Wireless Link Quality Estimation
Using LSTM ModelPS4-4

Yuki Kanto †, Kohei Watabe ††Graduate School of Engineering Nagaoka University of Technology Nagaoka, Niigata, Japan† s213121@stn.nagaokaut.ac.jp, †† k_watabe@vos.nagaokaut.ac.jp

Abstract
In this paper, we propose a novel Wireless Link Quality Estimation (WLQE) model leveraging Long Short-Term Memory (LSTM) to a highly accurate estimation communication quality inwireless networks. Our LSTM-based approach outperforms conventional methods by 19.3% in accuracy and 9.5% in macro-F1, offering enhanced stability for outdoor communication onmobile devices.

Keywords

III. Experiments

Link Quality Estimation, Deep Learning, LSTM, Wireless Network, Prediction, WirelessQuality of Service

u Datasetu Proposed model and conventional models trained on SRFG dataset..
u Dataset is split 7:2:1 for training, validation, and testing.
u Learning Methods for the Proposed Modelu The proposed model is first trained with LSTM, and the best model is prepared
before the loss of validation increases using Early Stopping.
u The output values estimated by the LSTM model are input to the binningcomponent and discretized.
u Finally, the trained LSTM model is used to evaluate the test dataset.

I. Introductionu Backgroundu In recent years, mobile communication devices face fluctuating communication
environments both indoors and outdoors and require stable and reliable network
connectivity.
u To address this, we aim to provide mobile devices with future communication
quality estimates, enabling them to buffer and switch to optimal links before
communication instability arises.
u Objectiveu Conventional methods for estimating communication quality does not utilize
time-series information, and it is performed in a static environment and cannot
flexibly respond to changes in the mobile environment.
u Our objective is to utilize sequential information to provide a highly accurate
estimate of future communication quality and a more adaptive model.

Fig. 2. Temporal variation in RSSI and SINR samples in the SRFG dataset.

Urban areas

IV. Results

Suburban areasu Table 2 shows the results of the evaluation of the proposed method against theconventional method ”Stacked AutoEncoder-based Link Quality Estimator (LQESAE)”[2] in terms of accuracy and macro-F1 metrics.

II. Proposed methodu LSTM-Based Wireless Link Quality Estimation (WLQE)
u In this study, we proposed the LSTM-based WLQE shown in Fig. 1, focusing ontime series data.
u Long Short-Term Memory (LSTM) is a method that can learn long-term
dependencies and is often used for time series data.
u By focusing on time-series data of communication metrics information (RSRP,SINR), future link quality (RSRP) is estimated.
u Data Processing and Model Creationu Based on Table 1, a classification model was created by labelling the continuousvalues of RSRP into link quality grades (LQ grades).
u Process communication metrics data into smoothed data and residual data anduse LSTM to learn based on these time-series information.
u We performed five preprocessing steps: missing value imputation, oversampling,residual separation, standardization, and binning.
Table 1. Mapping of RSRP values to link quality[1]
RSRP[dBm]−84 ≤ 𝑅𝑆𝑅𝑃
−84 < 𝑅𝑆𝑅𝑃 < −95−95 < 𝑅𝑆𝑅𝑃 < −105
−105 < 𝑅𝑆𝑅𝑃 < −115
𝑅𝑆𝑅𝑃 ≤ −115

Link Quality
Very GoodGoodIntermediate
Bad
Very Bad

LSTM component

LQ grades(class)
43210

LSTM block

LSTM block

LSTM block

FCFCFCFCFC

LSTM block

Predicted RSRP values

Binningcomponent

Dataset15minSequences( dim × window size)

Split by sliding window

Fig. 1. The architecture of LSTM-based WLQE model.

Predicted LQ gradeu The LSTM-based WLQE performs the best, with 19.3% higher accuracy and 9.5%
higher macro-F1 than LQE-SAE.

Fig.3 LQE-SAE

Fig.4 LSTM-based WLQE

Table 2. Model Performance
ModelAccuracy
LQE-SAE0.694
LSTM-based WLQE0.887macro-F1
0.527
0.622

V. Conclusion
u Model Performanceu In this study, we have developed an LSTM-based WLQE model that caneffectively use sequential information.
u The proposed method outperforms the conventional methods in terms of average
accuracy and macro-F1.
u The results of this study show that the proposed method, which utilizes
sequential information using LSTM, is effective in estimating link quality withhigh accuracy.
u Future Outlook
u In the future, we will search for optimal hyperparameters and propose newmethods (e.g., regression models) that utilize other features.
References[1] “MiFi Signal Strength Bar Meaning (RSRP,SNR,RSSI)”. https://insg.my.site.com/insgtechsupport/s/article/MiFi-Signal-Strength-Bars- vs-RSRP-etc[2] X. Luo, L. Liu, J. Shu, and M. Al-Kali, ”Link quality estimation method for wireless sensor
networks based on stacked autoencoder”, IEEE Access, vol. 7, 2019, pp. 21572–21583,
Acknowledgment• This work was partly supported by JSPS KAKENHI Grant Number JP23H03379.

2024 IEEE Network Operations and Management Symposium, Seoul, Korea, 6-10 May 2024