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

The implementation of Network Coding (NC) in IEEE 802.11-based wireless networks presents the important challenge of providing additional transmission priority for the relay nodes responsible for coding. These nodes are able to convey more information in each transmission than those that forward single packets, by combining several received packets in a single coded packet. To transmit data, the nodes execute the IEEE 802.11 Medium Access Control (MAC) protocol, called the Distributed Coordination Function (DCF). Thus, they compete for the access to the wireless channel and get equal transmission opportunities under high congestion. As a result, congested relay nodes will severely limit the performance of the network. In this paper, we investigate a backwards-compatible mechanism, called Reverse Direction DCF (RD-DCF), that allows relay nodes to transmit data upon successful reception of data. We analyze the performance limits of the proposed protocol with and without NC in terms of throughput and energy efficiency. The performance evaluation considers different traffic loads, packet lengths, and data rates. The results of this work show that the proposed RD-DCF+NC protocol can improve throughput and energy efficiency up to 335% when compared to legacy DCF.

Analysis of a network coding-aware MAC protocol for IEEE 802.11 wireless networks with Reverse Direction transmissions

Cognitive Radio provides a promising solution for the spectrum scarcity in wireless systems. The main stage of a successful cognitive transmission is the spectrum sensing stage, where the spectrum is sensed to detect and avoid interfering with licensed users. Unfortunately, regardless of the type of the spectrum sensing technique employed, the probability of missed detection can still be relevant. This paper investigates the effects of missed detection probability on the energy resources of the cognitive system, and provides a new algorithm for power allocation in OFDM systems, which reduces the loss in energy resources, and guarantees the target Quality-of-Service (QoS) of the served users. Unlike the current algorithms, the proposed algorithm is based on the Sensing Side Information (SSI) and the Channel State Information (CSI) as well. Simulation results underline a relevant reduction in terms of power loss (with gains up to 50%) and a consequent improvement in QoS satisfaction.

On the reduction of power loss caused by imperfect spectrum sensing in OFDMA-based Cognitive Radio access

The ever increasing demand for ubiquitous wireless connectivity and the inherently limited power resources at mobile devices highlight the need for an energy efficient operation of these devices. The paper addresses this need by means of an appropriate handover policy. According to it, a handover is initiated whenever the energy efficiency of the user equipment falls below a given threshold. The handover target is selected among the candidates so as to maximize the achievable energy efficiency. For networks employing Proportionally Fair access, it is shown that the achievable energy efficiency can be calculated by means of a simple expression, requiring only a limited amount of network and terminal status information. In particular, no network load status is required. Simulation results demonstrate that the proposed handover policy can lead to a significantly improved (up to 15%) energy efficiency without compromising throughput performance.

A Handover Policy for Energy Efficient Network Connectivity through Proportionally Fair Access

The next generation of cellular networks is expected to provide a huge step forward in terms of network capacity, reduced latency and reliability. Due to the large variety of applications and services, the mobile network is expected to deliver a differentiated quality of experience (QoE) depending on the major application scenarios expected in 5th Generation (5G). To provide the necessary flexibility and reconfigurability of the 5G architecture, network resource partitioning/slicing is expected to provide an efficient and cost effective way of delivering the required QoE. However, few testbeds are available to analyze the pros and cons of such technology, as well as to enable a proper performance evaluation. This paper proposes an outline of the ongoing implementation of a 5G end-to-end network slicing testbed. The testbed is based on an open-source software Long Term Evolution (LTE) protocol stack, OpenAirInterface, which operates on Software-defined Radio (SDR) devices, EXPRESSMIMO2 board, extended to implement and demonstrate slicing in a 5G scenario.

Towards E2E Slicing in 5G: A Spectrum Slicing Testbed and Its Extension to the Packet Core

An effective post-processing algorithm for error recovery in noise-corrupted JPEG bit streams is presented. The good performances provided in terms of perceptual quality and real-time working capability makes the algorithm highly suitable for applications such as outdoor video-surveillance, characterised by severe constraints on image quality and processing time.

Fabrizio Granelli

Papers

Analysis of a network coding-aware MAC protocol for IEEE 802.11 wireless networks with Reverse Direction transmissions

On the reduction of power loss caused by imperfect spectrum sensing in OFDMA-based Cognitive Radio access

A Handover Policy for Energy Efficient Network Connectivity through Proportionally Fair Access

Towards E2E Slicing in 5G: A Spectrum Slicing Testbed and Its Extension to the Packet Core

Real-time error correction algorithm for noise-corrupted JPEG bit-streams