Analysis of CQI Traces from LTE MIMO Deployments and Impact on Classical Schedulers

Transmission power allocation in modern mobile telecommunication systems is one of the most important aspects affecting overall performance. Addressing this issue has huge impact on improving state-of-the-art for mobile telecommunication systems such as Long Term Evolution-Advanced by reducing inter-cell interference. To approach global optimality, some sort of cooperation between base stations is needed to overcome many shared limitations. This article proposes cooperative transmission power assignment mechanism based on the coordinated multiple points' concept. It provides mathematical treatment and technical development for the proposed solution. This solution works in distributed fashion which is one of the main strengths of our approach. The proposed transmission power mechanism adopts auction theory approach to allocate power level for each base station. The proposed solution has showed tremendous good performance in extensive simulation experiments.

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“…35 The received signal (y j i ) over ith resource block from jth base station with multiple antennas can be formulated as follows…”

Section: Evaluations and Discussionmentioning

confidence: 99%

“…The proposed solution can be improved by utilizing the fact that Long Term Evolution (LTE) systems implement multi-input multi-output (MIMO) in the base station side. 35 The received signal ( y i j ) over i th resource block from j th base station with multiple antennas can be formulated as follows…”

Section: Evaluations and Discussionmentioning

confidence: 99%

Spatial-based cooperative transmission power assignment for mobile telecommunication systems

Ahmed

Subramaniam

Zukarnain

et al. 2016

International Journal of Distributed Sensor Networks

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“…The fronthaul interface is modeled as a serial line of 10 Gbps. For CQI reports, we employ traces reported in [16] and assume a reporting interval of 5 ms, which is the typical value used in LTE to carry the CQI feedback over the Physical Uplink Shared Channel under fading conditions [3].The CQI feedback is used by the Du to determine the modulation and coding scheme for data transmissions to the end user.…”

Section: Evaluation Methodologymentioning

confidence: 99%

pDCell

Fiandrino

Oliva

Widmer

et al. 2019

Proceedings of the 20th International Conference on Distributed Computing and Networking

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To deal with increasingly demanding services and the rapid growth in number of devices and traffic, 5G and beyond mobile networks need to provide extreme capacity and peak data rates at very low latencies. Consequently, applications and services need to move closer to the users into so-called edge data centers. At the same time, there is a trend to virtualize core and radio access network functionalities and bring them to edge data centers as well. However, as is known from conventional data centers, legacy transport protocols such as TCP are vastly suboptimal in such a setting.In this work, we present pDCell, a transport design for mobile edge computing architectures that extends data center transport approaches to the mobile network domain. Specifically, pDCell ensures that data traffic from application servers arrives at virtual radio functions (i.e., C-RAN Central Units) timely to (i) minimize queuing delays and (ii) to maximize cellular network utilization. We show that pDCell significantly improves flow completion times compared to conventional transport protocols like TCP and data center transport solutions, and is thus an essential component for future mobile networks. CCS CONCEPTS• Networks → Network architectures; Transport protocols; Data center networks; Mobile networks;

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“…The radio channel conditions are simulated through periodic wideband CQI reports sent by the users to the network side every 5 ms [35]. These CQIs were obtained through an independent simulation, using the SimuLTE [36] and the OMNeT++ [37] tools: an LTE urban macrocell scenario was considered, comprising a single base station and 200 mobile users moving around with a random trajectory; all the CQIs reported by the users to the base station, during a time interval of 3 minutes (the optimal length of a YouTube video [38]), were saved.…”

Section: User Cqi and Throughputmentioning

confidence: 99%

QoE‐Aware Scheduling Algorithm for Adaptive HTTP Video Delivery in Wireless Networks

Rodrigues

Sousa

Queluz

et al. 2018

Wireless Communications and Mobile Computing

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In the last years, the video content consumed by mobile users has increased exponentially. Since mobile network capacity cannot be increased as fast as required, it is crucial to develop intelligent schedulers that allocate radio resources very efficiently and are able to provide a high Quality of Experience (QoE) to most of the users. This paper proposes a new and effective scheduling solution—the Maximum Buffer Filling (MBF) algorithm—which aims to increase the number of satisfied users in video streaming services provided by wireless networks. The MBF algorithm uses the current buffer level at the client side and the radio channel conditions, which are reported to the network by the client, as well as the bitrate of the requested video segment. The proposed scheduling strategy can also fulfill different satisfaction criteria, since it can be tuned to maximize the numbers of users with high QoE levels or to minimize the number of users with low QoE levels. A simulation framework was developed, considering a Long Term Evolution (LTE) scenario, in order to assess the performance of the proposed scheduling scheme and to compare it with other well-known scheduling solutions. The results show the superior performance achieved by the proposed technique, in terms of the number of satisfied and unsatisfied users.

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Analysis of CQI Traces from LTE MIMO Deployments and Impact on Classical Schedulers

Cited by 7 publications

References 13 publications

Spatial-based cooperative transmission power assignment for mobile telecommunication systems

Spatial-based cooperative transmission power assignment for mobile telecommunication systems

pDCell

QoE‐Aware Scheduling Algorithm for Adaptive HTTP Video Delivery in Wireless Networks

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