2,227 research outputs found

    Mobility-based predictive call admission control and resource reservation for next-generation mobile communications networks.

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    Recently, the need for wireless and mobile communications has grown tremendously and it is expected that the number of users to be supported will increase with high rates in the next few years. Not only the number of users, but also the required bandwidth to support each user is supposed to increase especially with the deploying of the multimedia and the real time applications. This makes the researchers in the filed of mobile and wireless communications more interested in finding efficient solutions to solve the limitations of the available natural radio resources. One of the important things to be considered in the wireless mobile environment is that the user can move from one location to another when there is an ingoing call. Resource reservation ( RR ) schemes are used to reserve the bandwidth ( BW ) required for the handoff calls. This will enable the user to continue his/her call while he/she is moving. Also, call admission control ( CAC ) schemes are used as a provisioning strategy to limit the number of call connections into the network in order to reduce the network congestion and the call dropping. The problem of CAC and RR is one of the most challenging problems in the wireless mobile networks. Also, in the fourth generation ( 4G ) of mobile communication networks, many types of different mobile systems such as wireless local area networks ( WLAN s) and cellular networks will be integrated. The 4G mobile networks will support a broad range of multimedia services with high quality of service.New Call demission control and resource reservation techniques are needed to support the new 4G systems. Our research aims to solve the problems of Call Admission Control (CAC), and resource reservation (RR) in next-generation cellular networks and in the fourth generation (4G) wireless heterogeneous networks. In this dissertation, the problem of CAC and RR in wireless mobile networks is addressed in detail for two different architectures of mobile networks: (1) cellular networks, and (2) wireless heterogeneous networks (WHNs) which integrate cellular networks and wireless local area networks (WLANs). We have designed, implemented, and evaluated new mobility-based predictive call admission control and resource reservation techniques for the next-generation cellular networks and for the 4G wireless heterogeneous networks. These techniques are based on generating the mobility models of the mobile users using one-dimensional and multidimensional sequence mining techniques that have been designed for the wireless mobile environment. The main goal of our techniques is to reduce the call dropping probability and the call blocking probability, and to maximize the bandwidth utilization n the mobile networks. By analyzing the previous movements of the mobile users, we generate local and global mobility profiles for the mobile users, which are utilized effectively in prediction of the future path of the mobile user. Extensive simulation was used to analyze and study the performance of these techniques and to compare its performance with other techniques. Simulation results show that the proposed techniques have a significantly enhanced performance which is comparable to the benchmark techniques

    Final report on the evaluation of RRM/CRRM algorithms

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    Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin

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

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    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

    How to Knit Your Own Markov Blanket

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    Hohwy (Hohwy 2016, Hohwy 2017) argues there is a tension between the free energy principle and leading depictions of mind as embodied, enactive, and extended (so-called ‘EEE1 cognition’). The tension is traced to the importance, in free energy formulations, of a conception of mind and agency that depends upon the presence of a ‘Markov blanket’ demarcating the agent from the surrounding world. In what follows I show that the Markov blanket considerations do not, in fact, lead to the kinds of tension that Hohwy depicts. On the contrary, they actively favour the EEE story. This is because the Markov property, as exemplified in biological agents, picks out neither a unique nor a stationary boundary. It is this multiplicity and mutability– rather than the absence of agent-environment boundaries as such - that EEE cognition celebrates

    Optimization and Learning in Energy Efficient Cognitive Radio System

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    Energy efficiency and spectrum efficiency are two biggest concerns for wireless communication. The constrained power supply is always a bottleneck to the modern mobility communication system. Meanwhile, spectrum resource is extremely limited but seriously underutilized. Cognitive radio (CR) as a promising approach could alleviate the spectrum underutilization and increase the quality of service. In contrast to traditional wireless communication systems, a distinguishing feature of cognitive radio systems is that the cognitive radios, which are typically equipped with powerful computation machinery, are capable of sensing the spectrum environment and making intelligent decisions. Moreover, the cognitive radio systems differ from traditional wireless systems that they can adapt their operating parameters, i.e. transmission power, channel, modulation according to the surrounding radio environment to explore the opportunity. In this dissertation, the study is focused on the optimization and learning of energy efficiency in the cognitive radio system, which can be considered to better utilize both the energy and spectrum resources. Firstly, drowsy transmission, which produces optimized idle period patterns and selects the best sleep mode for each idle period between two packet transmissions through joint power management and transmission power control/rate selection, is introduced to cognitive radio transmitter. Both the optimal solution by dynamic programming and flexible solution by reinforcement learning are provided. Secondly, when cognitive radio system is benefited from the theoretically infinite but unsteady harvested energy, an innovative and flexible control framework mainly based on model predictive control is designed. The solution to combat the problems, such as the inaccurate model and myopic control policy introduced by MPC, is given. Last, after study the optimization problem for point-to-point communication, multi-objective reinforcement learning is applied to the cognitive radio network, an adaptable routing algorithm is proposed and implemented. Epidemic propagation is studied to further understand the learning process in the cognitive radio network

    Predicting topology propagation messages in mobile ad hoc networks: The value of history

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    This research was funded by the Spanish Government under contracts TIN2016-77836-C2-1-R,TIN2016-77836-C2-2-R, and DPI2016-77415-R, and by the Generalitat de Catalunya as Consolidated ResearchGroups 2017-SGR-688 and 2017-SGR-990.The mobile ad hoc communication in highly dynamic scenarios, like urban evacuations or search-and-rescue processes, plays a key role in coordinating the activities performed by the participants. Particularly, counting on message routing enhances the communication capability among these actors. Given the high dynamism of these networks and their low bandwidth, having mechanisms to predict the network topology offers several potential advantages; e.g., to reduce the number of topology propagation messages delivered through the network, the consumption of resources in the nodes and the amount of redundant retransmissions. Most strategies reported in the literature to perform these predictions are limited to support high mobility, consume a large amount of resources or require training. In order to contribute towards addressing that challenge, this paper presents a history-based predictor (HBP), which is a prediction strategy based on the assumption that some topological changes in these networks have happened before in the past, therefore, the predictor can take advantage of these patterns following a simple and low-cost approach. The article extends a previous proposal of the authors and evaluates its impact in highly mobile scenarios through the implementation of a real predictor for the optimized link state routing (OLSR) protocol. The use of this predictor, named OLSR-HBP, shows a reduction of 40–55% of topology propagation messages compared to the regular OLSR protocol. Moreover, the use of this predictor has a low cost in terms of CPU and memory consumption, and it can also be used with other routing protocols.Peer ReviewedPostprint (published version
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