Big-data-driven and AI-based framework to enable personalization in wireless networks

• URL: http://arxiv.org/abs/2306.04887v1
• Date: Thu, 8 Jun 2023 02:30:55 GMT
• Title: Big-data-driven and AI-based framework to enable personalization in wireless networks
• Authors: Rawan Alkurd, Ibrahim Abualhaol, and Halim Yanikomeroglu
• Abstract summary: We propose a big-data-driven and AI-based personalization framework to integrate personalization into wireless networks. Based on each user's actual requirements and context, a multi-objective formulation enables the network to micromanage and optimize the provided user satisfaction levels simultaneously.
• Score: 20.26379197206863
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Current communication networks use design methodologies that prevent the realization of maximum network efficiency. In the first place, while users' perception of satisfactory service diverges widely, current networks are designed to be a "universal fit," where they are generally over-engineered to deliver services appealing to all types of users. Also, current networks lack user-level data cognitive intelligence that would enable fast personalized network decisions and actions through automation. Thus, in this article, we propose the utilization of AI, big data analytics, and real-time non-intrusive user feedback in order to enable the personalization of wireless networks. Based on each user's actual QoS requirements and context, a multi-objective formulation enables the network to micro-manage and optimize the provided QoS and user satisfaction levels simultaneously. Moreover, in order to enable user feedback tracking and measurement, we propose a user satisfaction model based on the zone of tolerance concept. Furthermore, we propose a big-data-driven and AI-based personalization framework to integrate personalization into wireless networks. Finally, we implement a personalized network prototype to demonstrate the proposed personalization concept and its potential benefits through a case study. The case study shows how personalization can be realized to enable the efficient optimization of network resources such that certain requirement levels of user satisfaction and revenue in the form of saved resources are achieved.

Related papers

• Towards Intelligent Network Management: Leveraging AI for Network Service Detection [0.0]
  This study focuses on leveraging Machine Learning methodologies to create an advanced network traffic classification system. We introduce a novel data-driven approach that excels in identifying various network service types in real-time. Our system demonstrates a remarkable accuracy in distinguishing the network services.
  arXiv  Detail & Related papers  (2023-10-14T16:06:11Z)
• Personalized Resource Allocation in Wireless Networks: An AI-Enabled and Big Data-Driven Multi-Objective Optimization [22.77447144331876]
  The use of Artificial Intelligence (AI) is envisioned for wireless network design and optimization. One of the main future applications of AI is enabling user-level personalization for numerous use cases. The personalization technology advocated in this article is supported by an intelligent big data-driven layer designed to micro-manage the scarce network resources.
  arXiv  Detail & Related papers  (2023-07-08T00:26:36Z)
• Large Language Models Empowered Autonomous Edge AI for Connected Intelligence [51.269276328087855]
  Edge artificial intelligence (Edge AI) is a promising solution to achieve connected intelligence. This article presents a vision of autonomous edge AI systems that automatically organize, adapt, and optimize themselves to meet users' diverse requirements.
  arXiv  Detail & Related papers  (2023-07-06T05:16:55Z)
• Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks [68.00382171900975]
  In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. We present the AIGC-as-a-service concept and discuss the challenges in deploying A at the edge networks. We propose a deep reinforcement learning-enabled algorithm for optimal ASP selection.
  arXiv  Detail & Related papers  (2023-01-09T09:30:23Z)
• Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees [81.6471440778355]
  We propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users.
  arXiv  Detail & Related papers  (2022-09-04T06:22:37Z)
• Energy-Aware Edge Association for Cluster-based Personalized Federated Learning [2.3262774900834606]
  Federated Learning over wireless network enables data-conscious services by leveraging ubiquitous intelligence at network edge for privacy-preserving model training. We propose clustered federated learning to group user devices with similar preference and provide each cluster with a personalized model. We formulate an accuracy-cost trade-off optimization problem by jointly considering model accuracy, communication resource allocation and energy consumption.
  arXiv  Detail & Related papers  (2022-02-06T07:58:41Z)
• Offline Contextual Bandits for Wireless Network Optimization [107.24086150482843]
  In this paper, we investigate how to learn policies that can automatically adjust the configuration parameters of every cell in the network in response to the changes in the user demand. Our solution combines existent methods for offline learning and adapts them in a principled way to overcome crucial challenges arising in this context.
  arXiv  Detail & Related papers  (2021-11-11T11:31:20Z)
• Transferable and Distributed User Association Policies for 5G and Beyond Networks [9.727134312677842]
  We study the problem of user association, namely finding the optimal assignment of user equipment to base stations. We propose a novel distributed policy network architecture, which is transferable among users with zero-shot generalization capability.
  arXiv  Detail & Related papers  (2021-06-04T15:08:39Z)
• Deep-Mobility: A Deep Learning Approach for an Efficient and Reliable 5G Handover [0.0]
  5G cellular networks are being deployed all over the world and this architecture supports ultra-dense network (UDN) deployment. Small cells have a very important role in providing 5G connectivity to the end users. In contrast to any traditional handover improvement scheme, we develop a 'Deep-Mobility' model by implementing a deep learning neural network (DLNN) to manage network mobility.
  arXiv  Detail & Related papers  (2021-01-17T00:31:37Z)
• Unsatisfied Today, Satisfied Tomorrow: a simulation framework for performance evaluation of crowdsourcing-based network monitoring [68.8204255655161]
  We propose an empirical framework tailored to assess the quality of the detection of under-performing cells. The framework simulates both the processes of satisfaction surveys delivery and users satisfaction prediction. We use the simulation framework to test empirically the performance of under-performing sites detection in general scenarios.
  arXiv  Detail & Related papers  (2020-10-30T10:03:48Z)
• Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem [55.21502268698577]
  This work investigates the task of unsupervised model personalization, adapted to continually evolving, unlabeled local user images. We provide a novel Dual User-Adaptation framework (DUA) to explore the problem. This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device.
  arXiv  Detail & Related papers  (2020-03-30T09:35:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.