Article
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A TSENet Model for Predicting Cellular Network Traffic
Version 1
: Received: 6 February 2024 / Approved: 9 February 2024 / Online: 9 February 2024 (07:48:07 CET)
A peer-reviewed article of this Preprint also exists.
Wang, J.; Shen, L.; Fan, W. A TSENet Model for Predicting Cellular Network Traffic. Sensors 2024, 24, 1713. Wang, J.; Shen, L.; Fan, W. A TSENet Model for Predicting Cellular Network Traffic. Sensors 2024, 24, 1713.
Abstract
Wireless Sensor Networks (WSNs) are gaining traction in the realm of network communication, renowned for their adaptability, configuration, and flexibility. The forthcoming network traffic within WSNs can be forecasted through temporal sequence models. In this correspondence, We present a method (TSENet) that can accurately predict the traffic in the cellular network. TSENet is composed of transformers and self-attention network. We have designed a temporal transformer module specifically for extracting temporal features. This module accomplishes this by modeling the traffic flow within each grid of the communication network at both near-term and periodical intervals. Simultaneously, we amalgamate the spacial features of each grid with information from its correlated grids, generating spacial predictions within the spacial transformer. Furthermore, we employ self-attention aggregation to capture dependencies between external factor features and celluar data features. Empirical assessments performed on a genuine cellular traffic dataset offer compelling evidence substantiating the efficacy of TSENet.
Keywords
Cellular network; WSNs; TSENet; Traffic predict; Self-attention aggregation
Subject
Engineering, Telecommunications
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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