Map-Driven mmWave Link Quality Prediction With Spatial-Temporal Mobility Awareness
Authors: 
Zhizhen Li, Mingzhe Chen, Gaolei Li, Xi Lin, Yuchen LiuAuthors Info & Claims
Pages 12161 - 12175
Abstract
The susceptibility of millimeter-wave (mmWave) links to blockages poses challenges for maintaining consistent high-rate performance. By predicting link quality in advance at specific locations or times of interest, proactive resource allocation techniques, such as link-quality-aware scheduling, can be employed to optimize the utilization of network resources. In this paper, we introduce a map-driven link quality prediction framework that divides the problem into long-term and short-term link quality predictions to cater to the needs of mobile computing. The first stage aims to predict a long-term radio map considering static network characteristics. We propose to separate LoS and NLoS scenarios, and build an analytical model and a regression-based approach to construct a complete link quality map in the spatial domain. Next, short-term link quality prediction is explored to anticipate future variations in link quality through a spatial-temporal attention-based prediction framework. The essence of this approach lies in capturing the spatial correlation and temporal dependency of mmWave wireless characteristics, followed by an attention mechanism to complement the dynamic link quality prediction task. On top of that, we also design a regional training mechanism with a weighted loss function to address the classical data imbalance problem of map-driven prediction. Extensive experimental and simulation results show that our integrated framework effectively captures comprehensive spatial-temporal knowledge and achieves significantly higher accuracy than other baseline prediction methods, making it a promising solution for a wide range proactive configuration tasks in mobile mmWave networks.
References
[1]
V. Velez, J. P. Pavia, N. Souto, P. Sebastião, and A. Correia, “Performance assessment of a RIS-empowered post-5G/6G network operating at the mmWave/THz bands,” IEEE Access, vol. 11, pp. 49625–49638, 2023.
[2]
W. Jiang and H. D. Schotten, “Initial beamforming for millimeter-wave and terahertz communications in 6G mobile systems,” in Proc. IEEE Wireless Commun. Netw. Conf., 2022, pp. 2613–2618.
[3]
M. Kandasamy, N. Yuvaraj, R. A. Raja, N. Kousik, M. R. Tf, and A. S. Kumar, “QoS design using mmWave backhaul solution for utilising underutilised 5G bandwidth in GHz transmission,” in Proc. 3rd Int. Conf. Artif. Intell. Smart Energy, 2023, pp. 1615–1620.
[4]
Y. Liu, Q. Hu, and D. M. Blough, “Joint link-level and network-level reconfiguration for mmWave backhaul survivability in urban environments,” in Proc. ACM Conf. Model. Anal. Simul. Wireless Mobile Syst., 2019, pp. 143–151.
[5]
Y. Jian, M. Agarwal, S. K. Venkateswaran, Y. Liu, D. M. Blough, and R. Sivakumar, “Wimove: Toward infrastructure mobility in mmWave WiFi,” in Proc. 18th ACM Symp. Mobility Manage. Wireless Access, 2020, pp. 11–20.
[6]
Y. Liu, S. K. Crisp, and D. M. Blough, “Performance study of statistical and deterministic channel models for mmWave Wi-Fi networks in NS-3,” in Proc. Workshop NS-3, 2021, pp. 33–40.
[7]
Y. Ghasempour, C. R. Da Silva, C. Cordeiro, and E. W. Knightly, “IEEE 802.11 AY: Next-generation 60 GHz communication for 100 Gb/s Wi-Fi,” IEEE Commun. Mag., vol. 55, no. 12, pp. 186–192, Dec. 2017.
[8]
T. Nitsche, C. Cordeiro, A. B. Flores, E. W. Knightly, E. Perahia, and J. C. Widmer, “IEEE 802.11 AD: Directional 60 GHz communication for multi-Gigabit-per-second Wi-Fi,” IEEE Commun. Mag., vol. 52, no. 12, pp. 132–141, Dec. 2014.
[9]
S. H. A. Shah and S. Rangan, “Multi-cell multi-beam prediction using auto-encoder LSTM for mmWave systems,” IEEE Trans. Wireless Commun., vol. 21, no. 12, pp. 10366–10380, Dec. 2022.
[10]
Y. Liu, Y. Jian, R. Sivakumar, and D. M. Blough, “Maximizing line-of-sight coverage for mmWave wireless LANs with multiple access points,” IEEE/ACM Trans. Netw., vol. 30, no. 2, pp. 698–716, Apr. 2022.
[11]
Y. Jian, M. Agarwal, Y. Liu, D. M. Blough, and R. Sivakumar, “Poster: Hawkeye-predictive positioning of a ceiling-mounted mobile ap in mmWave WLANs for maximizing line-of-sight,” in Proc. 25th Annu. Int. Conf. Mobile Comput. Netw., 2019, pp. 1–3.
[12]
Y. Liu and D. M. Blough, “Environment-aware link quality prediction for millimeter-wave wireless LANs,” in Proc. ACM Int. Symp. Mobility Manage. Wireless Access, 2022, pp. 1–10.
[13]
G. Charan et al., “Towards real-world 6G drone communication: Position and camera aided beam prediction,” in Proc. IEEE Glob. Commun. Conf., 2022, pp. 2951–2956.
[14]
S. Wu, M. Alrabeiah, A. Hredzak, C. Chakrabarti, and A. Alkhateeb, “Deep learning for moving blockage prediction using real mmWave measurements,” in Proc. IEEE Int. Conf. Commun., 2022, pp. 3753–3758.
[15]
G. Charan, M. Alrabeiah, and A. Alkhateeb, “Vision-aided dynamic blockage prediction for 6G wireless communication networks,” in Proc. IEEE Int. Conf. Commun. Workshops, 2021, pp. 1–6.
[16]
J. Joo, M. C. Park, D. S. Han, and V. Pejovic, “Deep learning-based channel prediction in realistic vehicular communications,” IEEE Access, vol. 7, pp. 27846–27858, 2019.
[17]
K. Ma, D. He, H. Sun, and Z. Wang, “Deep learning assisted mmWave beam prediction with prior low-frequency information,” in Proc. IEEE Int. Conf. Commun., 2021, pp. 1–6.
[18]
S. Zhang, A. Wijesinghe, and Z. Ding, “RME-GAN: A learning framework for radio map estimation based on conditional generative adversarial network,” IEEE Internet Things J., vol. 10, no. 20, pp. 18016–18027, Oct. 2023.
[19]
K. He, Y. Huang, X. Chen, Z. Zhou, and S. Yu, “Graph attention spatial-temporal network for deep learning based mobile traffic prediction,” in Proc. IEEE Glob. Commun. Conf., 2019, pp. 1–6.
[20]
D. Wang, Y. Yang, and S. Ning, “DeepSTCL: A deep spatio-temporal ConvLSTM for travel demand prediction,” in Proc. Int. Joint Conf. Neural Netw., 2018, pp. 1–8.
[21]
K. He, X. Chen, Q. Wu, S. Yu, and Z. Zhou, “Graph attention spatial-temporal network with collaborative global-local learning for citywide mobile traffic prediction,” IEEE Trans. Mobile Comput., vol. 21, no. 4, pp. 1244–1256, Apr. 2022.
[22]
Q. Wu, K. He, X. Chen, S. Yu, and J. Zhang, “Deep transfer learning across cities for mobile traffic prediction,” IEEE/ACM Trans. Netw., vol. 30, no. 3, pp. 1255–1267, Jun. 2022.
[23]
J. Palacios, P. Casari, H. Assasa, and J. Widmer, “LEAP: Location estimation and predictive handover with consumer-grade mmWave devices,” in Proc. IEEE Conf. Comput. Commun., 2019, pp. 2377–2385.
[24]
S. H. A. Shah, M. Sharma, and S. Rangan, “LSTM-based multi-link prediction for mmWave and sub-THz wireless systems,” in Proc. IEEE Int. Conf. Commun., 2020, pp. 1–6.
[25]
A. Al-Saman, M. Cheffena, O. Elijah, Y. A. Al-Gumaei, S. K. Abdul Rahim, and T. Al-Hadhrami, “Survey of millimeter-wave propagation measurements and models in indoor environments,” Electronics, vol. 10, no. 14, 2021, Art. no.
[26]
Y. Liu, “MmWave ray tracer for WLAN data generation,” 2021. [Online]. Available: https://github.com/yuchen-sh/mmWave_ray_tracer
[27]
J. Lu, D. Steinbach, P. Cabrol, P. Pietraski, and R. V. Pragada, “Propagation characterization of an office building in the 60 GHz band,” in Proc. IEEE 8th Eur. Conf. Antennas Propag., 2014, pp. 809–813.
[28]
K. Sato et al., “Measurements of reflection and transmission characteristics of interior structures of office building in the 60-GHz band,” IEEE Trans. Antennas Propag., vol. 45, no. 12, pp. 1783–1792, Dec. 1997.
[29]
M. K. Samimi and T. S. Rappaport, “Characterization of the 28 GHz millimeter-wave dense urban channel for future 5G mobile cellular,” NYU Wireless TR, vol. 1, pp. 1–322, 2014.
[30]
H. Assasa, J. Widmer, T. Ropitault, and N. Golmie, “Enhancing the NS-3 IEEE 802.11 ad model fidelity: Beam codebooks, multi-antenna beamforming training, and quasi-deterministic mmWave channel,” in Proc. Workshop ns-3, 2019, pp. 33–40.
[31]
3GPP TR 38.901 v14.2.0, “Study on channel model for frequencies from 0.5 to 100 GHz,” 2019.
[32]
D. Steinmetzer, D. Wegemer, M. Schulz, J. Widmer, and M. Hollick, “Compressive millimeter-wave sector selection in off-the-shelf IEEE 802.11 ad devices,” in Proc. 13th Int. Conf. Emerg. Netw. EXperiments Technol., 2017, pp. 414–425.
[33]
Z. Li, “Dataset for STAP framwork with environment dynamics,” 2023. [Online]. Available: https://github.com/lzzz1998/moving-obj-data
[34]
Remcom Inc., “Wireless InSite: 3D wireless prediction software,” 2024. [Online]. Available: https://www.remcom.com/wireless-insite-em-propagation-software
[35]
C. Zhang et al., “Problem of data imbalance in building energy load prediction: Concept, influence, and solution,” Appl. Energy, vol. 297, 2021, Art. no.
[36]
Z. Li et al., “A hybrid deep learning approach with GCN and LSTM for traffic flow prediction,” in Proc. IEEE Intell. Transp. Syst. Conf., 2019, pp. 1929–1933.
Index Terms
- Map-Driven mmWave Link Quality Prediction With Spatial-Temporal Mobility Awareness
Index terms have been assigned to the content through auto-classification.
Recommendations
Data-driven link quality prediction using link features
As an integral part of reliable communication in wireless networks, effective link estimation is essential for routing protocols. However, due to the dynamic nature of wireless channels, accurate link quality estimation remains a challenging task. In ...
Temporal Adaptive Link Quality Prediction with Online Learning
Link quality estimation is a fundamental component of the low-power wireless network protocols and is essential for routing protocols in Wireless Sensor Networks (WSNs). However, accurate link quality estimation remains a challenging task due to the ...
Wireless Link Quality Prediction Based on Temporal Convolutional Networks and Self-Attention Fusion
CNIOT '24: Proceedings of the 2024 5th International Conference on Computing, Networks and Internet of ThingsMost current deep learning-based link quality prediction methods rely on statistically derived link quality parameters over sampling periods, which makes short-term correlations in link quality difficult to capture, and the prediction task often requires ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
1536-1233 © 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
Publisher
IEEE Educational Activities Department
United States
Publication History
Published: 01 December 2024
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 02 Mar 2025