Small data transmission (SDT) is a novel technique that allows IoT devices to transmit their payload data without a pre-allocated radio resource. However, unresolvable SDT collisions occur due to the unpredictability of IoT traffic. In this paper, we propose a semi-contention-free transmission scheme that combines dedicated time-slot allocation through hashing and random access with access control through a dueling deep Q-network. Focusing on uplink IoT traffic for SDT in a NOMA-enabled network with two clusters of devices, we implement the proposed scheme and demonstrate its effectiveness and performance with respect to throughput, latency, and energy consumption through extensive simulations.

The unprecedented surge of massive Internet of things (mIoT) traffic in beyond fifth generation (B5G) communication systems calls for transformative approaches for multiple access and data transmission. While classical model-based tools have been proven to be powerful and precise, an imminent trend for resource management in B5G networks is promoting solutions towards data-driven design. Considering an IoT network with devices spread in clusters covered by a base station, we present in this paper a novel model-free multiple access and data transmission framework empowered by reinforcement learning, designed for power-domain non-orthogonal multiple access networks to facilitate uplink traffic of small data packets. The framework supports two access modes referred to as contention-based and semi-contention-free, with its core component being a policy gradient algorithm executed at the base station. The base station performs access control and optimal radio resource allocation by periodically broadcasting two control parameters to each cluster of devices that considerably reduce data detection failures with a minimum computation requirement on devices. Numerical results, in terms of system and cluster throughput, throughput fairness, access delay, and energy consumption, demonstrate the efficiency and scalability of the framework as network size and traffic load vary.

The burgeoning growth of massive machine-type communication or massive Internet of things traffic in beyond fifth-generation communication systems calls for novel solutions for medium access. In this paper, we propose a reinforcement learning (RL)-based random access scheme for non-orthogonal multiple access (NOMA)-enabled uplink transmissions and investigate how RL can potentially bring benefits for medium access under various network configurations and traffic load conditions. Aiming to mitigate network congestion and enhance overall network performance, we introduce access control and compare network performance of the RL-based access scheme with two benchmark schemes. For performance assessment, we define system and cluster throughput as the key performance metrics. This study provides valuable insights on the feasibility and efficacy of RL-based access control mechanisms across low and high device density scenarios.

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.

To obtain benefits for non-orthogonal multiple access (NOMA) based concurrent transmissions for uplink Internet of things (IoT) traffic in multi-antenna enabled networks, device clustering has been a challenging task. Most previous studies focused on grouping devices into clusters that are served by different beams in multiple input multiple output (MIMO)-NOMA networks. In this paper, we perform an exploratory study on the impact of both intra- and inter-cluster interference on the performance of uplink concurrent transmissions considering that multiple clusters are served by a single beam. For performance assessment, we define two metrics, cluster throughput and transmission latency, and evaluate network performance with various network configurations. The study provides insight on how to configure device clusters and perform access control in order to maximize performance and improve fairness. As devices closer to the base station experience less path attenuation, we introduce distinct access control mechanisms to improve transmissions fairness for concurrent transmissions from difference clusters.

To address the challenges faced by non-orthogonal multiple access (NOMA) for multiple access in beyond fifth gen-eration (B5G) networks, rate-splitting multiple access (RSMA) has newly emerged as a promising approach. In addition to achieving high data rates, RSMA may be used for massive machine-type communication (mMTC)/massive Internet of things (mIoT) applications as well. Although RSMA addresses the user coupling and scheduling overhead problems met by NOMA, it is not clear whether and how RSMA can be applied to uplink mMTC/mIoT traffic. In this study, we explore the applicability of RSMA for uplink traffic and investigate whether there is any benefit to employ RSMA and if yes how this benefit can be achieved. To this end, we propose a novel uplink RSMA scheme with two constituent components, known as inter-device decoding and intra -device decoding, respectively. Through user pairing for inter-device decoding and power allocation for intra-device decoding, we reveal under which circumstances RSMA can bring benefits for uplink traffic.

In massive machine-type communications (mMTC), an immense number of wireless devices communicate autonomously to provide users with ubiquitous access to information and services.The current 4G LTE-A cellular system and its Internet of Things (IoT) implementation, the narrowband IoT (NB-IoT), present appealing options for the interconnection of these wireless devices.However, severe congestion may arise whenever a massive number of highly-synchronized access requests occur.Consequently, access control schemes, such as the access class barring (ACB), have become a major research topic.In the latter, the precise selection of the barring parameters in a real-time fashion is needed to maximize performance, but is hindered by numerous characteristics and limitations of the current cellular systems.In this paper, we present a novel ACB configuration (ACBC) scheme that can be directly implemented at the cellular base stations.In our ACBC scheme, we calculate the ratio of idle to total available resources, which then serves as the input to an adaptive filtering algorithm.The main objective of the latter is to enhance the selection of the barring parameters by reducing the effect of the inherent randomness of the system.Results show that our ACBC scheme greatly enhances the performance of the system during periods of high congestion.In addition, the increase in the access delay during periods of light traffic load is minimal.

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.

Massive content delivery in cellular networks is in the spotlight of the research community as data traffic is increasing at an incredibly fast pace. The existing LTE-A implementation for content broadcast presents several issues such as indoor coverage, along with low energy and spectral efficiency. Therefore, novel systems that provide efficient massive content delivery and reduced energy consumption are needed. In this paper we present a massive content distribution protocol that combines the benefits of cooperative mobile clouds (CMCs) with Random Linear Network Coding (RLNC) through multicast WiFi links. Our main goal is to offload data traffic from the LTE-A link and to reduce the energy consumption at the cooperating UEs. We solve the problem of excessive signaling that oftentimes arises in cooperative approaches by eliminating feedback messages within the CMCs. Instead, we provide a simple but accurate analytic model to correctly configure the number of coded transmissions to be performed within the CMCs. Results show that energy savings of more than 37 percent can be achieved with our protocol when compared to direct content download from the cellular base station. Furthermore, bandwidth utilization at the LTE-A link is sharply reduced.

Using cellular networks for providing machine-to- machine (M2M) connectivity offers numerous advantages regarding coverage, deployment costs, security and management, among others. Nevertheless, having a large number of M2M devices activated simultaneously is difficult to tackle at the evolved Node B, and it causes complications in the connection establishment. The random access channel (RACH) in LTE-A is adequate for handling human-to-human (H2H) communications. However, for the efficient provision of simultaneous H2H/M2M communications, it is necessary to optimize the available access control mechanisms so that network overload is avoided and a better QoS can be offered. Access Class Barring (ACB) has shown to be effective in reducing the number of simultaneous users contending for access. However, it is still not clear how to dynamically adapt its parameters, especially in highly changing scenarios with bursty traffic as it can occur when M2M communications are involved. We propose a dynamic algorithm based on reinforcement learning to adapt the barring rate parameter of ACB. This algorithm can adapt it to different traffic conditions, reducing congestion and hence the number of collisions in the RACH. The results show that our proposed mechanism increases the access success probability for all the users while barely impacting H2H users and other key performance indicators.

The performance evaluation of the random access (RA) procedure in LTE-A has recently become a major research topic as these networks are expected to play a significant role in upcoming 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 sacrifice precision in exchange of simplicity. In this paper, we present an analytic model for the performance evaluation of the LTE-A RA procedure. Using this analytic model, each and every one of the KPIs suggested by the 3GPP can be obtained with a minimal error when compared to the results obtained by simulation. To the best of our knowledge, this is the most accurate analytic model of the LTE-A RA procedure.

Cooperative transmission (CT) enables balanced energy consumption among sensor nodes and mitigates the energy hole problem in wireless sensor networks (WSNs). In typical CT enabled medium access control (MAC) protocols, a source node decides to trigger CT or not based on a residual energy comparison between itself and its relay node. In this paper, we propose a receiver initiated CT MAC protocol, in which the receiving node makes the decision on initiating CT or not based on a tradeoff between performing CT and non-CT. In this way, nodes can avoid idle listening and achieve an extended lifetime. A discrete-time Markov chain (DTMC) model is developed to analyze the performance of CT associated with synchronous CT MAC protocols. Using this DTMC model, the performance of the protocol is evaluated with respect to energy consumption, energy efficiency and network lifetime. Numerical results demonstrate the accuracy of the model and the effectiveness of CT, in contrast to non-CT, as it leads to balanced energy consumption and an optimal network lifetime.

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.

Overhearing and idle listening are two primary sources for unnecessary energy consumption in wireless sensor networks. Although introducing duty cycling in medium access control (MAC) reduces idle listening, it cannot avoid overhearing in a network with multiple contending nodes. In this paper, we propose an event-triggered sleeping (ETS) mechanism for synchronous duty-cycled (DC) MAC protocols in order to avoid overhearing when a node is not active. This ETS mechanism applies to any synchronous DC MAC protocols and makes them more energy efficient. Furthermore, we develop a two dimensional discrete time Markov chain model to evaluate the performance of the proposed ETS mechanism by integrating it to a popular synchronous DC MAC protocol namely sensor-MAC. Using the developed model, energy consumption, energy efficiency and network lifetime are calculated. Numerical results obtained through both analytical model and discrete-event simulations demonstrate the effectiveness of the ETS mechanism, represented by lower energy consumption, higher energy efficiency and longer lifetime when compared with the conventional control packet triggered sleeping mechanism.

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 constant evolution of mobile-phone traffic demands for novel networking solutions especially focused on indoor environment. In this context, the use of femtocells, i.e., cells with very limited coverage area, has been proposed. In this paper, a femtocell network with hybrid access control mode is considered. The activity profile of the Femtocell Users (FUs) is modeled to compute the maximum achievable throughput and the consumed energy per successfully transmitted data bit by the Macrocell Users (MUs), depending on which set of channels are operated in open access mode, i.e., which channels can be used by MUs. Thus, it is identified how many and which channels must be operated in open access mode, depending on the physical capacities of the channels and the amount of time these channels are not occupied by FUs. The results motivate the need for novel resource management schemes which can dynamically adapt the set of open access channels to the network conditions.

Many studies on Internet Service Provider (ISP) interconnection make simplifying assumptions on the implementation of the service provision. Our work explicitly models the ISP service that is provided to users that run peer-to-peer applications and it analyses the behavior of competing ISPs. The ISPs have agreed to peer each other and each ISP has purchased transit service from one Internet Backbone Provider. The quality of service, the equilibrium prices and the market shares that the competition game yields are computed by means of our model. Our work assesses the strategy of an ISP which provisions its transit link against a competing ISP in terms of competitive advantage and social welfare. And it assesses the effect of the entrance of more competing ISPs.

In this paper we consider a cognitive radio network in a heterogeneous multi-channel scenario where channels are different in terms of achievable bit rates. Upon arrival licensed users (PUs) will occupy first the channels with higher achievable rates. As a result, secondary users (SUs) will see a set of channels which are different not only in their physical capacities but also in the activity profile of PUs. Consequently, from the point of view of the effective throughput that SUs can obtain in each channel there exists a trade-off between the rate they can obtain in a certain channel and the amount of time they can use it. We develop a model that allows to solve this trade-off by computing the maximum effective throughput that SUs can achieve in each channel.

Cognitive Radio (CR) networks are envisaged as the key technology to realize dynamic spectrum access and solving the scarcity of radio spectrum. Having a temporal characterization of the spectrum white spaces in the primary network is a key element for studying and designing radio resource management mechanisms in CR networks. In that sense, most of the studies in the literature rely on an ON-OFF model with exponentially distributed on and off times. The usage of that model, however, is principally based on its analytical tractability. In this paper we propose a versatile Markovian model for the duration of the spectrum white spaces. Our model builds on a simple model of the channel holding time (CHT) in the primary network and then matrix-analytic techniques are applied to derive and analyze the duration of the white spaces. Despite its simplicity, the proposed approach is proven to be able to model very accurately scenarios where the CHT distribution is of a more complex type not amenable to mathematical analysis. Our numerical results show that the duration of the white spaces exhibits a low sensitivity to the distribution of the channel holding time beyond the mean.

This paper studies the robustness under high overloads of an extension of the Virtual Partitioning (VP) admission policy in multiservice cellular networks that carry streaming and elastic traffic. The VP policy is compared with the Multiple Fractional Guard Channel (MFGC) policy. The main contributions of the paper are the study of a new design method and the integration of streaming and elastic traffic.

Wireless technologies have rapidly evolved and are becoming ubiquitous. An increasing number of users attach to the Internet using these technologies; hence the performance of these wireless access links is a key point when considering the performance of the whole Internet. In this paper we present a measurement-based analysis of the performance of an IEEE 802.16 (WiMAX) client and an UMTS client. The measurements were carried out in a controlled laboratory. The wireless access links were loaded with traffic from a multi-point videoconferencing application and we measured three layer-3 metrics (one-way-delay, IP-delay-variation and packet loss ratio). Additionally we estimate the performance of a WiFi and Ethernet client as a reference. Our results show that Ethernet and WiFi have comparable performances. Both the WiMAX and the UMTS links exhibited an asymmetric behavior, with the uplink showing an inferior performance. We also assessed the causes of the discretization which appears in the jitter distributions of these links.

We evaluate different admission control (AC) policies in various mobile cellular scenarios with multimedia support and QoS guarantees defined by the expected blocking probabilities for new call set up and handover requests. For each of the studied policies we determine the maximum calling rate that can be offered to the system while satisfying the QoS objective, using a hill-climbing algorithm. This approach drastically reduces the computational complexity of the optimization process. Additionally we study how sensitive these policies are to the tolerance of system parameters and to overloads. In regard to the system capacity, preliminary results show that policies of the trunk reservation class outperform policies that produce a product-form solution and the improvement ranges approximately between 5 and 15%.