In this paper, a survey of the literature of the past 15 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 paper 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.

/pdf/a-survey-of-machine-learning-techniques-applied-to-self-5ggfpvrhwz.pdf

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

The area of Internet traffic measurement has advanced enormously over the last couple of years. This was mostly due to the increase in network access speeds, due to the appearance of bandwidth-hungry applications, due to the ISPs' increased interest in precise user traffic profile information and also a response to the enormous growth in the number of connected users. These changes greatly affected the work of Internet service providers and network administrators, which have to deal with increasing resource demands and abrupt traffic changes brought by new applications. This survey explains the main techniques and problems known in the field of IP traffic analysis and focuses on application detection. First, it separates traffic analysis into packet-based and flow-based categories and details the advantages and problems for each approach. Second, this work cites the techniques for traffic analysis accessible in the literature, along with the analysis performed by the authors. Relevant techniques include signature-matching, sampling and inference. Third, this work shows the trends in application classification analysis and presents important and recent references in the subject. Lastly, this survey draws the readers' interest to open research topics in the area of traffic analysis and application detection and makes some final remarks.

A Survey on Internet Traffic Identification

Mobile Edge Computing (MEC) is considered an essential future service for the implementation of 5G networks and the Internet of Things, as it is the best method of delivering computation and communication resources to mobile devices. It is based on the connection of the users to servers located on the edge of the network, which is especially relevant for real-time applications that demand minimal latency. In order to guarantee a resource-efficient MEC (which, for example, could mean improved Quality of Service for users or lower costs for service providers), it is important to consider certain aspects of the service model, such as where to offload the tasks generated by the devices, how many resources to allocate to each user (specially in the wired or wireless device-server communication) and how to handle inter-server communication. However, in the MEC scenarios with many and varied users, servers and applications, these problems are characterized by parameters with exceedingly high levels of dimensionality, resulting in too much data to be processed and complicating the task of finding efficient configurations. This will be particularly troublesome when 5G networks and Internet of Things roll out, with their massive amounts of devices. To address this concern, the best solution is to utilize Machine Learning (ML) algorithms, which enable the computer to draw conclusions and make predictions based on existing data without human supervision, leading to quick near-optimal solutions even in problems with high dimensionality. Indeed, in scenarios with too much data and too many parameters, ML algorithms are often the only feasible alternative. In this paper, a comprehensive survey on the use of ML in MEC systems is provided, offering an insight into the current progress of this research area. Furthermore, helpful guidance is supplied by pointing out which MEC challenges can be solved by ML solutions, what are the current trending algorithms in frontier ML research and how they could be used in MEC. These pieces of information should prove fundamental in encouraging future research that combines ML and MEC.

Machine Learning Meets Computation and Communication Control in Evolving Edge and Cloud: Challenges and Future Perspective

Review: A survey of network flow applications

Current IP-level mobility management protocols are unable to satisfy the stringent handover latency requirements for supporting real-time multimedia services between different domains in a high-speed vehicular environment. This paper proposes an inter-domain handover scheme for Proxy Mobile IPv6 (PMIPv6) with a new network entity, called intermediate-mobile access gateway (iMAG). The iMAG is located between the home and the foreign domains and is connected to both domains. The proposed scheme is a proactive handover approach that performs the inter-domain L3 handover in advance before the inter-domain L2 handover while the Mobile Node (MN) is still connected to the iMAG in the home domain. Therefore, the proposed scheme enables the inter-domain handover latency to reduce as low as the intra-domain handover latency. Numerical analysis and simulation results demonstrate that the proposed scheme is able to support seamless service continuity in inter-domain handovers. Copyright © 2009 John Wiley & Sons, Ltd.

This paper proposes an inter-domain handover scheme for Proxy Mobile IPv6 (PMIPv6) with a new network entity, called intermediate-mobile access gateway (iMAG) which is connected to both the home and the foreign domains. The proposed scheme is a proactive handover approach that performs the inter-domain L3 handover in advance before the inter-domain L2 handover while the Mobile Node (MN) is still connected to the iMAG in the home domain. Therefore, the proposed scheme enables the inter-domain handover latency to reduce as low as the intra-domain handover latency. Copyright © 2009 John Wiley & Sons, Ltd.

Inter-domain handover scheme using an intermediate mobile access gateway for seamless service in vehicular networks

In wireless networks employing carrier-sense multiple-access with collision avoidance (CSMA/CA), correlations between the service processes of different nodes arise as a result of competition for common wireless channels and the dependencies between upstream and downstream traffic flows. These dependencies make the development of tractable performance models extremely difficult. To address this purpose, we present a new continuous-time model for CSMA wireless networks where we combine a node model and a channel model in order to capture correlation. Simplification methods are presented that make our models computationally tractable for large networks with minimal loss of accuracy. The model can be used for both single and multi-hop wireless networks and takes into account non-saturated queues, backoff-stage dependence of collision probabilities, and the correlation between departure processes and arrival processes of adjacent nodes. The model can be used to compute probabilistic quality of service guarantees to optimize end-to-end throughput and end-to-end delay by adjusting arrival and backoff rates along various paths.

Analytical models for understanding space, backoff, and flow correlation in CSMA wireless networks

The purpose of this paper is to present a new method for the study of the competition and co- operation relationships among nodes in a network us- ing a CSMA (Carrier Sense Multiple Access) proto- col implemented with an exponential backoff process. Self-organized behavior is proposed. Standard topolo- gies, such as tandem, traffic splitting and merging, are studied with variables: 1) backoff rate, 2) topology po- sition, 3) traffic splitting. Several interesting phenom- ena are reported. We propose a “Channel Utilization Model” with a new derived variable “channel access rate” which can be used for all traffic loads. These mod- els are applied with different optimization methods. Special strategies, for example “MAC Friendliness”, are also presented for serving special traffic like emergency traffic.

http://perpetualinnovation.net/ojs/index.php/ijngc/article/download/166/57

Competition, Cooperation, and Optimization in Multi-Hop CSMA Networks with Correlated Traffic

In this paper we present a multi-cell analytic model for multi-class cellular networks. We investigate the effects subscriber mobility has on bandwidth control strategies when the network supports multiple classes of subscribers having different bandwidth requirements. We introduce control strategies from non-mobile networks and examine them in a mobile environment. The expressions for call blocking using these control strategies have product form solutions. This allows us to develop a multi-cell, multi-class, model by generalizing on the Erlang fixed point approximation using generalized multi-rate, multi-class, Erlang loss formulas for each class of traffic. We produce expressions for originating calls lost, hand-off calls lost, forced-termination, and mean channel occupancy for each class of traffic in each cell for different control strategies. The multicell analytic model allows us to investigate the effects of asymmetric loads and mobility patterns in the network. The analytic results are supported by simulation.

An analysis of the effects of mobility on bandwidth allocation strategies in multi-class cellular wireless networks

Analytical models for understanding misbehavior and MAC friendliness in CSMA networks

Approximation models of feed-forward G/G/1/N queueing networks with correlated arrivals

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