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See detailSDR Implementation of a Testbed for Real-Time Interference Detection with Signal Cancellation
Politis, Christos; Maleki, Sina UL; Merlano Duncan, Juan Carlos UL et al

in IEEE Access (2018)

Interference greatly affects the quality of service of wireless and satellite communications, having also a financial impact for the telecommunication operators. Therefore, as the interfering events ... [more ▼]

Interference greatly affects the quality of service of wireless and satellite communications, having also a financial impact for the telecommunication operators. Therefore, as the interfering events increase due to the deployment of new services, there is an increasing demand for the detection and mitigation of interference. There are several interference detectors in the literature, evaluated by using extensive simulations. However, this paper goes one step further, designing, implementing and evaluating the performance of the developed interference detection algorithms experimentally using a software defined radio, and particularly the universal software radio peripheral platform. A realistic communication system is implemented, consisting of a transmitter, a channel emulator and a receiver. Based on this system, we implement all the appropriate communications features such as pulse shaping, synchronization and demodulation. The real-time system implementation is validated and evaluated through signal and interference detection. We observe that the interference detection threshold is critical to the functioning of the system. Several existing interference detection techniques fail in practice due to this fact. In this paper, we propose a robust and practically implementable method the selection of threshold. Finally, we present real-time experimental results for the probabilities of false alarm and detection in order to verify the accuracy of our study and reinforce our theoretical analysis. [less ▲]

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See detailIntelligent Gaming for Mobile Crowd-Sensing Participants to Acquire Trustworthy Big Data in the Internet of Things
Pouryazdan, Maryam; Fiandrino, Claudio; Kantarci, Burak et al

in IEEE Access (2017), 5

In mobile crowd-sensing systems, the value of crowd-sensed big data can be increased by incentivizing the users appropriately. Since data acquisition is participatory, crowd-sensing systems face the ... [more ▼]

In mobile crowd-sensing systems, the value of crowd-sensed big data can be increased by incentivizing the users appropriately. Since data acquisition is participatory, crowd-sensing systems face the challenge of data trustworthiness and truthfulness assurance in the presence of adversaries whose motivation can be either manipulating sensed data or collaborating unfaithfully with the motivation of maximizing their income. This paper proposes a game theoretic methodology to ensure trustworthiness in user recruitment in mobile crowd-sensing systems. The proposed methodology is a platform-centric framework that consists of three phases: user recruitment, collaborative decision making on trust scores, and badge rewarding. In the proposed framework, users are incentivized by running sub-game perfect equilibrium and gami cation techniques. Through simulations, we showthat approximately 50% and a minimum of 15% improvement can be achieved by the proposed methodology in terms of platform and user utility, respectively, when compared with fully distributed and user-centric trustworthy crowd-sensing. [less ▲]

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See detailEnd-to-End Network Slicing in Virtualized OFDMA-Based Cloud Radio Access Networks
Ha, Vu Nguyen UL; Le, Long Bao

in IEEE Access (2017), 5

We consider the resource allocation for the virtualized OFDMA uplink cloud radio access network (C-RAN), where multiple wireless operators (OPs) share the C-RAN infrastructure and resources owned by an ... [more ▼]

We consider the resource allocation for the virtualized OFDMA uplink cloud radio access network (C-RAN), where multiple wireless operators (OPs) share the C-RAN infrastructure and resources owned by an infrastructure provider (InP). The resource allocation is designed through studying tightly coupled problems at two different levels. The upper-level problem aims at slicing the fronthaul capacity and cloud computing resources for all OPs to maximize the weighted profits of OPs and InP considering practical constraints on the fronthaul capacity and cloud computation resources. Moreover, the lower-level problems maximize individual OPs' sum rates by optimizing users' transmission rates and quantization bit allocation for the compressed I/Q baseband signals. We develop a two-stage algorithmic framework to address this two-level resource allocation design. In the first stage, we transform both upper-level and lowerlevel problems into corresponding problems by relaxing underlying discrete variables to the continuous ones. We show that these relaxed problems are convex and we develop fast algorithms to attain their optimal solutions. In the second stage, we propose two methods to round the optimal solution of the relaxed problems and achieve a final feasible solution for the original problem. Numerical studies confirm that the proposed algorithms outperform two greedy resource allocation algorithms and their achieved sum rates are very close to sum rate upper-bound obtained by solving relaxed problems. Moreover, we study the impacts of different parameters on the system sum rate, performance tradeoffs, and illustrate insights on a potential system operating point and resource provisioning issues. [less ▲]

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See detailJoint Beamforming and Power Optimization with Iterative User Clustering for MISO-NOMA Systems
Liu, Zhengxuan; Lei, Lei UL; Zhang, Ningbo et al

in IEEE Access (2017)

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See detailLive Data Analytics with Collaborative Edge and Cloud Processing in Wireless IoT Network
Sharma, Shree Krishna UL; Wang, Xianbin

in IEEE Access (2017)

Recently, big data analytics has received important attention in a variety of application domains including business, finance, space science, healthcare, telecommunication and Internet of Things (IoT ... [more ▼]

Recently, big data analytics has received important attention in a variety of application domains including business, finance, space science, healthcare, telecommunication and Internet of Things (IoT). Among these areas, IoT is considered as an important platform in bringing people, processes, data and things/objects together in order to enhance the quality of our everyday lives. However, the key challenges are how to effectively extract useful features from the massive amount of heterogeneous data generated by resource-constrained IoT devices in order to provide real-time information and feedback to the endusers, and how to utilize this data-aware intelligence in enhancing the performance of wireless IoT networks. Although there are parallel advances in cloud computing and edge computing for addressing some issues in data analytics, they have their own benefits and limitations. The convergence of these two computing paradigms, i.e., massive virtually shared pool of computing and storage resources from the cloud and real-time data processing by edge computing, could effectively enable live data analytics in wireless IoT networks. In this regard, we propose a novel framework for coordinated processing between edge and cloud computing/processing by integrating advantages from both the platforms. The proposed framework can exploit the network-wide knowledge and historical information available at the cloud center to guide edge computing units towards satisfying various performance requirements of heterogeneous wireless IoT networks. Starting with the main features, key enablers and the challenges of big data analytics, we provide various synergies and distinctions between cloud and edge processing. More importantly, we identify and describe the potential key enablers for the proposed edge-cloud collaborative framework, the associated key challenges and some interesting future research directions. [less ▲]

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See detailOpen IoT Ecosystem for Sporting Event Management
Kubler, Sylvain UL; Robert, Jérémy UL; Främling, Kary et al

in IEEE Access (2017), 5(1), 7064-7079

By connecting devices, people, vehicles, and infrastructures everywhere in a city, governments and their partners can improve community well-being and other economic and financial aspects (e.g., cost and ... [more ▼]

By connecting devices, people, vehicles, and infrastructures everywhere in a city, governments and their partners can improve community well-being and other economic and financial aspects (e.g., cost and energy savings). Nonetheless, smart cities are complex ecosystems that comprise many different stakeholders (network operators, managed service providers, logistic centers, and so on), who must work together to provide the best services and unlock the commercial potential of the so-called Internet of Things (IoT). This is one of the major challenges that faces today’s smart city movement, and the emerging "API economy." Indeed, while new smart connected objects hit the market every day, they mostly feed "vertical silos" (e.g., vertical apps, siloed apps, and so on) that are closed to the rest of the IoT, thus hampering developers to produce new added value across multiple platforms and/or application domains. Within this context, the contribution of this paper is twofold: 1) present the strategic vision and ambition of the EU to overcome this critical vertical silos’ issue and 2) introduce the first building blocks underlying an open IoT ecosystem developed as part of an EU (Horizon 2020) Project and a joint project initiative (IoT-EPI). The practicability of this ecosystem, along with a performance analysis, is carried out considering a proof-of-concept for enhanced sporting event management in the context of the forthcoming FIFA World Cup 2022 in Qatar. [less ▲]

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See detailCrowdSenSim: a Simulation Platform for Mobile Crowdsensing in Realistic Urban Environments
Fiandrino, Claudio UL; Capponi, Andrea UL; Cacciatore, Giuseppe UL et al

in IEEE Access (2017)

Smart cities take advantage of recent ICT developments to provide added value to existing public services and improve quality of life for the citizens. The Internet of Things (IoT) paradigm makes the ... [more ▼]

Smart cities take advantage of recent ICT developments to provide added value to existing public services and improve quality of life for the citizens. The Internet of Things (IoT) paradigm makes the Internet more pervasive where objects equipped with computing, storage and sensing capabilities are interconnected with communication technologies. Because of the widespread diffusion of IoT devices, applying the IoT paradigm to smart cities is an excellent solution to build sustainable Information and Communication Technology (ICT) platforms. Having citizens involved in the process through mobile crowdsensing (MCS) techniques augments capabilities of these ICT platforms without additional costs. For proper operation, MCS systems require the contribution from a large number of participants. Simulations are therefore a candidate tool to assess the performance of MCS systems. In this paper, we illustrate the design of CrowdSenSim, a simulator for mobile crowdsensing. CrowdSenSim is designed specifically for realistic urban environments and smart cities services. We demonstrate the effectiveness of CrowdSenSim for the most popular MCS sensing paradigms (participatory and opportunistic) and we present its applicability using a smart public street lighting scenario. [less ▲]

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See detailSelf-Powered Two-Way Cognitive Relay Networks: Protocol Design and Performance Analysis
Nguyen; Jayakody; Chatzinotas, Symeon UL et al

in IEEE Access (2017)

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See detailResource allocation optimization in multi-user multi-cell massive MIMO networks considering pilot contamination
Nguyen, Tri Minh; Ha, Vu Nguyen UL; Le, Long Bao

in IEEE Access (2015), 3

In this paper, we study the joint pilot assignment and resource allocation for system energy efficiency (SEE) maximization in the multi-user and multi-cell massive multi-input multi-output network. We ... [more ▼]

In this paper, we study the joint pilot assignment and resource allocation for system energy efficiency (SEE) maximization in the multi-user and multi-cell massive multi-input multi-output network. We explicitly consider the pilot contamination effect during the channel estimation in the SEE maximization problem, which aims to optimize the power allocation, the number of activated antennas, and the pilot assignment. To tackle the SEE maximization problem, we transform it into a subtractive form, which can be solved more efficiently. In particular, we develop an iterative algorithm to solve the transformed problem where optimization of power allocation and number of antennas is performed, and then pilot assignment optimization is conducted sequentially in each iteration. To tackle the first sub-problem, we employ a successive convex approximation (SCA) technique to attain a solvable convex optimization problem. Moreover, we propose a novel iterative low-complexity algorithm based on the Hungarian method to solve the pilot assignment sub-problem. We also describe how the proposed solution approach can be useful to address the sum rate (SR) maximization problem. In addition to the algorithmic developments, we characterize the optimal structure of both SEE and SR maximization problems. The numerical studies are conducted to illustrate the convergence of the proposed algorithms, impacts of different parameters on the SR and SEE, and significant performance gains of the proposed solution compared the conventional design. [less ▲]

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