Mobile Data Offloading for Streaming-Class Traffic with QoS Guarantee

2015 ◽  
Vol 7 (4) ◽  
pp. 26-42
Author(s):  
Anusree Ajith ◽  
T. G. Venkatesh
Keyword(s):  
Mobile Network ◽  
Network Capacity ◽  
Live Streaming ◽  
Data Traffic ◽  
Mobile Data ◽  
Qos Guarantee ◽  
Real Time Traffic ◽  
Video And Audio ◽  
Mobile Network Operators ◽  
Mobile Data Offloading

Faced with the tremendous increase in the amount of data traffic and associated congestion, mobile network operators are moving towards Heterogeneous networks (HetNets), in the process of expanding network capacity. Offloading data traffic onto Wi-Fi in order to avoid congestion in the backbone is an important step in the evolution of HetNets. On-the-spot and delayed offloading have been widely studied in the literature. This paper proposes an offloading algorithm which has low computational complexity. The proposed algorithm offloads data based on a balking function which is dependent on present network condition. Using extensive simulations, the authors demonstrate that the proposed algorithm achieves reduction in mean transmission delay without sacrificing much on the offloading efficiency. This technique is more efficient and applicable to real-time traffic, like live streaming video and audio, which has short and stringent delay requirements or deadlines.

scholarly journals A Highly Effective Route for Real-Time Traffic Using an IoT Smart Algorithm for Tele-Surgery Using 5G Networks

2021 ◽  
Vol 10 (2) ◽  
pp. 30
Author(s):  
Radwan S. Abujassar ◽  
Husam Yaseen ◽  
Ahmad Samed Al-Adwan
Keyword(s):  
Live Streaming ◽  
Data Traffic ◽  
Data Packets ◽  
Real Time Traffic ◽  
Medical Quality ◽  
Optimal Smoothing ◽  
Patient Will ◽  
Three Stages ◽  
Exchange Data

Nowadays, networks use many different paths to exchange data. However, our research will construct a reliable path in the networks among a huge number of nodes for use in tele-surgery using medical applications such as healthcare tracking applications, including tele-surgery which lead to optimizing medical quality of service (m-QoS) during the COVID-19 situation. Many people could not travel due to the current issues, for fear of spreading the covid-19 virus. Therefore, our paper will provide a very trusted and reliable method of communication between a doctor and his patient so that the latter can do his operation even from a far distance. The communication between the doctor and his/her patient will be monitored by our proposed algorithm to make sure that the data will be received without delay. We test how we can invest buffer space that can be used efficiently to reduce delays between source and destination, avoiding loss of high-priority data packets. The results are presented in three stages. First, we show how to obtain the greatest possible reduction in rate variability when the surgeon begins an operation using live streaming. Second, the proposed algorithm reduces congestion on the determined path used for the online surgery. Third, we have evaluated the affection of optimal smoothing algorithm on the network parameters such as peak-to-mean ratio and delay to optimize m-QoS. We propose a new Smart-Rout Control algorithm (s-RCA) for creating a virtual smart path between source and destination to transfer the required data traffic between them, considering the number of hops and link delay. This provides a reliable connection that can be used in healthcare surgery to guarantee that all instructions are received without any delay, to be executed instantly. This idea can improve m-QoS in distance surgery, with trusted paths. The new s-RCA can be adapted with an existing routing protocol to track the primary path and monitor emergency packets received in node buffers, for direct forwarding via the demand path, with extended features.

scholarly journals Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1674 ◽  
Author(s):  
Daisuke Mochizuki ◽  
Yu Abiko ◽  
Takato Saito ◽  
Daizo Ikeda ◽  
Hiroshi Mineno
Keyword(s):  
Reinforcement Learning ◽  
Mobile Networks ◽  
Data Communication ◽  
Mobile Network ◽  
Utilization Efficiency ◽  
Bandwidth Utilization ◽  
Delay Tolerance ◽  
Data Offloading ◽  
Mobile Data ◽  
Mobile Data Offloading

The demand for mobile data communication has been increasing owing to the diversification of its purposes and the increase in the number of mobile devices accessing mobile networks. Users are experiencing a degradation in communication quality due to mobile network congestion. Therefore, improving the bandwidth utilization efficiency of cellular infrastructure is crucial. We previously proposed a mobile data offloading protocol (MDOP) for improving the bandwidth utilization efficiency. Although this method balances a load of evolved node B by taking into consideration the content delay tolerance, accurately balancing the load is challenging. In this paper, we apply deep reinforcement learning to MDOP to solve the temporal locality of a traffic. Moreover, we examine and evaluate the concrete processing while considering a delay tolerance. A comparison of the proposed method and bandwidth utilization efficiency of MDOP showed that the proposed method reduced the network traffic in excess of the control target value by 35% as compared with the MDOP. Furthermore, the proposed method improved the data transmission ratio by the delay tolerance range. Consequently, the proposed method improved the bandwidth utilization efficiency by learning how to provide the bandwidth to the user equipment when MDOP cannot be used to appropriately balance a load.

scholarly journals SDN-Based Mobile Data Offloading Scheme Using a Femtocell and WiFi Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Chang-Woo Ahn ◽  
Sang-Hwa Chung
Keyword(s):  
Target Selection ◽  
Network Capacity ◽  
Centralized Control ◽  
Data Traffic ◽  
Wifi Networks ◽  
Selection Scheme ◽  
Mobile Data ◽  
Available Bandwidth ◽  
Seamless Connectivity ◽  
The Many

Because of the many applications running on smartphones, the load of mobile data traffic on cellular networks is increasing rapidly. A femtocell is a solution to increase the cellular network capacity and coverage. However, because it uses the same frequency bands as a macrocell, interference problems have prevented its widespread adoption. In this paper, we propose a scheme for traffic offloading between femtocells and WiFi networks utilizing software-defined networking (SDN) technology. In the proposed offloading scheme, the SDN technology allows a terminal to maintain existing sessions after offloading through a centralized control of the SDN-based equipment. We also propose an offloading target selection scheme based on available bandwidth estimation and an association control mechanism to reduce the femtocell load while ensuring quality of service (QoS) in terms of throughput. Experimental results on an actual testbed showed that the proposed offloading scheme provides seamless connectivity and reduces the femtocell load by up to 46% with the aid of the proposed target selection scheme, while ensuring QoS after offloading. We also observed that the proposed target selection scheme offloads 28% more traffic to WiFi networks compared to received signal strength indicator-based target selection in a low background traffic environment.

scholarly journals Developing a Framework for Future Mobile Data Pricing

2017 ◽  
Vol 21 (2) ◽  
pp. 84 ◽  
Author(s):  
Michael Paetsch ◽  
Peter Dorčák ◽  
František Pollák ◽  
Ľubomír Štrba ◽  
Branislav Kršák
Keyword(s):  
Mobile Networks ◽  
Economic Value ◽  
Network Capacity ◽  
Data Traffic ◽  
Fixed Amount ◽  
Mobile Data ◽  
Data Value ◽  
Voice Calls ◽  
The Impact ◽  
New Framework

<p><strong>Purpose:</strong> The revenues for mobile data transmission overtook the revenue of voice calls for the first time in 2014 in the USA. It can be observed that demand for mobile data – largely driven by video and cloud - is increasing exponentially, while overall data revenue is rising only moderately. This will lead to insufficient revenues stream to increase investments into mobile networks and ensure quality service. Consequently, hereof network performance will deteriorate sharply. At the heart of the problem is the current global pricing regime of fixed multiple MB/GB bundles, irrespective of time of the day, intensity of usage (e.g. video vs. email) and underlying economic value of the data. A new framework is proposed as to optimize and align network capacity and implicit data value/utility, which is crucial to ensure customer satisfaction and access justice.</p><p><strong>Methodology/Approach:</strong> The fundamental differences in pricing voice and data in voice and/or data centric networks are analysed in detail. Information has been synthesized as to develop insights into the impact of different devises and type of digital traffic for the overall performance of mobile networks. Based hereupon, a new framework for mobile data has been proposed to address the increasing misalignment between network capacity, usage and underlying data value/utility. Initial solutions have been proposed and discussed.</p><p><strong>Findings:</strong> While voice calls are easily quantifiable and are largely predictable in its occurrence and network load implications, mobile data traffic shows very large variations depending on type of traffic. While social media messaging by many customers consumes very little capacity, consumption of video streaming by relatively few customers can lead already to network saturation.</p><p><strong>Research Limitation/implication:</strong> Carriers set prices for a fixed amount of data – irrespective of intensity and time of data traffic - which leads to sharp spiky type of traffic patterns essentially signalling sharp overuse during busy hours coexist with large period of underused times.</p><strong>Originality/Value of paper:</strong> A new framework for proposition building and particularly pricing of mobile data services is provided.

scholarly journals A Novel WLAN Roaming Decision and Selection Scheme for Mobile Data Offloading

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Nam Nguyen ◽  
Mohammad Arifuzzaman ◽  
Takuro Sato
Keyword(s):  
Traffic Load ◽  
Data Traffic ◽  
Mobile Nodes ◽  
Network Resource ◽  
Selection Scheme ◽  
Mobile Data ◽  
Access Points ◽  
The Right ◽  
Mobile Data Offloading ◽  
Point Of Service

The existing IEEE and 3GPP standards have laid the foundation for integrating cellular and WiFi network to deliver a seamless experience for the end-users when roaming across multiple access networks. However, in recent studies, the issue of making roaming decision and intelligently selecting the most preferable Point of Service to optimize network resource and improve end user’s experience has not been considered properly. In this paper, we propose a novel cellular and WiFi roaming decision and AP selection scheme based on state of the art, 3GPP TS24.312 and IEEE 802.11u, k standards. Our proposed scheme assists the mobile nodes to decide the right timing to make roaming decision and select preferable point of service based on the operator’s policies and real-time network condition. We also introduce our simulation model of a heterogeneous network with cellular and WiFi interworking as well as 3GPP ANDSF, TS24.312. It is a complete end-to-end system model from application to physical layer with considering user’s mobility and realistic traffic model. The proposed scheme outperformed the conventional WiFi selection scheme in terms of dynamically steering mobile node’s data traffic from macrocell to available Access Points. The proposed scheme increased the utilization and balanced the traffic load of access points and improved user’s experienced throughput.

Performance Evaluation of Ethernet Network for Mobile Fronthual Networks

2017 ◽  
Vol 7 (1) ◽  
pp. 287 ◽  
Author(s):  
Dawit Hadush Hailu
Keyword(s):  
Access Network ◽  
High Capacity ◽  
Mobile Network ◽  
Packet Delay ◽  
Separation Distance ◽  
Data Traffic ◽  
Link Length ◽  
Mobile Data ◽  
Radio Access ◽  
Ethernet Network

<p>Increasing mobile data traffic due to the rise of both smartphones and tablets has led to high-capacity demand of mobile data network. To meet the ever-growing capacity demand and reduce the cost of mobile network components, Cloud Radio Access Network (C-RAN) has emerged as a promising solution. In such network, the mobile operator’s Remote Radio Head (RRH) and Base Band Unit (BBU) are often separated and the connection between them has very tight timing and latency requirements imposed by Common Public Radio Interface (CPRI) and 3rd Generation Partnership Project (3GPP). This fronthaul connection is not yet provided by packet based network. To employ packet-based network for C-RAN fronthaul, the carried fronthaul traffic are needed to achieve the requirements of fronthaul streams. For this reason, the aim of this paper is focused on investigating and evaluating the feasibility of Ethernet networks for mobile fronthaul. The fronthaul requirements used to evaluate and investigate this network are maximum End to End (E2E) latency, Packet Loss Ratio (PLR) and Packet Delay Variation (PDV). The simulated results and numerical analysis confirm that the PDV and PLR of High Priority (HP) traffic in Ethernet network meet the requirements of mobile fronthaul using CPRI. However, the PDV of HP traffic meets the fronthaul network when the number of nodes in the Ethernet network is at most four. For Ethernet network, the number of nodes in the network limits the maximum separation distance between BBU and RRH (link length); for increasing the number of nodes, the link length decreases. Consequently, Radio over Ethernet (RoE) traffic should receive the priority and Quality of Service (QoS) HP can provide. On the other hand, Low Priority (LP) classes are not sensitive to QoS metrics and should be used for transporting time insensitive applications and services.</p>

A Human-in-the-Loop Architecture for Mobile Network: From the View of Large Scale Mobile Data Traffic

2017 ◽  
Vol 102 (3) ◽  
pp. 2233-2259
Author(s):  
Yuanyuan Qiao ◽  
Jianyang Yu ◽  
Wenhui Lin ◽  
Jie Yang
Keyword(s):  
Large Scale ◽  
Mobile Network ◽  
Data Traffic ◽  
Mobile Data ◽  
Human In The Loop

scholarly journals FDD LTE MIMO CLOSED-LOOP VS OPEN-LOOP PERFORMANCE EVALUATION IN COMMERCIAL NETWORK

2021 ◽  
Vol 13 (0) ◽  
pp. 1-6
Author(s):  
Darius Chmieliauskas
Keyword(s):  
Large Scale ◽  
Network Performance ◽  
Closed Loop ◽  
Mobile Network ◽  
Network Capacity ◽  
Open Loop ◽  
Lte Networks ◽  
Commercial Network ◽  
Mobile Network Operators ◽  
Growing Network

With a growing network traffic Mobile Network Operators (MNO) looking for ways to increase network capacity and improve customer experience. One of the ways is to find the best parameters from the set defined by 3GPP. In the study, closed-loop MIMO was compared to open-loop MIMO on the LTE FDD network. Network performance was evaluated in 3 different scenarios: slow and fast-moving UE under different SINR levels and large scale on 2T2R and 4T4R cells. The result shows gains of using closed-loop and it is recommended to use it commercial LTE networks.

scholarly journals Extending 5G Capacity Planning Through Advanced Subscriber Behavior-Centric Clustering

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1385
Author(s):  
Gonçalves ◽  
Sebastião ◽  
Souto ◽  
Correia
Keyword(s):  
Resource Management ◽  
Capacity Planning ◽  
Data Science ◽  
Planning Process ◽  
Network Planning ◽  
Mobile Network ◽  
Network Capacity ◽  
5G Networks ◽  
Planning Processes ◽  
Mobile Network Operators

This work focuses on providing enhanced capacity planning and resource management for 5G networks through bridging data science concepts with usual network planning processes. For this purpose, we propose using a subscriber-centric clustering approach, based on subscribers’ behavior, leading to the concept of intelligent 5G networks, ultimately resulting in relevant advantages and improvements to the cellular planning process. Such advanced data-science-related techniques provide powerful insights into subscribers’ characteristics that can be extremely useful for mobile network operators. We demonstrate the advantages of using such techniques, focusing on the particular case of subscribers’ behavior, which has not yet been the subject of relevant studies. In this sense, we extend previously developed work, contributing further by showing that by applying advanced clustering, two new behavioral clusters appear, whose traffic generation and capacity demand profiles are very relevant for network planning and resource management and, therefore, should be taken into account by mobile network operators. As far as we are aware, for network, capacity, and resource management planning processes, it is the first time that such groups have been considered. We also contribute by demonstrating that there are extensive advantages for both operators and subscribers by performing advanced subscriber clustering and analytics.

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