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

A generic communication architecture for end to end mobility management in the Internet

The proliferation of laptops, cellular phones, and other mobile computing platforms connected to the Internet has triggered numerous research works into mobile networking. The increasingly dense set of wireless access networks that can be potentially accessed by mobile users open the door to an era of pervasive computing. However, the puzzle of wireless access networks that tends to become the natural access networks to the Internet pushes legacy“wireoriented” communication architectures to their limit. Indeed, there is a critical gap between the increasingly used stream centric multimedia applications and the incapacity of legacy communication stacks to insure the continuity of these multimedia sessions for mobile users. This paper proposes a generic communication architecture (i.e. not dedicated to a specific protocol or technology) that aims to fill the gap between the application layer continuity needs and the discontinuity of the communication service inherent to the physical layer of wireless mobile networks. This paper introduces an end to end communication architecture that preserves efficiently session continuity in the context of mobile and wireless networks. This architecture is mainly based on end to end mechanisms that could be integrated into a new generation reconfigurable transport protocol. The proposed contribution efficiently satisfies mobility requirements such as efficient location management, fast handover, and continuous connection support

Dynamic Channel Allocation in Mobile Multimedia Networks Using Error Back Propagation and Hopfield Neural Network (EBP-HOP)

AbstractIn mobile multimedia communication systems, the limited bandwidth is an issue of serious concern. However for the better utilization of available resources in a network, channel allocation scheme plays a very important role to manage the available resources in each cell. Hence this issue should be managed to reduce the call blocking or dropping probabilities. This paper gives the new dynamic channel allocation scheme which is based on handoff calls and traffic mobility using hopfield neural network. It will improve the capacity of existing system. Hopfield method develops the new energy function that allocates channel not only for new call but also for handoff calls on the basis of traffic mobility information. Moreover, we have also examined the performance of traffic mobility with the help of error back propagation neural network model to enhance the overall Quality of Services (QoS) in terms of continuous service availability and intercell handoff calls. Our scheme decreases the call handoff dropping and blocking probability up to a better extent as compared to the other existing systems of static and dynamic channel allocation schemes

Robustness of optimal channel reservation using handover prediction in multiservice wireless networks

The aim of our study is to obtain theoretical limits for the gain that can be expected when using handover prediction and to determine the sensitivity of the system performance against different parameters. We apply an average-reward reinforcement learning approach based on afterstates to the design of optimal admission control policies in mobile multimedia cellular networks where predictive information related to the occurrence of future handovers is available. We consider a type of predictor that labels active mobile terminals in the cell neighborhood a fixed amount of time before handovers are predicted to occur, which we call the anticipation time. The admission controller exploits this information to reserve resources efficiently. We show that there exists an optimum value for the anticipation time at which the highest performance gain is obtained. 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Modeling Seamless Vertical Handovers in Heterogeneous Wireless Networks

Vertical handover in heterogeneous wireless networks provides customers with better Quality of Service (QoS) experience. For seamless handover, timely initiation of handover process plays a key role. Various vertical handover management protocols have been proposed and standardized to support mobility across heterogeneous networks. In Media Independent Handover (MIH) based schemes, distributed handover decision is made via certain predefined triggers that consider user context. In this paper, we present a comprehensive review of the modeling techniques used during management of vertical handover. We have also defined a novel architecture, HRPNS: Handoff Resolving and Preferred Network Selection module enabling vertical handover that ensures QoS. The construction of HRPNS module involves integration of fuzzy logic and Markov Decision Process (MDP) for providing precise decision of handover

A survey of network coverage prediction mechanisms in 4G heterogeneous wireless networks.

Seamless connectivity in 4G wireless networks requires the development of intelligent proactive mechanisms for efficiently predicting vertical handovers. Random device mobility patterns further increase the complexity of the handover process. Geographical topologies such as indoor and outdoor environments also exert additional constraints on network coverage and device mobility. The ability of a device to acquire refined knowledge about surrounding network coverage can significantly affect the performance of vertical handover prediction and QoS management mechanisms. This paper presents a comprehensive survey of research work conducted in the area of 4G wireless network coverage prediction for the optimisation of vertical handovers. It discusses different coverage prediction approaches and analyses their ability to accurately predict network coverage