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 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.

In this letter, we propose an analytical model to evaluate the performance of the S-MAC protocol. The proposed model improves the accuracy of previous models in two aspects. First, it incorporates the dependence among the nodes within a cluster by defining a DTMC that models the number of active nodes, whereas the previous models considered that nodes were mutually independent. Second, it proposes new methods to calculate packet delay and energy consumption. The analytical model is validated through discrete-event based simulations. Numerical results demonstrate that the proposed analytical model and methods yield accurate results under realistic assumptions.

To enable concurrent transmissions for Internet of Things (IoT) traffic in multiantenna beyond fifth generation networks, nonorthogonal multiple access (NOMA) mechanisms appear as a promising approach. For NOMA-enabled transmissions, IoT devices are grouped into clusters in order to exploit the benefit of concurrent transmissions. However, how to facilitate transmissions from both intra- and intercluster is not an easy task and the performance of such concurrent transmissions is so far not well understood from a mathematical point of view, especially when error-prone channel conditions are considered. In this article, we propose two random access schemes which enable intra- and intercluster concurrent transmissions for uplink IoT traffic with and without access control. To assess the performance of such systems, we develop two analytical models based on discrete-time Markov chains (DTMCs) that mimic the behavior of such transmissions. Our models deal with cluster-level performance considering dynamic packet arrivals and the transmissions from devices belonging to the same or different clusters. Through extensive simulations, we validate the accuracy of the analytical models and evaluate the system- and cluster-level performance in terms of throughput and delay under various traffic load conditions and network configurations.

Sensing is a fundamental aspect in cognitive radio networks and one of the most complex issues. In the design of sensing strategies, a number of tradeoffs arise between throughput, interference to primary users, and energy consumption. This paper provides several Markovian models that enable the analysis and evaluation of sensing strategies under a broad range of conditions. The occupation of a channel by primary users is modeled as alternating idle and busy intervals, which are represented by a Markov phase renewal process. The behavior of secondary users is represented mainly through the duration of transmissions, sensing periods, and idle intervals between consecutive sensing periods. These durations are modeled by phase-type distributions, which endow the model with a high degree of generality. Unlike our previous work, here the source of secondary users is nonsaturated, which is a more practical assumption. The arrival of secondary users is modeled by the versatile Markovian arrival process, and models for both finite and infinite queues are built. Furthermore, the proposed models also incorporate a quite general representation of the resumption policy of an SU transmission after being interrupted by PUs activity. A comprehensive analysis of the proposed models is carried out to derive several key performance indicators in cognitive radio networks. Finally, some numerical results are presented to show that, despite the generality and versatility of the proposed models, their numerical evaluation is perfectly feasible.

Wireless Sensor Networks (WSN) have experienced an important revitalization, particularly with the arrival of Internet of Things applications. In a general sense, a WSN can be composed of different classes of nodes, having different characteristics or requirements (heterogeneity). Duty-cycling is a popular technique used in WSN, that allows nodes to sleep and wake up periodically in order to save energy. We believe that the modeling and performance evaluation of heterogeneous WSN with priorities operating in duty-cycling, being of capital importance for their correct design and successful deployment, have not been sufficiently explored. The present work presents a performance evaluation study of a WSN with these features. For a scenario with two classes of nodes composing the network, each with a different channel access priority, an approximate analytical model is developed with a pair of two-dimensional discrete-time Markov chains. Note that the same modeling approach can be used to analyze networks with a larger number of classes. Performance parameters such as average packet delay, throughput and average energy consumption are obtained. Analytical results are validated by simulation, showing accurate results. Furthermore, a new procedure to determine the energy consumption of nodes is proposed that significantly improves the accuracy of previous proposals. We provide quantitative evidence showing that the energy consumption accuracy improvement can be up to two orders of magnitude.