There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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Table Of Integrals Series And Products

Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Technological evolution of mobile user equipments (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. Suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud (CC). Nevertheless, this option introduces significant execution delay consisting in delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such delay is inconvenient and make the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: i) decision on computation offloading, ii) allocation of computing resource within the MEC, and iii) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

This document provides updates to IEEE Std 802.16's MIB for the MAC, PHY and asso- ciated management procedures in order to accommodate recent extensions to the standard.

IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems Draft Amendment: Management Information Base Extensions

Modeling computer networks is a complex task, as their behavior depends from several variables. Focusing on a single communication device, ISO/OSI and TCP/IP layered protocol stacks provide interoperability and fast deployment of networking solutions, but they limit the control on the interaction among protocols operating at different layers. As a consequence, the need is emerging to develop appropriate models to capture and evaluate the interaction of protocols within a single communication device in order to underline such forms of "indirect" interaction - since they may lead to unforeseen performance degradations. The proposed work aims at using the game theory for capturing the interactions within the protocol stack of a single node, with the goal of allowing to determine the "steady state" or the operating point of the system in a given scenario. As a result, a scalable and modular framework is presented, that enables characterization and analysis of cross-layer interactions starting from the protocols' specifications. Finally, as an example of application, the model is applied to a single-hop IEEE 802.11 wireless network.

Game Theory As a Tool for Modeling Cross-Layer Interactions

Optimization of the performance of the link layer in wireless networks is complex due to multiple parameters involved. Network management in real-time and performance adaptation are extremely challenging. In this paper, we introduce CogMAC, a cognitive link layer approach capable of tuning the network performance in highly dynamic environments. Results obtained using simulations and testbed measurements evince the superiorities of the proposed approach over existing non-adaptive techniques.

/pdf/cogmac-a-cognitive-link-layer-for-wireless-local-area-cuwoee5083.pdf

CogMAC: a cognitive link layer for wireless local area networks

This paper proposes a new method to model the instantaneous uplink (UL) energy efficiency (EE) of a mobile terminal when it is transmitting to a base station using the Long Term Evolution (LTE) technology. It is known that the transmitted power can vary significantly, mainly depending on the used modulation code scheme (MCS), or the path loss between the user equipment (UE) and the eNodeB (eNB), or even the number of resource blocks (RBs) used to send the data. Even though this transmitted power, also known as irradiated power, does not correspond to the final power consumption during an UL transmission, its variability has an impact on it. Unlike existing models, this paper considers the power consumed in the radio frequency chain and the power consumed by the baseband processing. The proposed model is expected to improve the accuracy of the theoretic measurements of both the power consumption and the EE of UL transmissions in LTE systems.

Uplink energy efficiency in LTE systems

A motion prediction technique is presented, which searches for the vector for each block of the field inside a prediction list. The list is filled using the autocompensation principle and those vectors already known in the spatio-temporal neighbourhood.

Autocompensation-with-parameter for motion field prediction

The proceedings contain 21 papers. The topics discussed include: a MAC protocol for cognitive radio wireless ad hoc networks; design of a robust cognitive control channel for cognitive radio networ ...

Fabrizio Granelli

Papers

Game Theory As a Tool for Modeling Cross-Layer Interactions

CogMAC: a cognitive link layer for wireless local area networks

Uplink energy efficiency in LTE systems

Autocompensation-with-parameter for motion field prediction

Multiple Access Communications : 4th International Workshop, MACOM 2011, Trento, Italy, September 12-13, 2011. Proceedings