Online grant prediction for mobile networks based on traffic perception

doi:10.3969/j.issn.1000-5641.2025.04.001

Abstract

Abstract:

In mobile networks, mobile devices need to continuously monitor the control channels to ensure that they receive all the grants for scheduling sent by the base station, which results in enormous energy waste when the base station does not send any grants. Predicting the pattern of grant sending can effectively reduce such waste. Existing approaches to grant prediction can achieve high accuracy in specific traffic scenarios, but it’s still difficult for them to cope with dynamically changing communication environments. To solve this problem, an online grant prediction method based on traffic scenario perception is proposed. Firstly, the method extracts features from historical grant information sequences and perform a clustering of these features to classify various traffic scenarios. Then, a single prediction model is designed and trained offline for each class of scenarios. During the online prediction stage, the system selects the most appropriate model for prediction based on the results of traffic scenario perception. Experimental results show that, the proposed method is able to reduce prediction error rate by up to 81.52% compared with the benchmarking methods and can achieve effective energy savings on both simulated and real traffic traces. In addition, the proposed approach to traffic scenario perception is able to significantly reduce the computational complexity compared with the benchmarking approach, which makes it more suitable for real-time traffic scenario perception.

Key words: machine learning, time series forecasting, mobile network, clustering

CLC Number:

TP393.0

Yufei FU, Zesong XU, Anhao FENG, Tongquan WEI. Online grant prediction for mobile networks based on traffic perception[J]. J* E* C* N* U* N* S*, 2025, 2025(4): 1-14.

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特征名称	描述
$f_{{\mathrm{mean}}} $	序列的均值
$f_{{\mathrm{secondDerivate}}} $	序列的二阶差分的均值
$f_{{\mathrm{firstBurst}}} $	第一次出现授权信息的相对位置
$f_{{\mathrm{lastBurst}}} $	最后一次出现授权信息的相对位置
$f_{{\mathrm{firstIdle}}} $	第一次出现空闲的相对位置
$f_{{\mathrm{lastIdle}}} $	最后一次出现空闲的相对位置

$ l_{{\mathrm{pred}}} $	终端策略	RF_RX	RF_CTRL	PHY_RX	PHY_CTRL
1	监听	$ \left\langle{\left(1,\mathrm{O}\mathrm{N}\right)}\right\rangle $	$ \left\langle{\left(1,\mathrm{O}\mathrm{N}\right)}\right\rangle $	$ \left\langle{\left(\dfrac{7}{14},\mathrm{C}\mathrm{T}\mathrm{R}\mathrm{L}\right),\left(\dfrac{7}{14},\mathrm{D}\mathrm{A}\mathrm{T}\mathrm{A}\right)}\right\rangle $	$ \left\langle{\left(1,\mathrm{O}\mathrm{N}\right)}\right\rangle $
0	休眠	$ \left\langle{\left(1,\mathrm{O}\mathrm{F}\mathrm{F}\right)}\right\rangle $	$ \left\langle{\left(1,\mathrm{O}\mathrm{N}\right)}\right\rangle $	$ \left\langle{\left(1,\mathrm{O}\mathrm{F}\mathrm{F}\right)}\right\rangle $	$ \left\langle{\left(1,\mathrm{O}\mathrm{N}\right)}\right\rangle $

操作	加法	减法	乘法	除法	比较	平方根	指数运算
所消耗的FLOP数量/个	1	1	1	4	1	4	8

方法	平均FNR/%
方法	真实轨迹	Syn A	Syn B
本文方案	4.78	6.91	23.63
文献[21]	25.86	7.33	25.81
单预测模型^[20]	16.79	7.47	28.84

方法	平均能耗节省/%
方法	真实轨迹	Syn A	Syn B
本文提出的方案	68.47	45.18	21.65
文献[21]	53.31	44.87	3.31
单预测模型^[20]	56.45	47.88	16.84