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.

Machine-type communications (MTC) is an emerging technology that boosts the development of the Internet of Things by providing ubiquitous connectivity and services. Cellular networks are an excellent choice for providing such hyper-connectivity thanks to their widely deployed infrastructure, among other features. However, dealing with a large number of connection requests is a primary challenge in cellular-based MTC. Severe congestion episodes can occur when a large number of devices try to access the network almost simultaneously. Extended access barring (EAB) is a congestion control mechanism for MTC that has been proposed by the 3GPP. In this paper, we carry out a thorough performance analysis of EAB and show the limitations of its current specification. To overcome these limitations, we propose two enhanced EAB schemes: the combined use of EAB and access class barring, and the introduction of a congestion avoidance backoff after the barring status of a UE is switched to unbarred. It is shown through extensive simulations that our proposed solutions improve the key performance indicators. A high successful access probability can be achieved even in heavily congested scenarios, the access delay is shortened and, most importantly, the number of required preamble retransmissions is reduced, which results in significant energy savings. Furthermore, we present an accurate congestion estimation method that solely relies on the information available at the base station. We show that this method permits a realistic and effective implementation of EAB.

We propose and analyze a business model for a set of operators that use the same physical network. Each operator is entitled to a share of a network operated by an Infrastructure Provider (InP) and uses network slicing mechanisms to request network resources as needed for service provision. The network operators become Network Slice Tenants (NSTs). The InP performs the resource allocation based on a vector of weights chosen selfishly by each NST. The weights distribute the NST's share of resources between its subscribers in each cell. We model this relationship as a game propose a solution for the Nash equilibrium in which each NST chooses weights equal to the product of its share by the ratio between the total number of subscribers in the cell and the total number of subscribers in the network. We characterize the proposed solution in terms of subscription ratios and fractions of subscribers, for different cell capacities and user sensitivities. The proposed solution provides the exact values for the Nash equilibrium if the cells are homogeneous in terms of normalized capacity, which is a measure of the total amount of resources available in the cell. Otherwise, if the cells are heterogeneous, it provides an accurate approximation. We quantify the deviation from the equilibrium and conclude that it is highly accurate.

The case for a competitive market operated by a Mobile Network Operator (MNO) and a Mobile Virtual Network Operator (MVNO) is analysed in the paper. The resource that is leased by the MNO to the MVNO is spectrum. The MNO and the MVNO compete "a la Bertrand" posting subscription prices and the mobile users may choose to subscribe to one operator. The scenario is modeled by a three-level game comprising a bargaining game, which models the spectrum leasing by the MNO; a competition game, which models the price competition between the MNO and the MVNO; and a subscription game, which models the subscription choice by the mobile users, and the outcome of which may be either not to subscribe, to subscribe to the MNO or to subscribe to the MVNO. The game is solved through backward induction, and each level has a specific solution concept: Shapley value, for the bargain; Nash equilibrium, for the competition; and Wardrop equilibrium, for the subscription. The paper assesses which conditions lead to an equilibrium where the competition does take place, which are expressed as restrictions for the spectrum leasing price agreed at the bargaining, and the spectrum efficiency improvement achieved by the MVNO. Furthermore, it argues that the amount of the leased spectrum should be fixed exogenously in order to achieve optimal user and social welfares.