Survey on QoE\QoS Correlation Models For Multimedia Services

This paper presents a brief review of some existing correlation models which attempt to map Quality of Service (QoS) to Quality of Experience (QoE) for multimedia services. The term QoS refers to deterministic network behaviour, so that data can be transported with a minimum of packet loss, delay and maximum bandwidth. QoE is a subjective measure that involves human dimensions; it ties together user perception, expectations, and experience of the application and network performance. The Holy Grail of subjective measurement is to predict it from the objective measurements; in other words predict QoE from a given set of QoS parameters or vice versa. Whilst there are many quality models for multimedia, most of them are only partial solutions to predicting QoE from a given QoS. This contribution analyses a number of previous attempts and optimisation techniquesthat can reliably compute the weighting coefficients for the QoS/QoE mapping.Comment: 20 pages, International Journal of Distributed and Parallel Systems (IJDPS

The Convergence of Machine Learning and Communications

The areas of machine learning and communication technology are converging. Today's communications systems generate a huge amount of traffic data, which can help to significantly enhance the design and management of networks and communication components when combined with advanced machine learning methods. Furthermore, recently developed end-to-end training procedures offer new ways to jointly optimize the components of a communication system. Also in many emerging application fields of communication technology, e.g., smart cities or internet of things, machine learning methods are of central importance. This paper gives an overview over the use of machine learning in different areas of communications and discusses two exemplar applications in wireless networking. Furthermore, it identifies promising future research topics and discusses their potential impact.Comment: 8 pages, 4 figure

Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum- and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design and computation resource management, while ML based networking focuses on the applications in clustering, base station switching control, user association and routing. Moreover, literatures in each aspect is organized according to the adopted ML techniques. In addition, several conditions for applying ML to wireless communication are identified to help readers decide whether to use ML and which kind of ML techniques to use, and traditional approaches are also summarized together with their performance comparison with ML based approaches, based on which the motivations of surveyed literatures to adopt ML are clarified. Given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where ML based network slicing, infrastructure update to support ML based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation and so on are discussed.Comment: 34 pages,8 figure

White Paper on Critical and Massive Machine Type Communication Towards 6G

The society as a whole, and many vertical sectors in particular, is becoming increasingly digitalized. Machine Type Communication (MTC), encompassing its massive and critical aspects, and ubiquitous wireless connectivity are among the main enablers of such digitization at large. The recently introduced 5G New Radio is natively designed to support both aspects of MTC to promote the digital transformation of the society. However, it is evident that some of the more demanding requirements cannot be fully supported by 5G networks. Alongside, further development of the society towards 2030 will give rise to new and more stringent requirements on wireless connectivity in general, and MTC in particular. Driven by the societal trends towards 2030, the next generation (6G) will be an agile and efficient convergent network serving a set of diverse service classes and a wide range of key performance indicators (KPI). This white paper explores the main drivers and requirements of an MTC-optimized 6G network, and discusses the following six key research questions: - Will the main KPIs of 5G continue to be the dominant KPIs in 6G; or will there emerge new key metrics? - How to deliver different E2E service mandates with different KPI requirements considering joint-optimization at the physical up to the application layer? - What are the key enablers towards designing ultra-low power receivers and highly efficient sleep modes? - How to tackle a disruptive rather than incremental joint design of a massively scalable waveform and medium access policy for global MTC connectivity? - How to support new service classes characterizing mission-critical and dependable MTC in 6G? - What are the potential enablers of long term, lightweight and flexible privacy and security schemes considering MTC device requirements?Comment: White paper by http://www.6GFlagship.co

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Gaussian-based Probabilistic Deep Supervision Network for Noise-Resistant QoS Prediction

Quality of Service (QoS) prediction is an essential task in recommendation systems, where accurately predicting unknown QoS values can improve user satisfaction. However, existing QoS prediction techniques may perform poorly in the presence of noise data, such as fake location information or virtual gateways. In this paper, we propose the Probabilistic Deep Supervision Network (PDS-Net), a novel framework for QoS prediction that addresses this issue. PDS-Net utilizes a Gaussian-based probabilistic space to supervise intermediate layers and learns probability spaces for both known features and true labels. Moreover, PDS-Net employs a condition-based multitasking loss function to identify objects with noise data and applies supervision directly to deep features sampled from the probability space by optimizing the Kullback-Leibler distance between the probability space of these objects and the real-label probability space. Thus, PDS-Net effectively reduces errors resulting from the propagation of corrupted data, leading to more accurate QoS predictions. Experimental evaluations on two real-world QoS datasets demonstrate that the proposed PDS-Net outperforms state-of-the-art baselines, validating the effectiveness of our approach

CHAOS: Accurate and Realtime Detection of Aging-Oriented Failure Using Entropy

Even well-designed software systems suffer from chronic performance degradation, also named "software aging", due to internal (e.g. software bugs) and external (e.g. resource exhaustion) impairments. These chronic problems often fly under the radar of software monitoring systems before causing severe impacts (e.g. system failure). Therefore it's a challenging issue how to timely detect these problems to prevent system crash. Although a large quantity of approaches have been proposed to solve this issue, the accuracy and effectiveness of these approaches are still far from satisfactory due to the insufficiency of aging indicators adopted by them. In this paper, we present a novel entropy-based aging indicator, Multidimensional Multi-scale Entropy (MMSE). MMSE employs the complexity embedded in runtime performance metrics to indicate software aging and leverages multi-scale and multi-dimension integration to tolerate system fluctuations. Via theoretical proof and experimental evaluation, we demonstrate that MMSE satisfies Stability, Monotonicity and Integration which we conjecture that an ideal aging indicator should have. Based upon MMSE, we develop three failure detection approaches encapsulated in a proof-of-concept named CHAOS. The experimental evaluations in a Video on Demand (VoD) system and in a real-world production system, AntVision, show that CHAOS can detect the failure-prone state in an extraordinarily high accuracy and a near 0 Ahead-Time-To-Failure (ATTF). Compared to previous approaches, CHAOS improves the detection accuracy by about 5 times and reduces the ATTF even by 3 orders of magnitude. In addition, CHAOS is light-weight enough to satisfy the realtime requirement.Comment: 1

FES: A Fast Efficient Scalable QoS Prediction Framework

Quality-of-Service prediction of web service is an integral part of services computing due to its diverse applications in the various facets of a service life cycle, such as service composition, service selection, service recommendation. One of the primary objectives of designing a QoS prediction algorithm is to achieve satisfactory prediction accuracy. However, accuracy is not the only criteria to meet while developing a QoS prediction algorithm. The algorithm has to be faster in terms of prediction time so that it can be integrated into a real-time recommendation or composition system. The other important factor to consider while designing the prediction algorithm is scalability to ensure that the prediction algorithm can tackle large-scale datasets. The existing algorithms on QoS prediction often compromise on one goal while ensuring the others. In this paper, we propose a semi-offline QoS prediction model to achieve three important goals simultaneously: higher accuracy, faster prediction time, scalability. Here, we aim to predict the QoS value of service that varies across users. Our framework consists of multi-phase prediction algorithms: preprocessing-phase prediction, online prediction, and prediction using the pre-trained model. In the preprocessing phase, we first apply multi-level clustering on the dataset to obtain correlated users and services. We then preprocess the clusters using collaborative filtering to remove the sparsity of the given QoS invocation log matrix. Finally, we create a two-staged, semi-offline regression model using neural networks to predict the QoS value of service to be invoked by a user in real-time. Our experimental results on four publicly available WS-DREAM datasets show the efficiency in terms of accuracy, scalability, fast responsiveness of our framework as compared to the state-of-the-art methods.Comment: 13 pages, 15 figure