Social-Aware Data Dissemination via Device-to-Device Communications: Fusing Social and Mobile Networks with Incentive Constraints

2019 ◽  
Vol 12 (3) ◽  
pp. 489-502 ◽  
Author(s):  
Yiming Zhao ◽  
Wei Song ◽  
Zhu Han
Keyword(s):  
Mobile Networks ◽  
Data Dissemination ◽  
Device To Device ◽  
Incentive Constraints

scholarly journals Handover Management for D2D Communication in 5G Networks

2020 ◽  
Vol 10 (12) ◽  
pp. 4409
Author(s):  
Wei Kuang Lai ◽  
Chin-Shiuh Shieh ◽  
Fu-Sheng Chou ◽  
Chia-Yu Hsu ◽  
Meng-Han Shen
Keyword(s):  
Mobile Networks ◽  
Base Station ◽  
Base Stations ◽  
D2d Communication ◽  
Expected Improvement ◽  
5G Networks ◽  
Handover Management ◽  
Current Standard ◽  
Handover Procedure ◽  
Device To Device

This study addresses the handover management issue for Device-to-Device communication in fifth-generation (5G) networks. The Third Generation Partnership Project (3GPP) drafted a standard for proximity services (ProSe), also named device-to-device (D2D) communication, which is a promising technology in offering higher throughput and lower latency services to end users. Handover is an essential issue in wireless mobile networks due to the mobility of user equipment (UE). Specifically, we need to transfer an ongoing connection from an old E-UTRAN Node B (eNB) to a new one, so that the UE can retain its connectivity. In the data plane, both parties of a D2D pair can communicate directly with each other without the involvement of the base station. However, in the control plane, devices must be connected to the eNB for tasks such as power control and resource allocation. In the current standard of handover scheme, the number of unnecessary handovers would be increased by the effect of shadowing fading on two devices. More important, the handover mechanism for D2D pairs is not standardized yet. LTE-A only considers the handover procedure of a single user. Therefore, when a D2D pair moves across cell boundaries, the control channels of the two UEs may connect to different base stations and result in increased latency due to the exchange of D2D related control messages. Hence, we propose a handover management scheme for D2D communication to let both parties of a D2D pair handover to the same destination eNB at the same time. By doing so, the number of unnecessary handovers, as well as the handover latency, can be reduced. In the proposed method, we predict the destination eNB of D2D users based on their movements and the received signal characteristics. Subsequently, we make a handover decision for each D2D pair by jointly factoring in the signal quality and connection stability. Expected improvement can be attained, as revealed in the simulation. Unnecessary handover can be avoided. Consequently, both UEs of a D2D pair reside in the same cell and, therefore, result in increased throughput and decreased delay.

scholarly journals A Deep Learning Based Transmission Algorithm for Mobile Device-to-Device Networks

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1361 ◽  
Author(s):  
Tae-Won Ban ◽  
Woongsup Lee
Keyword(s):  
Deep Learning ◽  
Mobile Networks ◽  
Spectral Efficiency ◽  
Radio Resource Management ◽  
Cellular Communications ◽  
D2d Communications ◽  
Radio Resource ◽  
Next Generation Mobile Networks ◽  
Device To Device ◽  
Mobile Caching

Recently, device-to-device (D2D) communications have been attracting substantial attention because they can greatly improve coverage, spectral efficiency, and energy efficiency, compared to conventional cellular communications. They are also indispensable for the mobile caching network, which is an emerging technology for next-generation mobile networks. We investigate a cellular overlay D2D network where a dedicated radio resource is allocated for D2D communications to remove cross-interference with cellular communications and all D2D devices share the dedicated radio resource to improve the spectral efficiency. More specifically, we study a problem of radio resource management for D2D networks, which is one of the most challenging problems in D2D networks, and we also propose a new transmission algorithm for D2D networks based on deep learning with a convolutional neural network (CNN). A CNN is formulated to yield a binary vector indicating whether to allow each D2D pair to transmit data. In order to train the CNN and verify the trained CNN, we obtain data samples from a suboptimal algorithm. Our numerical results show that the accuracies of the proposed deep learning based transmission algorithm reach about 85%∼95% in spite of its simple structure due to the limitation in computing power.

scholarly journals D2D Big Data: Content Deliveries over Wireless Device-to-Device Sharing in Large-Scale Mobile Networks

2018 ◽  
Vol 25 (1) ◽  
pp. 32-38 ◽  
Author(s):  
Xiaofei Wang ◽  
Yuhua Zhang ◽  
Victor C. M. Leung ◽  
Nadra Guizani ◽  
Tianpeng Jiang
Keyword(s):  
Big Data ◽  
Mobile Networks ◽  
Large Scale ◽  
Device To Device ◽  
Data Content ◽  
Wireless Device

scholarly journals A baseline for context-aware system for safety messages dissemination in VANETs

2019 ◽  
pp. 9-18
Author(s):  
Alexis Yáñez ◽  
Sandra Céspedes ◽  
Javier Rubio-Loyola
Keyword(s):  
Mobile Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Data Dissemination ◽  
Context Aware ◽  
Traffic Conditions ◽  
Communication Environments ◽  
Hoc Networks ◽  
Context Aware System ◽  
Parameter Values

Vehicular ad-hoc networks (VANETs) are mobile networks with highly dynamic contexts of operation. The design and application of context-aware systems could be critical to enhance the performance of protocols deployed in VANETs, which depend on both network and traffic conditions, to detect hostile communication environments, as well as to offer a novel way to make decisions in real-time. In this paper, we present a baseline study for the design of a context-aware system for dissemination of safety messages in VANETs. We model a realistic vehicular intersection and perform extensive simulations to evaluate two well-known dissemination mechanisms, namely the Slotted 1-persistence and the Traffic Adaptive Data Dissemination (TrAD) Protocol, with a set of parameters according to different operation scenarios. We show how the dissemination mechanisms’ performance could be improved, or worsened, by choosing a different set of parameter values. By characterizing the operation scenarios, it is possible to adequate the parameters of the dissemination mechanism with the values that work best in a given context, improving in this way the general performance of the dissemination mechanisms under study.

Interference Management Techniques for Device-to-Device Communications

2021 ◽  
pp. 383-409
Author(s):  
Weston Mwashita ◽  
Marcel Ohanga Odhiambo
Keyword(s):  
Mobile Networks ◽  
Interference Management ◽  
Research Community ◽  
Comprehensive Analysis ◽  
D2d Communication ◽  
Next Generation ◽  
Next Generation Mobile Networks ◽  
Device To Device ◽  
Management Techniques ◽  
Art Research

The snowballing of many different electronic gadgets connected to different networks and to the internet is a clear indication that the much-anticipated internet of things (IoT) is fast becoming a reality. It is generally agreed that the next generation mobile networks should offer wireless connection to anything and anyone with a proper enabling device at any time leading to the full realization of IoT. Device-to device (D2D) communication is one technology that the research community believes will aid the implementation of the next generation of mobile networks, specifically 5G. Full roll out of D2D is however being impeded by the resulting interference. This chapter looks at the state-of-the-art research works on interference management technologies proposed for device-to-device communications. A comprehensive analysis of the proposed schemes is given and open challenges and issues that need to be considered by researchers in D2D communication for it to become a key enabler for 5G technology are highlighted and recommendations provided.

Interference Management Techniques for Device-to-Device Communications

2019 ◽  
pp. 219-245 ◽  
Author(s):  
Weston Mwashita ◽  
Marcel Ohanga Odhiambo
Keyword(s):  
Mobile Networks ◽  
Interference Management ◽  
Research Community ◽  
Comprehensive Analysis ◽  
D2d Communication ◽  
Next Generation ◽  
Next Generation Mobile Networks ◽  
Device To Device ◽  
Management Techniques ◽  
Art Research

The snowballing of many different electronic gadgets connected to different networks and to the internet is a clear indication that the much-anticipated internet of things (IoT) is fast becoming a reality. It is generally agreed that the next generation mobile networks should offer wireless connection to anything and anyone with a proper enabling device at any time leading to the full realization of IoT. Device-to device (D2D) communication is one technology that the research community believes will aid the implementation of the next generation of mobile networks, specifically 5G. Full roll out of D2D is however being impeded by the resulting interference. This chapter looks at the state-of-the-art research works on interference management technologies proposed for device-to-device communications. A comprehensive analysis of the proposed schemes is given and open challenges and issues that need to be considered by researchers in D2D communication for it to become a key enabler for 5G technology are highlighted and recommendations provided.

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