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

This paper proposes a service-aware cross-layer approach between application/transport layers on the mobile terminal and link layer on the wireless base station to enable dynamic control on the level of per-packet error protection for multimedia data streams. Specifically, in the context of cellular networks, the proposed scheme enables the mobile terminal to specify to the base station the desired level of Hybrid ARQ (HARQ) protection by using an in-band control feedback channel. Such protection is dynamically adapted on a per-packet basis and depends on the perceptual importance of different packets as well as on the reception history of the flow. Experimental results demonstrate the potential benefits deriving from the proposed strategy either for audio and video real-time streams as well as for TCP-based data transfers.

/pdf/service-aware-retransmission-control-in-cellular-networks-y0p46jnq7x.pdf

Service-aware retransmission control in cellular networks

The vision of future fifth generation networks includes an entire ecosystem mainly composed by several different access technologies, virtualization and cloud/edge computing paradigms. Side by side, it also includes full deployment of machine learning for proactive network management and diagnosis. All those elements of such a complex system are used to host the huge variety of verticals the mobile network will have to support. These premises have enforced the idea that fifth generation will be the last one, while future modifications will just represent software/hardware upgrades of that complex ecosystem. This article envisions the realisation of autonomic mobile network operators as the main element, which will significantly change the intrinsic nature of networks, network operators and humans' role in mobile networks, to establish the actual change to a future generation. After presenting architecture and logical-functional structure of such autonomic operators, the paper shows a possible effective model of that future generation as autonomic multi-agent multi-layer system.

Towards Autonomic Mobile Network Operators

In this work, a novel methodology for radio resource management is considered for multi-rate MC-CDMA WLAN indoor networking. The proposed strategy is based on resource reallocation and rate adaptation depending on the network situation, monitored in terms of achieved QoS by an intelligent access-point (AP). As they access the medium, all users send bit-rate requests on the basis of their application requirements. Then, the AP monitors the QoS in terms of frame-error-rate (FER) and decides a) to reallocate the radio resources (in terms of number of orthogonal subchannels) and b) to reduce the data rate, in order to improve throughput performances for those users penalized by heavy FER. The "rate downshift" process is continued until the FER measured by the AP allows data transfer with an acceptable QoS. The intelligent AP can also issue a "rate upshift" for users previously downshifted, or in presence of explicit requests, when the channel situation improves. Simulation results underline a general improvement of the aggregated throughput deriving from adaptively manage the QoS requirements with respect to the network situation, thus enabling good resource usage with the WLAN cell.

/pdf/situation-aware-radio-resource-management-for-multi-rate-mc-1pi2ekbn0l.pdf

Situation-aware radio resource management for multi-rate MC-CDMA wireless networks targeted at multimedia data exchanges in local areas

Spectrum sensing in cognitive radio technology consumes a significant amount of time and energy resources. Thus, it has a direct effect on the achievable throughput and consumed energy. In case of multi-channel systems, the problem becomes more effective since both the resources expenditure and the performance influence of spectrum sensing increase. Considering energy detection as the spectrum sensing method, a number of energy samples should be collected from each channel. Unlike the conventional scheme, the number of samples collected from each channel should be different due to the variant channel conditions. In this paper, the number of samples collected from each channel is optimized based on different setups, namely, throughput maximization setup, interference minimization setup, and sensing energy minimization setup.

/pdf/optimizing-the-number-of-samples-for-multi-channel-spectrum-1488eu3g3s.pdf

Optimizing the number of samples for multi-channel spectrum sensing

Internet of Things (IoT) network is dominating both the research and industry. There are numerous emerging IoT connectivity Technologies such as Sigfox, LoRa, NB-IoT, LTEM. However, these IoT connectivity technologies have different protocols and packet/message formatting. Thus, IoT devices are usually not able to interact with one another, causing interoper-ability challenges. This is creating the so-called network island or silos. Interoperability between different IoT networks needs to be achieved to fully exploit IoT potential. This is required at each level of the network. Different solutions have been proposed to tackle the interoperability problem at different levels reducing the difficulty in defining a solution breaching the vertical silos barrier. In this article, we focus on addressing network-level interoperability. We provide a network format translator in a virtualized environment as a flexible and lightweight deployment. As a proof of concept, a testbed is developed implementing the proposed translator using NS3. Using the testbed, we can communicate with different IoT technologies sending packets between each device in each type of IoT network. For example, sending a LoRaWAN packet to Wi-Fi and 6LoWPAN and visa-versa. Finally, we have measured the latency introduced by the translator.

Fabrizio Granelli

Papers

Service-aware retransmission control in cellular networks

Towards Autonomic Mobile Network Operators

Situation-aware radio resource management for multi-rate MC-CDMA wireless networks targeted at multimedia data exchanges in local areas

Optimizing the number of samples for multi-channel spectrum sensing

A Translator as Virtual Network Function for Network Level Interoperability of Different IoT Technologies