MOST: Mobile Broadband Network Optimization Using Planned Spatio-Temporal Events

Samulevicius, Saulius; Pedersen, Torben Bach; Sørensen, Troels B.

doi:10.1109/vtcspring.2015.7145938

Cited by 10 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an example, the inter-download times of video segments are predicted in [102], where the output sequences are the interdownload times of the already downloaded segments and the states are the instants of the next download request. ARIMA: [13], [38], [40], [46], [47], [54], [58], [59], [63], [100], [119] Kalman: [32], [ CF: [16], [134], [149] Cluster: [15], [34], [51], [117], [122], [123], [148], [156] Decision trees: [35], [98], [ Functional: [28], [29], [38], [64], [99], [104], [105] SVM: [51], [114], [139] ANN: [14], [48], [106], [ 2) Bayesian inference: This approach allows to make statements about what is unknown, by conditioning on what is known. Bayesian prediction can be summarized in the following steps: 1) define a model that expresses qualitative aspects of our knowledge but has unknown parameters, 2) specify a prior probability distribution for the unknown parameters, 3) compute the posterior probability distribution for the parameters, given the observed data, and 4) make predictions by averaging ove...…”

Section: Statistical Methods For Probabilistic Forecastingmentioning

confidence: 99%

“…By predicting throughput, packet loss and transmission delay half a second in advance, the authors propose to dynamically adjust application-level parameters of the reference video streaming or video conferencing services including the compression ratio of the video codec, the forward error correction code rate and the size of the de-jittering buffer. Traffic prediction is also addressed in [99], where the authors propose to use a database of events (concerts, gatherings, etc.) to improve the quality of the traffic prediction in case of unexpected traffic patterns and in [100], where a general predictive control framework along with Kalman filter is proposed to counteract the impact of network delay and packet loss.…”

Section: Traffic Contextmentioning

confidence: 99%

“…No entry can be associated to multiple centroids ( K j=1 c ij = 1, ∀i ∈ M). Clustering is applied in anticipatory networking to build a data-driven link model [51], to find similarities within vehicular paths [34], to identify social events [99] that might impact network performance, and to identify device types [93].…”

Section: B Similarity-based Classificationmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Bui

Cesana

Hosseini³

et al. 2017

IEEE Commun. Surv. Tutorials

217

137

View full text Add to dashboard Cite

A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks.

show abstract

Section: Statistical Methods For Probabilistic Forecastingmentioning

confidence: 99%

Section: Traffic Contextmentioning

confidence: 99%

Section: B Similarity-based Classificationmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Bui

Cesana

Hosseini³

et al. 2017

IEEE Commun. Surv. Tutorials

217

137

View full text Add to dashboard Cite

show abstract

“…Another solution is presented in [257]. In this work, the authors tackle the problems of improving traffic load and network planning.…”

Section: G Resource Optimizationmentioning

confidence: 99%

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

412

200

View full text Add to dashboard Cite

Abstract-In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future.

show abstract

“…In particular, communication networks nowadays generate a huge amount of data at the network infrastructure level and at the user/customer level, which contain a huge amount of useful information such as location information, mobility and call patterns [14]- [21]. To improve the network performance and enhance users' experiences, new machine learning methods for big data analytics in communication networks can extract relevant information from the network data while taking into account of limited communication resources, and then leverage the derived knowledge for autonomic network control and management as well as service provisioning [22]- [24]. Machine learning is also proved to be applicable for automatic network orchestration and network management [25].…”

Section: Introductionmentioning

confidence: 99%

A Supervised Learning Based QoS Assurance Architecture for 5G Networks

et al. 2019

View full text Add to dashboard Cite

The 5G networks are broadly characterized by three unique features: ubiquitous connectivity, extremely low latency, and extraordinary high-speed data transfer. The challenge of 5G is to assure the network performance and different quality of service (QoS) requirements of different services, such as machine type communication (MTC), enhanced mobile broad band (eMBB), and ultra-reliable low latency communications (URLLC) over 5G networks. Unlike the previous ''one size fits all'' system, the softwarization, slicing and network capability exposure of 5G provide dynamic programming capabilities for QoS assurance. With the increasing complexity and dynamics of the network behaviors, it is non-trivial for a programmer to develop traditional software codes to schedule the network resources based on expert knowledge, especially when there is no quantitative relationship among the network events and the QoS anomalies. Machine learning is a computer technology that gives computer systems the ability to learn with data and improve performance and accuracy of decision making on a specific task, without being explicitly programmed. The areas of machine learning and communication technology are converging. Supervised learning based QoS assurance architecture for 5G networks was proposed in this paper. The supervised machine learning mechanisms can intelligently learn the network environment and react to dynamic situations. They can learn from the fore passed QoS related information and anomalies, and further reconstruct the relationship between the fore passed data and the current QoS related anomalies automatically and accurately. They, then, can trigger automatic mitigation or provide suggestions. The supervised machine learning mechanisms can also predict future QoS related anomalies with high confidence. In this paper, a case study for QoS anomaly root cause tracking based on decision tree was given to validate the proposed framework architecture.

show abstract

MOST: Mobile Broadband Network Optimization Using Planned Spatio-Temporal Events

Cited by 10 publications

References 9 publications

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

A Supervised Learning Based QoS Assurance Architecture for 5G Networks

Contact Info

Product

Resources

About