26 research outputs found
Performance optimization of next-generation data-driven wireless networks : a machine learning approach
No full textOrganisering av ekonomiskt bistånd – Perspektiv på en integrerad och en specialiserad kommun
No full textAuthor: Merima Colak Title: Organization of financial assistance - Perspective on an integrated and a specialized municipality [translated title] Supervisor: Håkan Johansson Assessor: Staffan Blomberg The purpose of this study was to examine how specialized and integrated social services manage their social assistance. In addition to this the aim was to examine how social workers and officials working within these two forms of organizations experience discretion. Furthermore, a goal of this paper was also to examine if the social workers felt that any of these two organizational forms had any impact on the workload and how that in such case manifested. The study was conducted through a qualitative research method. Six social workers and officials were interviewed for the study. The results from the study demonstrated that the employees in the integrated organization had more general work tasks and that those in the specialized organization had their tasks divided on different units that were more specialized. Findings also showed that employees in each municipality interpret discretion differently. The social workers from the integrated organization interpreted discretion in terms of being able to affect their client’s case. The social workers from the specialized organization, on the other hand, defined discretion as being able to influence their own daily work tasks. Conclusively, the study showed that workload exists in both municipalities and that it was manageable in each of the municipalities, the workload was however higher in the specialized one but it was no burden on the employees in neither the specialized nor the integrated. Key words: Discretion, organizational structure, integrated and specialized organizations, social assistance, workload. Key words: handlingsutrymme, organisationsstruktur, integrerade och specialiserade organisationer, ekonomiskt bistånd, arbetsbelastning
End-to-end learning from spectrum data : a deep learning approach for wireless signal identification in spectrum monitoring applications
No full textThis paper presents end-to-end learning from spectrum data an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural networks. End-to-end learning allows to: 1) automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments and 2) train wireless signal classifiers in one end-to-end step which eliminates the need for complex multi-stage machine learning processing pipelines. The purpose of this paper is to present the conceptual framework of end-to-end learning for spectrum monitoring and systematically introduce a generic methodology to easily design and implement wireless signal classifiers. Furthermore, we investigate the importance of the choice of wireless data representation to various spectrum monitoring tasks. In particular, two case studies are elaborated: 1) modulation recognition and 2) wireless technology interference detection. For each case study three convolutional neural networks are evaluated for the following wireless signal representations: temporal IQ data, the amplitude/phase representation, and the frequency domain representation. From our analysis, we prove that the wireless data representation impacts the accuracy depending on the specifics and similarities of the wireless signals that need to be differentiated, with different data representations resulting in accuracy variations of up to 29%. Experimental results show that using the amplitude/phase representation for recognizing modulation formats can lead to performance improvements up to 2% and 12% for medium to high SNR compared to IQ and frequency domain data, respectively. For the task of detecting interference, frequency domain representation outperformed amplitude/phase and IQ data representation up to 20%
A survey on machine learning-based performance improvement of wireless networks: PHY, MAC and network layer
No full textThis paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY,MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-ofservice (QoS) and quality-of-experience (QoE).We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.Signal Processing System
Towards a cognitive MAC layer: Predicting the MAC-level performance in dynamic WSN using machine learning
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Towards a cognitive MAC layer: Predicting the MAC-level performance in dynamic WSN using machine learning
No full textEnd-to-end learning from spectrum data a deep learning approach for wireless signal identification in spectrum monitoring applications
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