Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover

Abstract

The future high-speed cellular networks require efficient and high-speed handover mechanisms. However, the traditional cellular handovers are based upon measurements of target cell radio strength which requires frequent measurement gaps. During these measurement windows, data transmission ceases each time, while target bearings are measured causing serious performance degradation. Therefore, prediction-based handover techniques are preferred in order to eliminate frequent measurement windows. Thus, this work proposes an ultrafast and efficient XGBoost-based predictive handover technique for next generation mobile communications. The ML algorithm in general prefers 70–30% of training and test data, respectively. However, always obtaining 70% of training samples in mobile communications is challenging because the channel remains correlated within coherence time only. Therefore, collecting training samples beyond coherence time limits performance and adds delay; thus, the proposed work trains the model within coherence time where this fixed data split of 70–30% makes the model exceed coherence time. Despite the fact that the proposed model gets starved of required training samples, still there is no loss in predication accuracy. The test results show a maximum delay improvement of up to 596 ms with enhanced performance efficiency of 68.70% depending upon the scenario. The proposed model reduces delay and improves efficiency by having an appropriate training period; thus, the intelligent technique activates faster with improved accuracy and eliminates delay in the algorithm to predict mmWaves’ signal strength in contrast to actually measuring them.

Classification of normal and abnormal heart by classifying PCG signal using MFCC coefficients and CGP-ANN classifier

“…The human auditory system functions in such a way that its perception frequency (Mel frequency) differs from actual sound frequencies. The following formula shows the relationship between the Mel scale and frequencies [13][14][15][16][17]. From PCG signal (which is sound signal) the features are extracted through MFCC by applying the above steps mentioned in block diagram, then stored in excel sheet and is labeled (i.e.…”

Resource optimization-based network selection model for heterogeneous wireless networks

“…However, the model is designed considering homogeneous network and still lack of how to successfully employ ML methodologies into heterogeneous cellular network [18], [17]. According to Majid et al [19], emphasized XGBoost based handover execution prediction model for reducing frequent measurement updated and enhance overall performance for next generation network. However, are not efficient when dataset exhibit imbalanced behavior.…”

“…The modified resource allocation scheme handover probability 𝑄 𝑖1 and 𝑄 𝑖2 of 𝒰 1 and 𝒰 2 , respectively is computed using (19) to (21).…”

Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks

“…Traditional cell-to-cell handovers rely on measurements of target cell radio strength, which necessitates frequent measurement gaps. Therefore, a handover solution based on machine learning was proposed in [27][28][29]; also, prediction-based handover strategies are recommended to reduce the number of measurement windows as in [30], an ultrafast and effective XGBoostbased predictive handover technique for next-generation mobile communications was presented. Another fascinating study about conditional handover was proposed in [31].…”