Angeliki Alexiou

Uplink scheduling for Machine-to-Machine communications in LTE-based cellular systems

Cellular systems are expected to play a fundamental role in the future Machine-to-Machine (M2M) networks, which could inherit crucial benefits from the former, such as ubiquitous coverage and global internetworking. However, unique features of M2M communications, such as the larger number of connected devices and the diversity of applications, require specific enhancements to these cellular systems. Because of the nature of most of M2M applications, a large number of uplink transmissions is expected, rendering uplink scheduling as an essential issue towards supporting M2M communications via cellular networks. We propose two scheduling schemes for the uplink of LTE-based cellular systems, which take into account both the channel conditions and the maximum allowed delay of each device requesting to be served. In contrast to other scheduling algorithms no classes of devices are formed, but the exact delay constraint of each device is considered, approaching the requirements of M2M communications in a more realistic way. This way, the number of effectively served requests increases, while it becomes possible to exploit the exact delay constraints in order to put the devices in sleep modes, until next transmissions. It is also shown that dividing the devices into a limited number of QoS classes deteriorates the systems performance.

Analytical modelling and performance evaluation of realistic time-controlled M2M scheduling over LTE cellular networks

Supporting emerging machine-to-machine (M2M) communications over Long-term Evolution (LTE)/LTE Advanced cellular networks in an efficient way will be beneficial for both telecommunication communities. The first step to migrate to an M2M-enabled cellular standard is to provide these new services through the existing architectures and protocols, while maintaining seamless backward compatibility. To this end, we thoroughly examined a key LTE Medium Access Control entity, which is the packet scheduler, and proposed solutions based on the time-controlled M2M feature, to deal with the diverse M2M traffic characteristics and quality-of-service requirements. Starting from the single M2M class case, we extended our study to more realistic scenarios, involving more M2M classes with diverse quality-of-service requirements. We defined analytical models for predicting the system performance on the basis of queueing theory concepts and considered the interaction between classes with different priorities. The proposed analytical models are validated through extensive system-level simulations. On the basis of the insight obtained from our analytical approach, we modified an existing scheduling algorithm to improve the performance of low-priority M2M device groups, and we demonstrated its superior performance both experimentally and analytically. Copyright

Evolution of packet scheduling for Machine-Type communications over LTE: Algorithmic design and performance analysis

Providing LTE connectivity to emerging Machine-to-Machine (M2M) applications imposes several challenges to the operation and optimization of current and future 3GPP mobile broadband standard releases. Scheduling in an efficient way M2M traffic over the existing LTE MAC infrastructure is decisive for the smooth evolution towards an M2M-enabled LTE system. The large number of connecting devices compared to classical LTE terminals, and their vastly diverse quality-of-service requirements, call for the design of new packet scheduling schemes tailored to the M2M paradigm. To this end, we propose low complexity and signaling scheduling policies which periodically grant access to the M2M devices. In particular, we first propose an analytical model for predicting the QoS performance of M2M services when the fixed periodic scheduling algorithm is employed. Next we propose a modification to this scheme, which exploit queueing-dynamics and finally we examine QoS-differentiation issues when devices are grouped into clusters. Interesting performance-complexity trade-offs are exposed. The results of our study may aid the system designer in tuning and optimizing M2M traffic scheduling.

UltraDense Networks: The New Wireless Frontier for Enabling 5G Access

The extreme traffic load that future wireless networks are expected to accommodate requires a rethinking of the system design. The initial estimations indicate that, unlike the evolutionary path of previous cellular generations that was based on spectral efficiency improvements, the most substantial amount of future system performance gains will be obtained by means of network infrastructure densification. By increasing the density of operator-deployed infrastructure elements along with incorporation of user-deployed access nodes (ANs) and mobile user devices acting as infrastructure prosumers, having one or more ANs exclusively dedicated to each user is expected to become feasible, introducing the ultradense network (UDN) paradigm. Although it is clear that UDNs are able to take advantage of the significant benefits provided by proximal transmissions and increased spatial reuse of system resources, large node density and irregular deployment introduce new challenges, mainly due to the interference environment characteristics that are vastly different from previous cellular deployments. This article attempts to provide insights on fundamental issues related to UDN deployment, such as determining the infrastructure density required to support the given traffic load requirements and the benefits of network-wise coordination, demonstrating the potential of UDNs for fifth-generation (5G) wireless networks.

Wireless World 2020: Radio Interface Challenges and Technology Enablers

Future pervasive communication system requirements for two to three orders of magnitude capacity improvement, flexible, fast deployment, and cost/energy efficiency are expected to revolutionize the way we design and use wireless networks. From a network infrastructure perspective, the emphasis is placed on achieving ubiquitous, real-time high data rate communications ?anytime-anywhere,? including at cell-edge, through Small Cell Network architectures and Heterogeneous Cellular Networks (HetNets). From a pervasive systems? perspective, the vision of the Internet of Things suggests the integration between ubiquitous computing and wireless communications targeting a reliable connectivity of things, i.e., computers, sensors, and everyday objects equipped with transceivers. From a backhaul bandwidth, network resource sharing, and optimization perspective, cloud-based processing and radio access network virtualization provide a revolutionary approach toward balancing the degree of centralization of physical and virtual resources management. In this article we analyze these three trends, present key technology enablers, and assess suitable performance merits in an effort to set the scene for the wireless evolution in the era beyond 2020.

Editorial: IEEE Communications Surveys & Tutorials Machine-to-Machine Technologies & Architectures

WE ARE currently witnessing the emergence of an internet in which industrial as well as consumer objects are able to connect to the Internet, tweet or be queried. Whilst the impact onto economies and societies around the world is undisputed, the technologies facilitating such a ubiquitous connectivity have struggled so far and have only recently commenced to take shape. A cornerstone to this connectivity landscape is and will be Machine-to-Machine (M2M). M2M generally refers to Information and Communications Technologies (ICT) able to measure, deliver, digest and react upon information in an autonomous fashion, i.e. with no or really minimal human interaction during deployment, configuration, operation and maintenance phases. Examples of M2M technologies are: telemetry readings of a vehicle fleet; measurements of the health state of the elderly; occupancy measurements of parking in cities; remote metering of gas consumption; etc. M2M is increasingly used in industries for i) repetitive measurements, like delivering gas meter data once a day; and ii) time critical jobs with decisions taken within a few milliseconds based on the input of a large amount of data. Indeed, whilst M2M is mainly about the connectivity infrastructure, the value it unlocks relates to the Big Data potential. The return-of-investment benefits stem from the provision of i) real-time, ii) scalable, iii) ubiquitous, iv) reliable and v) heterogeneous Big Data, and thus associated opportunities. To facilitate said opportunities, M2M systems bear very specific and unparalleled engineering challenges in both research and development. Prime design drivers here are the need for virtually zero-outage, immediate-response and highefficiency to support reliable, green, long-living and delayconstrained M2M applications. The aim of this special issue on machine-to-machine thus was to collect from industrial and academic players tutorials and surveys related to M2M protocols, technologies, architectures and key functionalities. We have received a total of 25 submissions from which 6 were finally accepted for this special issue. The papers span from M2M access technologies, to security issues, research

Spatial coordination strategies in future ultra-dense wireless networks

Ultra network densification is considered a major trend in the evolution of cellular networks, due to its ability to bring the network closer to the user side and reuse resources to the maximum extent. In this paper we explore spatial resources coordination as a key empowering technology for next generation (5G) ultra-dense networks. We propose an optimization framework for flexibly associating system users with access nodes in a densely deployed network, opting for the exploitation of densification and the control of overhead signaling. Combined with spatial precoding strategies, network resources management approaches are designed, reflecting various features, namely local vs. global channel state information knowledge exploitation, centralized vs. distributed implementation, and non-cooperative vs. joint multi-node data processing. We apply these strategies to future dense network setups, and explore the impact of critical network parameters, that is, the densification levels of access nodes, the density of users requesting service, and the power budget constraints. We demonstrate that spatial resources coordination is a key factor for capitalizing on the gains of ultra dense network deployments.

Optimal user association for Massive MIMO empowered ultra-dense wireless networks

Ultra network densification and Massive MIMO are considered major 5G enablers since they promise huge capacity gains by exploiting proximity, spectral and spatial reuse benefits. Both approaches rely on increasing the number of access elements per user, either through deploying more access nodes over an area or increasing the number of antenna elements per access node. At the network-level, optimal user-association for a densely and randomly deployed network of Massive MIMO empowered access nodes must account for both channel and load conditions. In this paper we formulate this complex problem, report its computationally intractability and reformulate it to a plausible form, amenable to acquire a global optimal solution with reasonable complexity. We apply the proposed optimization model to typical ultra-dense outdoor small-cell setups and demonstrate: (i) the significant impact of optimal user-association to the achieved rate levels compared to a baseline strategy, and (ii) the optimality of alternative network access element deployment strategies.

Partitioning of Distributed MIMO Systems Based on Overhead Considerations

Distributed-Multiple Input Multiple Output (D-MIMO) networks is a promising enabler to address the challenges of high traffic demand in future wireless networks. A limiting factor that is directly related to the performance of these systems is the overhead signaling required for distributing data and control information among the network elements. In this paper, the concept of orthogonal partitioning is extended to D-MIMO networks employing joint multi-user beamforming, aiming to maximize the effective sum-rate, i.e., the actual transmitted information data. Furthermore, in order to comply with practical requirements, the overhead subframe size is considered to be constrained. In this context, a novel formulation of constrained orthogonal partitioning is introduced as an elegant Knapsack optimization problem, which allows the derivation of quick and accurate solutions. Several numerical results give insight into the capabilities of D-MIMO networks and the actual sum-rate scaling under overhead constraints.

Energy efficient AF relaying under error performance constraints with application to M2M networks

In this paper, we present a machine to machine (M2M) communication scheme, where the source communicates with the corresponding destination node with the help of one or more relay nodes. The objective is to minimize the total consumed power at the relays under specific performance constraints, while also considering the individual power limitations of each relay. In order to solve the original problem, we transform into an equivalent convex optimization problem. Simulations results are provided to validate the theoretical analysis and to illustrate the desired tradeoff between error performance and total energy consumption.