A cell outage management framework for dense heterogeneous networks

In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner

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

Self-Sustaining Caching Stations: Towards Cost-Effective 5G-Enabled Vehicular Networks

In this article, we investigate the cost-effective 5G-enabled vehicular networks to support emerging vehicular applications, such as autonomous driving, in-car infotainment and location-based road services. To this end, self-sustaining caching stations (SCSs) are introduced to liberate on-road base stations from the constraints of power lines and wired backhauls. Specifically, the cache-enabled SCSs are powered by renewable energy and connected to core networks through wireless backhauls, which can realize "drop-and-play" deployment, green operation, and low-latency services. With SCSs integrated, a 5G-enabled heterogeneous vehicular networking architecture is further proposed, where SCSs are deployed along roadside for traffic offloading while conventional macro base stations (MBSs) provide ubiquitous coverage to vehicles. In addition, a hierarchical network management framework is designed to deal with high dynamics in vehicular traffic and renewable energy, where content caching, energy management and traffic steering are jointly investigated to optimize the service capability of SCSs with balanced power demand and supply in different time scales. Case studies are provided to illustrate SCS deployment and operation designs, and some open research issues are also discussed.Comment: IEEE Communications Magazine, to appea

Labour Administration Reforms in China

[Excerpt] This publication provides an explanation of the comprehensive labour administration system in China, including its recent advances, with emphasis on its public services functions, such as public employment, labour inspection and social insurance services. With the recent improvements to both the legal framework and the institutions of labour administration, it is believed that these public services will play bigger and more active roles in ensuring compliance with legislation and protecting the legitimate rights and interests of employers and workers alike

Non-cooperative Feedback Rate Control Game for Channel State Information in Wireless Networks

It has been well recognized that channel state information (CSI) feedback is of great importance for dowlink transmissions of closed-loop wireless networks. However, the existing work typically researched the CSI feedback problem for each individual mobile station (MS), and thus, cannot efficiently model the interactions among self-interested mobile users in the network level. To this end, in this paper, we propose an alternative approach to investigate the CSI feedback rate control problem in the analytical setting of a game theoretic framework, in which a multiple-antenna base station (BS) communicates with a number of co-channel MSs through linear precoder. Specifically, we first present a non-cooperative feedback-rate control game (NFC), in which each MS selects the feedback rate to maximize its performance in a distributed way. To improve efficiency from a social optimum point of view, we then introduce pricing, called the non-cooperative feedback-rate control game with price (NFCP). The game utility is defined as the performance gain by CSI feedback minus the price as a linear function of the CSI feedback rate. The existence of the Nash equilibrium of such games is investigated, and two types of feedback protocols (FDMA and CSMA) are studied. Simulation results show that by adjusting the pricing factor, the distributed NFCP game results in close optimal performance compared with that of the centralized scheme.Comment: 26 pages, 10 figures; IEEE Journal on Selected Areas in Communications, special issue on Game Theory in Wireless Communications, 201

A cluster randomized controlled trial of the effectiveness and cost-effectiveness of Intermediate Care Clinics for Diabetes (ICCD) : study protocol for a randomized controlled trial

Background World-wide healthcare systems are faced with an epidemic of type 2 diabetes. In the United Kingdom, clinical care is primarily provided by general practitioners (GPs) rather than hospital specialists. Intermediate care clinics for diabetes (ICCD) potentially provide a model for supporting GPs in their care of people with poorly controlled type 2 diabetes and in their management of cardiovascular risk factors. This study aims to (1) compare patients with type 2 diabetes registered with practices that have access to an ICCD service with those that have access only to usual hospital care; (2) assess the cost-effectiveness of the intervention; and (3) explore the views and experiences of patients, health professionals and other stakeholders. Methods/Design This two-arm cluster randomized controlled trial (with integral economic evaluation and qualitative study) is set in general practices in three UK Primary Care Trusts. Practices are randomized to one of two groups with patients referred to either an ICCD (intervention) or to hospital care (control). Intervention group: GP practices in the intervention arm have the opportunity to refer patients to an ICCD - a multidisciplinary team led by a specialist nurse and a diabetologist. Patients are reviewed and managed in the ICCD for a short period with a goal of improving diabetes and cardiovascular risk factor control and are then referred back to practice. or Control group: Standard GP care, with referral to secondary care as required, but no access to ICCD. Participants are adults aged 18 years or older who have type 2 diabetes that is difficult for their GPs to control. The primary outcome is the proportion of participants reaching three risk factor targets: HbA1c (≤7.0%); blood pressure (<140/80); and cholesterol (<4 mmol/l), at the end of the 18-month intervention period. The main secondary outcomes are the proportion of participants reaching individual risk factor targets and the overall 10-year risks for coronary heart disease(CHD) and stroke assessed by the United Kingdom Prospective Diabetes Study (UKPDS) risk engine. Other secondary outcomes include body mass index and waist circumference, use of medication, reported smoking, emotional adjustment, patient satisfaction and views on continuity, costs and health related quality of life. We aimed to randomize 50 practices and recruit 2,555 patients