A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

5G definition and standardization projects are well underway, and governing characteristics and major challenges have been identified. A critical network element impacting the potential performance of 5G networks is the backhaul, which is expected to expand in length and breadth to cater to the exponential growth of small cells while offering high throughput in the order of gigabit per second and less than 1 ms latency with high resilience and energy efficiency. Such performance may only be possible with direct optical fiber connections that are often not available country-wide and are cumbersome and expensive to deploy. On the other hand, a prime 5G characteristic is diversity, which describes the radio access network, the backhaul, and also the types of user applications and devices. Thus, we propose a novel, distributed, self-optimized, end-to-end user-cell-backhaul association scheme that intelligently associates users with candidate cells based on corresponding dynamic radio and backhaul conditions while abiding by users' requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator (DPI) of the end-to-end network performance such as capacity, latency, resilience, energy consumption, and so on. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul performance that conforms to the user requirements. Reinforcement learning is used at the radio cells to optimise the bias factors for each DPI in a way that maximise the system throughput while minimising the gap between the users' achievable and required end-to-end quality of experience (QoE). Preliminary results show considerable improvement in users' QoE and cumulative system throughput when compared with the state-of-the-art user-cell association schemes

Distributed drone base station positioning for emergency cellular networks using reinforcement learning

Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network

Access-aware backhaul optimization in 5G

The final publication is available at ACM via http://dx.doi.org/10.1145/3265863.3265881Aggressive demand of future access network services is being translated into the stringent requirement on future backhaul infrastructure. It is not possible to take the backhaul resources for granted anymore; rather, more focused research is required to tackle the challenge of limited resources. It is also anticipated that, to meet the expectation of 5G, access and backhaul networks will work closely and therefore, total separation of their resources may not be possible anymore and joint operation is required. In this paper, we argue that, joint access-backhaul mechanisms is becoming necessary to ensure the best use of the scarce resources. We introduce the problem of statically assigning resources to capacity-limited backhaul links and we provide preliminary results to show the potential benefits of an intelligent access-aware backhaul capacity optimization scheme, where a central controller optimizes backhaul capacity according to corresponding access network requirements. Simulation results show that, with this approach, we are able to carry more traffic in a network limited by its backhaul capacity.Peer ReviewedPostprint (published version

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Backhaul Aware User-Specific Cell Association Using Q-Learning

With the advent of network densification and the development of other radio interface technologies, the major bottleneck of future cellular networks is shifting from the radio access network to the backhaul. The future networks are expected to handle a wide range of applications and users with different requirements. In order to tackle the problem of downlink user-cell association, and allocate users to the best cell, an intelligent solution based on reinforcement learning is proposed. A distributed solution based on Q-Learning is developed in order to determine the best cell range extension offsets (CREOs) for each small cell (SC) and the best weights of each user requirement to efficiently allocate users to the most appropriate SC, based on both backhaul constraints and user demands. By optimizing both CREOs and user weights, a user-specific allocation can be achieved, resulting in a better overall quality of service. The results show that the proposed algorithm outperforms current solutions by achieving better user satisfaction, mitigating the total number of users in outage, and minimizing user dissatisfaction when satisfaction is not possible

Memory-Based User-Centric Backhaul-Aware User Cell Association Scheme

Ultra-dense small cell networks represent a key future network solution that can help meet the exponentially rising traffic requirements of modern wireless networks. Backhauling these small cells are an emerging challenge to the extent that various cells are likely to have different backhaul constraints. The user-centric backhaul scheme has been proposed in the literature to jointly exploit the diversity in users' requirement and backhaul constraints. In this paper, we propose a novel scheme, termed the memory-based hybrid scheme, which additionally also exploits the predictability in a user's mobility. We compare the novel scheme to two variants of memory-less user-centric backhaul implementations and show significant gains in convergence time (15%), user-centric KPIs (51% and 82%) at the negligible cost 2% loss in cumulative throughput. The novel scheme requires additional memory in user-devices to store learned values, which is nonetheless well justified in view of the considerable gains achieved

The METIS 5G System Concept: Meeting the 5G Requirements

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] The development of every new generation of wireless communication systems starts with bold, high-level requirements and predictions of its capabilities. The 5G system will not only have to surpass previous generations with respect to rate and capacity, but also address new usage scenarios with very diverse requirements, including various kinds of machine-type communication. Following this, the METIS project has developed a 5G system concept consisting of three generic 5G services: extreme mobile broadband, massive machine-type communication, and ultra-reliable MTC, supported by four main enablers: a lean system control plane, a dynamic radio access network, localized contents and traffic flows, and a spectrum toolbox. This article describes the most important system-level 5G features, enabled by the concept, necessary to meet the very diverse 5G requirements. System-level evaluation results of the METIS 5G system concept are presented, and we conclude that the 5G requirements can be met with the proposed system concept.This work was supported in part by the European Commission under FP7, grant number ICT-317669 METIS.Tullberg, H.; Popovski, P.; Li, Z.; Uusitalo, MA.; Hoglund, A.; Bulakci, O.; Fallgren, M.... (2016). The METIS 5G System Concept: Meeting the 5G Requirements. IEEE Communications Magazine. 54(12):132-139. https://doi.org/10.1109/MCOM.2016.1500799CMS132139541

View on 5G Architecture: Version 2.0

The 5G Architecture Working Group as part of the 5GPPP Initiative is looking at capturing novel trends and key technological enablers for the realization of the 5G architecture. It also targets at presenting in a harmonized way the architectural concepts developed in various projects and initiatives (not limited to 5GPPP projects only) so as to provide a consolidated view on the technical directions for the architecture design in the 5G era. The first version of the white paper was released in July 2016, which captured novel trends and key technological enablers for the realization of the 5G architecture vision along with harmonized architectural concepts from 5GPPP Phase 1 projects and initiatives. Capitalizing on the architectural vision and framework set by the first version of the white paper, this Version 2.0 of the white paper presents the latest findings and analyses with a particular focus on the concept evaluations, and accordingly it presents the consolidated overall architecture design

Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges

With the rapid development of marine activities, there has been an increasing number of maritime mobile terminals, as well as a growing demand for high-speed and ultra-reliable maritime communications to keep them connected. Traditionally, the maritime Internet of Things (IoT) is enabled by maritime satellites. However, satellites are seriously restricted by their high latency and relatively low data rate. As an alternative, shore & island-based base stations (BSs) can be built to extend the coverage of terrestrial networks using fourth-generation (4G), fifth-generation (5G), and beyond 5G services. Unmanned aerial vehicles can also be exploited to serve as aerial maritime BSs. Despite of all these approaches, there are still open issues for an efficient maritime communication network (MCN). For example, due to the complicated electromagnetic propagation environment, the limited geometrically available BS sites, and rigorous service demands from mission-critical applications, conventional communication and networking theories and methods should be tailored for maritime scenarios. Towards this end, we provide a survey on the demand for maritime communications, the state-of-the-art MCNs, and key technologies for enhancing transmission efficiency, extending network coverage, and provisioning maritime-specific services. Future challenges in developing an environment-aware, service-driven, and integrated satellite-air-ground MCN to be smart enough to utilize external auxiliary information, e.g., sea state and atmosphere conditions, are also discussed