Cellular Traffic Prediction Based on an Intelligent Model

Alsaade, Fawaz Waselallah; Al-Adhaileh, Mosleh Hmoud

doi:10.1155/2021/6050627

Cited by 16 publications

(3 citation statements)

References 47 publications

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“…Maximum data denoted as x max , and x min is the minimum of the data. New min and New max is the zero and one respectively [18]. After Normalization and transformation, we divided data into two parts: test and train.…”

Section: System Modelmentioning

confidence: 99%

Deep Learning Based Fusion Model for Multivariate LTE Traffic Forecasting and Optimized Radio Parameter Estimation

et al. 2023

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With the evaluation of cellular network internet data traffic, forecasting and understanding traffic patterns become the critical objectives for managing the network-designed Quality of Service (QoS) benchmark. For this purpose, cellular network planners often use different methodologies for predicting data traffic. However, traditional traffic forecasting approaches are erroneous. As well as most of the time, traditional traffic forecasts are high-level or a generously large regional cluster level. Also, eNodeB-level utilization with concerning traffic forecasting is not readily available. As a result, user experience degradation or unnecessary network expansion is triggered based on the traditional method. This research deals with extensive 6.2 million real network time series Long-Term Evolution (LTE) data traffic and other associate parameters, including eNodeB-wise Physical Resource Block (PRB) utilization, which focuses on building a traffic forecasting model with the help of multivariate feature inputs and deep learning algorithms. A stateof-the-art Deep Learning algorithm-based fusion model is proposed. The combination of different deep learning algorithms, namely Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU), enables traffic forecasting at a granular eNodeB-level and also provides eNodeB-wise forecasted PRB utilization. In this research R 2 score value for the proposed fusion model is 0.8034, which outperforms traditional models. Apart from the PRB utilization, QoS threshold was devised as 70% from a real network experience to trigger soft parameter tuning decisions. Based on the forecasted PRB utilization, this research proposed a unique algorithm that estimates eNodeB-level soft capacity parameter optimization for a short-term step-up solution or long-term network expansion to ensure a guaranteed QoS benchmark.

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Section: System Modelmentioning

confidence: 99%

Deep Learning Based Fusion Model for Multivariate LTE Traffic Forecasting and Optimized Radio Parameter Estimation

et al. 2023

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“…The rapid development of artificial intelligence (AI) technologies offers novel methods and tools to improve the performance of time series prediction in sensor networks [ 2 ]. Several studies [ 3 , 4 , 5 ] have modeled it as a general time series prediction problem and focused on improving prediction performance. The variability of time series data is driven by various factors, including seasonal variations, long-term trends, periodic fluctuations, and random events.…”

Section: Introductionmentioning

confidence: 99%

BiLSTM-MLAM: A Multi-Scale Time Series Prediction Model for Sensor Data Based on Bi-LSTM and Local Attention Mechanisms

Fan,

Tang,

Guo

et al. 2024

Sensors

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This paper introduces BiLSTM-MLAM, a novel multi-scale time series prediction model. Initially, the approach utilizes bidirectional long short-term memory to capture information from both forward and backward directions in time series data. Subsequently, a multi-scale patch segmentation module generates various long sequences composed of equal-length segments, enabling the model to capture data patterns across multiple time scales by adjusting segment lengths. Finally, the local attention mechanism enhances feature extraction by accurately identifying and weighting important time segments, thereby strengthening the model’s understanding of the local features of the time series, followed by feature fusion. The model demonstrates outstanding performance in time series prediction tasks by effectively capturing sequence information across various time scales. Experimental validation illustrates the superior performance of BiLSTM-MLAM compared to six baseline methods across multiple datasets. When predicting the remaining life of aircraft engines, BiLSTM-MLAM outperforms the best baseline model by 6.66% in RMSE and 11.50% in MAE. In the LTE dataset, it achieves RMSE improvements of 12.77% and MAE enhancements of 3.06%, while in the load dataset, it demonstrates RMSE enhancements of 17.96% and MAE improvements of 30.39%. Additionally, ablation experiments confirm the positive impact of each module on prediction accuracy. Through segment length parameter tuning experiments, combining different segment lengths has resulted in lower prediction errors, affirming the effectiveness of the multi-scale fusion strategy in enhancing prediction accuracy by integrating information from multiple time scales.

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“…In 2019, the average per capita volume of traffic usage reached 7.27GB per month [1] and it is expected to be 200 to 1,000 times in the following years [2]. Therefore, mobile operators face challenges in maintaining Quality of Service (QoS) by optimizing their network design planning as efficiently and quickly as possible [3][4][5][6][7][8]. Forecasting future traffic volume is crucial for successfully planning, managing, and developing network systems [9,10].…”

Section: Introductionmentioning

confidence: 99%

Forecasting LTE Network Traffic Using Hot-Winter’s Multiplicative Seasonal Method and Rolling Forecasting for Telecommunication Company Investment Optimization in Indonesia

2023

IJIES

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The rapid growth of mobile internet usage, particularly in LTE networks, poses challenges for mobile operators in maintaining quality of service (QoS) and optimizing network design planning. Accurate traffic volume forecasting is crucial for network planning and effective resource allocation. This study aims to propose an enhancement to the accuracy of the holt's winter multiplicative seasonal (HWMS) method by applying the rolling forecast technique to achieve better forecasting results. Using a public dataset of 56 cells, the evaluation based on mean absolute percentage error (MAPE) reveals the following sequence of prediction errors: HWMS & rolling forecast (20.47%), HWMS only (30.06%), FbProphet (30.45%), and auto regressive integrated moving average (ARIMA) (31.52%). The cells are categorized as "Good" (59%), "Reasonable" (39%), and "Poor" (2%). The results indicate that the proposed method achieves a significantly higher percentage of cells in the "Good" category, with a 45% difference compared to HWMS without rolling forecast, which only achieved 14%. Moreover, the proposed method outperforms ARIMA by 50% and FbProphet by 37% in the same category. Furthermore, when applied to real data from a telecommunications company in Indonesia, the proposed method identified 11 cells that require solutions out of a total of 100 cells. In comparison, the ARIMA method identified 3 cells, FbProphet identified 12 cells, and HWMS without Rolling Forecast identified 9 cells. Thus, the company can provide solutions for the identified 11 cells without the need for excessive investment while still maintaining revenue potential.

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Cellular Traffic Prediction Based on an Intelligent Model

Cited by 16 publications

References 47 publications

Deep Learning Based Fusion Model for Multivariate LTE Traffic Forecasting and Optimized Radio Parameter Estimation

Deep Learning Based Fusion Model for Multivariate LTE Traffic Forecasting and Optimized Radio Parameter Estimation

BiLSTM-MLAM: A Multi-Scale Time Series Prediction Model for Sensor Data Based on Bi-LSTM and Local Attention Mechanisms

Forecasting LTE Network Traffic Using Hot-Winter’s Multiplicative Seasonal Method and Rolling Forecasting for Telecommunication Company Investment Optimization in Indonesia

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