Gian Michele Dell'Aera

Flexible dynamic coordinated scheduling in virtual-RAN deployments

Using Coordinated Scheduling (CS), eNodeBs in a cellular network dynamically agree on which Resource Blocks (not) to use, so as to reduce the interference, especially for cell-edge users. This paper describes a software framework that allows dynamic CS to occur among a relatively large number of nodes, as part of a more general framework of network management devised within the Flex5Gware project. The benefits of dynamic CS, in terms of spectrum efficiency and resource saving, are illustrated by means of simulation and with live measurements on a prototype implementation using virtualized eNodeBs.

Scalability and energy efficiency of Coordinated Scheduling in cellular networks towards 5G

Coordinated Scheduling (CS) is one of the main techniques to control inter-cell interference in present (4G) and future (5G) cellular networks. We show that coordination of a cluster of nodes can be formulated as an optimization problem, i.e., placing the Resource Blocks in each nodes subframe with the least possible overlapping with neighboring nodes. We provide a clever formulation, which allow optimal solutions to be computed in clusters of ten nodes, and algorithms that compute good suboptimal solutions for clusters of several tens of nodes, fast enough for a network to respond to traffic changes in real time. This allows us to assess the relationship between the scale at which CS is performed and its benefits in terms of network energy efficiency and cell-edge user rate. Our results show that optimal CS allows a significant protection of cell-edge users. Moreover, this goes hand-in-hand with a significant reduction in the number of allocated Resource Blocks, which in turn allows an operator to reduce its energy consumption. Both benefits actually increase with the size of the clusters.

Distributed Downlink Power Control for Dense Networks With Carrier Aggregation

Given the proven benefits cell densification brings in terms of capacity and coverage, it is certain that 5G networks will be even more heterogeneous and dense. However, as smaller cells are introduced in the network, interference will inevitably become a serious problem as they are expected to share the same radio resources. Another central feature envisioned for future cellular networks is carrier aggregation (CA), which allows users to simultaneously use several component carriers of various widths and frequency bands. By exploiting the diversity of the different carriers, CA can also be used to effectively mitigate the interference in the network. In this paper, we leverage the above key features of next-generation cellular networks and formulate a downlink power setting problem for the different available carriers. Using game theory, we design a distributed algorithm that lets cells dynamically adjust different transmit powers for the different carriers. The proposed solution greatly improves network performance by reducing interference and power consumption, while ensuring coverage for as many users as possible. We compare our scheme with other interference mitigation techniques, in a realistic large-scale scenario. Numerical results show that our solution outperforms the existing schemes in terms of user throughput, energy, and spectral efficiency.

A testbed for flexible and energy-efficient resource management with virtualized LTE-A nodes

This paper describes the software architecture and the implementation of a fully operational testbed that demonstrates the benefits of flexible, dynamic resource allocation with virtualized LTE-A nodes. The testbed embodies and specializes the general software architecture devised within the Flex5Gware EU project, and focuses on two intelligent programs: the first one is a Global Scheduler, that coordinates radio resource allocation among interfering nodes; the second one is a Global Power Manager, which switches on/off nodes based on their expected and measured load over a period of minutes. The software framework is written using open-source software, and includes fast, scalable optimization algorithms at both components. Moreover, it supports virtualized BaseBand Units, implemented using OpenAir-Interface, that can run on physical and virtual machines. We present the results obtained via on-field measurements, that demonstrate the feasibility and benefits of our approach.

Downlink transmit power setting in LTE HetNets with carrier aggregation

Carrier aggregation, which allows users to aggregate several component carriers to obtain up to 100 MHz of bandwidth, is one of the central features envisioned for next generation cellular networks. While this feature will enable support for higher data rates and improve quality of service, it may also be employed as an effective interference mitigation technique, especially in multi-tier heterogeneous networks. Having in mind that the aggregated component carriers may belong to different frequency bands and, hence, have varying propagation profiles, we argue that it is not necessary, indeed even harmful, to transmit at maximum power at all carriers, at all times. Rather, by using game theory, we design a distributed algorithm that lets eNodeBs and micro base stations dynamically adjust the downlink transmit power for the different component carriers. We compare our scheme to different power strategies combined with popular interference mitigation techniques, in a typical large-scale scenario, and show that our solution significantly outperforms the other strategies in terms of global network utility, power consumption and user throughput.

Interference-aware resource scheduling in LTE HetNets with carrier aggregation support

Optimal resource allocation in LTE networks is known to be a hard problem, and is further exacerbated when support for advanced features such as heterogeneity and carrier aggregation are also considered. In particular, in LTE heterogeneous networks (HetNets) where radio resources are shared between different layers of base stations, interference management can be a daunting task. Carrier aggregation (CA), which allows the simultaneous use of several LTE component carriers to achieve high user data rates, also adds to the complexity. In this paper, we propose an interference-aware heuristic algorithm that jointly performs carrier selection and resource allocation to serve a mix of users with CA-enabled and legacy terminals. We evaluate the performance of our approach in a large-scale scenario and compare it with other widely used heuristic algorithms such as Proportional-Fair scheduling and Enhanced Inter Cell Interference Coordination (eICIC) techniques. Simulation results show that the solution we propose increases system throughput, minimises energy consumption and improves spectrum utilisation, while also ensuring better fairness between CA-enabled and legacy user terminals.

Practical feasibility, scalability and effectiveness of coordinated scheduling algorithms in cellular networks towards 5G

Abstract Coordinated Scheduling (CS) is used to mitigate inter-cell interference in present (4G) and future (5G) cellular networks. We show that coordination of a cluster of nodes can be formulated as an optimization problem, i.e., placing the Resource Blocks (RB) in each nodes subframe with the least possible overlapping with neighboring nodes. We provide a clever formulation, which allows optimal solutions to be computed in clusters of ten nodes, and algorithms that compute good suboptimal solutions for clusters of tens of nodes, fast enough for a network to respond to traffic changes in real time. This allows us to assess the relationship between the scale at which CS is performed and its benefits in terms of network energy efficiency and cell-edge user rate. Our results, obtained using realistic power, radiation and Signal-to-Interference-and-Noise-Ratio (SINR) models, show that optimal CS allows a significant protection of cell-edge users. Moreover, this goes hand-in-hand with a reduction in the number of allocated RBs, which in turn allows an operator to reduce its energy consumption. Both benefits actually increase with the size of the clusters. The evaluation is carried out in both a 4G and a foreseen 5G setting, using different power models, system bandwidths and SINR-to-datarate mappings.