Data rate fluctuations from user perspective in 4G mobile networks

Skočir, Pavle; Katušić, Damjan; Novotni, Ivan; Bojić, Iva; Jezic, Gordan

doi:10.1109/softcom.2014.7039086

Cited by 12 publications

(4 citation statements)

References 1 publication

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“…Big latency causes big RTTs. RTT, throughput and latency are dependable KPIs for user experience metering of network speed [10]. Hence, our system mainly depends on those KPIs for user experience analysis.…”

Section: Background and Related Workmentioning

confidence: 99%

Proactive Metering of Mobile Internet User Experience

Uzun¹,

Abul

2022

Balkan Journal of Electrical and Computer Engineering

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Having 67% worldwide share, mobile internet is very important for Internet Service Providers (ISPs). Since mobile Internet access is a collective service, Key Performance Indicators (KPIs) measuring quality of data traffic on select network segments/servers may not correctly indicate true user experience. For this reason, mobile ISPs are investing in sophisticated high-end commercial speed analysis systems which typically collect and analyze network traffic data from key network segments/servers. Unfortunately, their utility is quite limited as long as the proactive network intervention is considered. In this work, we develop a MapReduce based network speed analysis system which measures end-to-end network speed to quantify true user experience across multiple geographic regions and service categories. Also functioning as an online decision support system, it enables network administrators with timely ISP network intervention right before potential arrival of mass number of user complaints. The system has been tested with a leading mobile ISP in Turkey. The results confirm its effectiveness.

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Section: Background and Related Workmentioning

confidence: 99%

Proactive Metering of Mobile Internet User Experience

Uzun¹,

Abul

2022

Balkan Journal of Electrical and Computer Engineering

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“…Others apply custom performance measurement tools that better fit their research aims [40,41]. The MBB data measurement can be conducted in indoor environments [42,43], within university campuses [44][45][46][47], along roads [48,49], in different locations of the same city [41,50], in several cities [6,51], in different countries [52] or at specific large-scale events [53]. To preserve the privacy of the measurement analysis outcomes, the names of MBB service operators may be coded to conceal their identities [44], while others choose to surprisingly declare the findings of operators' measurement results, as in [45,46], or as by Ofcom in the United Kingdom (UK) [6].…”

Section: Introductionmentioning

confidence: 99%

“…The measured data referred to MBB performance metrics such as the Reference Signal Received Power (RSRP)/received signal strength [44-46, 48, 49, 53], downlink/uplink data rate [6,40,42,50,52,53], latency/Round Trip Time (RTT) [6,40,42,45,51], etc. Some works categorised the measurement datasets as either during the weekdays or weekends [49,50], while others classified datasets during peak or off-peak hours [45,51]. Most studies in the literature revealed that Long-Term Evolution (LTE)/4G networks provide better services than those of Universal Mobile Telecommunications System (UMTS)/3G networks, with only marginal differences from the target figures as set by 3GPP.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Mobile Broadband Networks With 5G Trends and Beyond: Urban Areas Scope in Malaysia

et al. 2021

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The performance of Mobile Broadband (MBB) services of Fourth Generation (4G) and Third Generation (3G) mobile networks over urban morphology is studied in Malaysia based on experimental measurements of drive test data. The aim of this study is to provide a roadmap for service providers to establish a realistic plan for future Fifth Generation (5G) networks. This work is a continuation of our previous work for the scope of rural areas in Malaysia. The MBB measurement data have been gathered through drive tests conducted in the urban areas of four states throughout Malaysia (namely, Klang Valley/Selangor, Johor, Sarawak and Sabah) to characterise and analyse MBB performances. The gathered data are from the cities, highways and federal roads of the chosen states, and encompasses three main Mobile Network Operators (MNOs). Data has been collected in a time span of 2 months, from January to February, using the Samsung Galaxy S6 smartphone handsets. Four MBB Key Performance Indicators (KPIs) are considered in this study (coverage, latency, satisfaction and speed) for two MBB services (web browsing and video streaming). The measurement data for characterising the performance of each MBB service has been collected using a dedicated smartphone handset. YouTube videos with 720p and 1080p resolutions have been sequentially streamed to assess the performance of MBB video-streaming services. Three distinct websites (Google, Instagram and mStar) have been accessed to evaluate the performance of MBB web-browsing services. The experimental methodology of this study integrates several diversified elements including four different urban states, four distinct KPIs, three main MNOs, two MBB services and two radio networks (4G and 3G), which are both accessible by the smartphones when available to mimic real-world scenarios. The results of this study reveal that the performance of 4G radio networks is generally superior to that of 3G . For instance, 4G networks achieved a vMOS score of more than 3 for both MBB video-streaming and web-browsing services, while 3G networks scored less than 3 across all four study areas. The analysed experimental results confirmed that compared to 3G networks, 4G technology presents an enhancement factor of up to 1.6 and 4.2 in download speed when streaming a video and browsing a web page, respectively. The study outcomes can contribute to the efficient planning of nonstandalone (NSA) 5G networks in Malaysia where 5G networks will be aided by existing 4G infrastructures. Analysing the 4G coverage performance is the first step towards deciding the deployment rate of NSA 5G in Malaysia.INDEX TERMS Mobile Broadband, urban areas, Malaysia, 3G and 4G networks, plans for 5G technology in Malaysia

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“…The user's off-peak data rate is expected to be much greater than that of rush hours in the 4G wireless networks. These variable rates are due to network congestions when they are overloaded with connections that significantly lower download speeds [2]. These speed changes will become an issue as mobile services expand.…”

Section: Introductionmentioning

confidence: 99%

Smart Technique for Cache-Assisted Device to Device Communications

2020

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The emergence of smart terminals with the consequence of higher network traffic growth rates, has increased the demand for new techniques that are needed to meet user requirements for high-quality communication and social media services. One of these techniques is Device-to-Device (D2D) communication, allowing direct data transmission of popular files between users. This is an advantage during rush hours where there is no access to the traffic via a base station (BS), thereby reducing network loads. In this paper, we propose a new caching paradigm for D2D communication to achieve the goal of rising network offloading. A Smart Adaptive Algorithm (SAA) is proposed that smartly selects users with higher probabilities of sharing more data, then places popular files in their cache. This is done to optimize network offloading at minimal consumed energy by the user (user cost), taking into account the user speed, location, predicted zones, interests, and the size of the requested file. The data offloading ratio is taken as a performance metric in evaluating network offloading. It is defined as the proportion of requested data that can be delivered via D2D links. The offloading ratio is calculated in different scenarios in order to assess the influence of proactive caching on core network load. To minimize the complexity of the algorithm, and to respond in a shorter time period to the number of requests, user demand shall be handled with both the base station and the user's device application. For the simulation and implementation of the proposed algorithm, a human mobility model that predicts different human mobility behaviors is used. The simulation results reveal that the offloading ratio is significantly affected by the size of the file transmitted via D2D connection as well as the user speed. The amount of increase in the offloading ratio due to the increase in the user speed is based on the size of the files transmitted through D2D connection. The smaller files transmitted via D2D connection would result in a significant increase in the offloading ratio, whereas the larger file size would result in a less change in the offloading ratio for higher speed users. The simulation results also reveal that higher speed users share more data at a high bit rates compared to users with lower speeds. Simulation results also show that the proposed SAA has superior performance as compared to other algorithms available in literature.

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Data rate fluctuations from user perspective in 4G mobile networks

Cited by 12 publications

References 1 publication

Proactive Metering of Mobile Internet User Experience

Proactive Metering of Mobile Internet User Experience

Performance Analysis of Mobile Broadband Networks With 5G Trends and Beyond: Urban Areas Scope in Malaysia

Smart Technique for Cache-Assisted Device to Device Communications

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