Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
Source abstract
Abstract
The proliferation of IoT using heterogeneous wireless technologies within the unlicensed spectrum has intensified cross-technology interference (CTI) in wireless local area networks (WLANs). As WLANs increasingly adopt time-triggered transmission methods to support real-time services, this interference affects throughput, packet loss, and latency. This paper presents a CTI-aware rate adaptation framework designed to mitigate interference in WLANs without direct coordination with heterogeneous wireless devices. The framework includes a CTI identification model and CTI-aware rate selection algorithms. Leveraging short-time Fourier transform, the identification model captures the time–frequency–power characteristics of CTI signals, enabling the estimation of the average power of various heterogeneous wireless technologies employed by interfering devices. The rate selection algorithms predict CTI occurrence times and adjust the transmission rate accordingly, enhancing the performance of existing explicit and implicit interference mitigation methods. Experimental results demonstrated that the lightweight CTI identification model accurately estimated the average power of each type with an error margin of ±1.414 dBm, achieving this in under 1 ms on the target hardware. Additionally, applying the proposed framework to explicit interference mitigation enhanced goodput by 20.67%, reduced packet error rate by 2.38%, and decreased the probability of packets exceeding 1 ms latency by 0.932% compared to conventional methods.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Wireless Networks and Protocols · Indoor and Outdoor Localization Technologies · Wireless Body Area Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Future Convergence Engineering Major, Department of Computer Science and Engineering, Korea University of Technology and Education, Cheonan-si 31253, Republic of Korea
Industrial AI Research Center, Chungbuk National University, Cheongju-si 28116, Republic of Korea
Funding
Ministry of Trade, Industry & Energy (MOTIE, Korea)
References (42)
Show source-provided references
1. D-SCAN: Toward collaborative multi-radio coexistence in mobile devices via deep learning10.1016/j.iot.2022.100646 ↗
2. Uncoordinated Spectrum Access for Massive Connectivity in IoT Applications10.1109/IOTM.001.2300125 ↗
3. Real-Time Investigation of Cross-Technology Interference in Heterogeneous IoT Networks10.1109/ACCESS.2023.3321221 ↗
4. Using ranging for collision-immune IEEE 802.11 rate selection with statistical learning10.1016/j.comcom.2024.07.001 ↗
5. Towards Programmable Backscatter Radio Design for Heterogeneous Wireless Networks10.1109/TNET.2024.3454095 ↗
6. Havinga, T., Jiao, X., Liu, W., and Moerman, I. (2024, January 20–23). Experimental Study Towards Efficient Interference Avoidance Using Wi-Fi 6 OFDMA on SDR. Proceedings of the IEEE INFOCOM 2024—IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Vancouver, BC, Canada.10.1109/INFOCOMWKSHPS61880.2024.10620761 ↗
7. Kim, H., Kim, Y.J., and Kim, W.T. (2024). Deep Reinforcement Learning-Based Adaptive Scheduling for Wireless Time-Sensitive Networking. Sensors, 24.10.3390/s24165281 ↗
8. Real-Time Performance of Industrial IoT Communication Technologies: A Review10.1109/JIOT.2023.3332507 ↗
9. Seijo, O., Perez–Ramirez, J., Val, I., Rastorfer, D., Akhmetov, D., Das, D., Sharma Banjade, V.R., and Cavalcanti, D. (2024, January 9–13). Experimental Hardware Implementation of Trigger Based Scheduled MAC for Next Generation Wi-Fi. Proceedings of the ICC 2024—IEEE International Conference on Communications, Denver, CO, USA.10.1109/ICC51166.2024.10622667 ↗
10. Yin, Z., Li, Z., Kim, S.M., and He, T. (2018, January 10–15). Explicit Channel Coordination via Cross-technology Communication. Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services, Munich, Germany. MobiSys ’18.10.1145/3210240.3210318 ↗
11. Enabling Cross-Technology Coexistence for ZigBee Devices Through Payload Encoding10.1109/TMC.2023.3345830 ↗
12. MAC-layer rate control for 802.11 networks: A survey10.1007/s11276-020-02295-2 ↗
13. WaveLAN®-II: A high-performance wireless LAN for the unlicensed band10.1002/bltj.2069 ↗
14. Onasis, V., Kosasih, A., Gu, Y., Hardjawana, W., Qu, X., Vucetic, B., and Chikkam, K. (2022, January 10–13). Improving The Minstrel Rate Adaptation Algorithm using Shallow Neural Networks in IEEE 802.11ah. Proceedings of the 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, TX, USA.10.1109/WCNC51071.2022.9771795 ↗
15. Survey of cross-technology communication for IoT heterogeneous devices10.1049/iet-com.2018.6069 ↗
16. Lin, C.L., Lin, K.C.J., Lee, C.C., and Tsao, Y. (2020, January 7–11). Cross-Technology Interference Mitigation Using Fully Convolutional Denoising Autoencoders. Proceedings of the GLOBECOM 2020—2020 IEEE Global Communications Conference, Taipei, Taiwan.10.1109/GLOBECOM42002.2020.9322340 ↗
17. Yu, W., Wang, L., Song, J., He, L., and Wang, Y. (2023). Environment-Aware Rate Adaptation Based on Occasional Request and Robust Adjustment in 802.11 Networks. Sensors, 23.10.3390/s23187889 ↗
18. S, S.C., Kuri, J., and Akhtar, N. (2022, January 4–8). Performance Analysis of Channel-Dependent Rate Adaptation for OFDMA transmission in IEEE 802.11ax WLANs. Proceedings of the 2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS), Bangalore, India.10.1109/COMSNETS53615.2022.9668513 ↗
19. Signal identification for adaptive spectrum hyperspace access in wireless communications systems10.1109/MCOM.2014.6917415 ↗
20. Kim, H., Kim, Y.J., and Kim, W.T. (2023). Multitask Learning-Based Deep Signal Identification for Advanced Spectrum Sensing. Sensors, 23.10.3390/s23249806 ↗
21. A Survey on Deep Learning Techniques in Wireless Signal Recognition10.1155/2019/5629572 ↗
22. Cyclostationarity-Based Robust Algorithms for QAM Signal Identification10.1109/LCOMM.2011.112311.112006 ↗
23. Technology recognition and traffic characterization for wireless technologies in ITS band
24. Zhang, W., Feng, M., Krunz, M., and Hossein Yazdani Abyaneh, A. (2021, January 10–13). Signal Detection and Classification in Shared Spectrum: A Deep Learning Approach. Proceedings of the IEEE INFOCOM 2021—IEEE Conference on Computer Communications, Vancouver, BC, Canada.10.1109/INFOCOM42981.2021.9488834 ↗
25. Empowering Heterogeneous Wireless Networks through Efficient Signal Identification10.1109/MWC.013.2200499 ↗
26. Multi-Task Based Deep Learning Approach for Open-Set Wireless Signal Identification in ISM Band10.1109/TCCN.2021.3118456 ↗
27. A Very Brief Introduction to Machine Learning with Applications to Communication Systems10.1109/TCCN.2018.2881442 ↗
28. Chen, R., and Gao, W. (May, January 29). Enabling Cross-Technology Coexistence for Extremely Weak Wireless Devices. Proceedings of the IEEE INFOCOM 2019—IEEE Conference on Computer Communications, Paris, France.10.1109/INFOCOM.2019.8737379 ↗
29. Autonomous interference mapping for industrial Internet of Things networks over unlicensed bands: Identifying cross-technology interference10.1109/MIE.2020.3007568 ↗
30. Distributed DDPG-Based Resource Allocation for Age of Information Minimization in Mobile Wireless-Powered Internet of Things10.1109/JIOT.2024.3406044 ↗
31. Deployment-Friendly Link Adaptation in Wireless Local-Area Network Based on On-Line Reinforcement Learning10.1109/LCOMM.2023.3327964 ↗
32. Sammour, I., and Chalhoub, G. (2020). Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks. Electronics, 9.10.3390/electronics9091436 ↗
33. Tao, Y., and Tan, W.L. (2024, January 21–24). A Reinforcement Learning Approach to Wi-Fi Rate Adaptation Using the REINFORCE Algorithm. Proceedings of the 2024 IEEE Wireless Communications and Networking Conference (WCNC), Dubai, United Arab Emirates.10.1109/WCNC57260.2024.10570861 ↗
34. (2013). IEEE Standard for Information Technology–Telecommunications and Information Exchange between Systems Local and Metropolitan Area Networks–Specific Requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications–Amendment 4: Enhancements for Very High Throughput for Operation in Bands below 6 GHz (Standard No. 802.11ac-2013).
35. SIG, B. (2024, December 30). Bluetooth Core Specification. Available online: https://www.bluetooth.com/specifications/specs/core-specification-5-3/.
36. IEEE Standard for Low-Rate Wireless Networks (2020). Revision of IEEE Std 802.15.4-2015 (Standard No. IEEE Std 802.15.4-2020).
37. Zhao, Y., Agee, B., and Reed, J. (2005, January 8–12). Simulation and measurement of microwave oven leakage for 802.11 WLAN interference management. Proceedings of the 2005 IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, Beijing, China.
38. Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., and Pang, R. (November, January 27). Searching for MobileNetV3. Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.10.1109/ICCV.2019.00140 ↗
39. Noh, Y., Lee, D., Moon, S., Cheong, M., and Yu, H. (2015, January 8–13). Link Adaptation for HE WLAN, IEEE 802.11-14/1329r2. Proceedings of the IEEE 802.11 TGax Meeting, Berlin, Germany.
40. Yuan, W., Linnartz, J.P.M.G., and Niemegeers, I.G.M.M. (2010, January 18–21). Adaptive CCA for IEEE 802.15.4 Wireless Sensor Networks to Mitigate Interference. Proceedings of the 2010 IEEE Wireless Communication and Networking Conference, Sydney, Australia.10.1109/WCNC.2010.5506124 ↗
41. Lin, M., Chen, Q., and Yan, S. (2013). Network In Network. arXiv.
42. Saeedan, F., Weber, N., Goesele, M., and Roth, S. (2018, January 18–23). Detail-Preserving Pooling in Deep Networks. Proceedings of the CVPR, Salt Lake City, UT, USA.10.1109/CVPR.2018.00949 ↗