be easily adapted to incorporate modifications of network parameters and/or extensions to the LTE-A system. In addition, our model of the ACB scheme can be easily incorporated to other analytic models of similar nature without further modifications.

Although grant-based mechanisms have been a predominant approach for wireless access for years, the additional latency required for initial handshake message exchange and the extra control overhead for packet transmissions have stimulated the emergence of grant-free (GF) transmission. GF access provides a promising mechanism for carrying low and moderate traffic with small data and fits especially well for massive machine type communications (mMTC) applications. Despite a surge of interest in GF access, how to handle heterogeneous mMTC traffic based on GF mechanisms has not been investigated in depth. In this paper, we propose a priority enabled GF access scheme which performs dynamic slot allocation in each 5G new radio subframe to devices with different priority levels on a subframe-by-subframe basis. While high priority traffic has access privilege for slot occupancy, the remaining slots in the same subframe will be allocated to low priority traffic. To evaluate the performance of the proposed scheme, we develop a two-dimensional Markov chain model which integrates these two types of traffic via a pseudo-aggregated process. Furthermore, the model is validated through simulations and the performance of the scheme is evaluated both analytically and by simulations and compared with two other GF access schemes.

An enhanced version of the movement-based location update with selective paging strategy proposed by Akyildiz, Ho and Lin (see IEEE/ACM Trans. Networking, vol.4, p.629-38, 1996) is presented. Although the terminal paging cost is slightly increased, a significant reduction in the location update cost is achieved. The net effect is, for low call-to-mobility ratio, a saving of around 10%-15% in the total cost (location+paging) per call arrival is achieved. Our proposal can be easily implemented in real cellular systems.

The deployment of machine-type communications (MTC) together with cellular networks have great potential to create the ubiquitous Internet of Things environment. Nevertheless, the simultaneous activation of a large number of MTC devices (UEs) is a situation difficult to manage at the evolved Node B (eNB). The knowledge of the joint probability distribution function (PDF) of the number of successful and collided access requests within a random access opportunity (RAO) is a crucial piece of information for contriving congestion control schemes. A closed-form expression and an efficient recursion to obtain this joint PDF are derived in this paper. Furthermore, we exploit this PDF to design estimators of the number of contending UEs in a RAO. Our numerical results validate the effectiveness of our formulation and show that its computational cost is considerably lower than that of other related approaches. In addition, our estimators can be used by the eNBs to implement highly efficient congestion control methods.