Abstract
The uplink OFDMA−based random access (UORA) is a key mechanism in the 802.11ax protocol. The advantage of the UORA is that nodes can send their frames quickly without requiring resources from the AP; the disadvantage is that the UORA can be heavily affected by collisions, resulting in low channel efficiency. In this paper, we focus on constructing an analytical model in which the number of random−access RUs (RA−RUs) is taken as a random variable. We investigate how the key parameters, the number of RA−RUs and the allowed maximum transmission times affect the network performance. Based on this, we explore how these two parameters can be set in practice. The comparison results show that our scheme outperforms conventional 802.11ax and previous work in terms of delay and the packet delivery ratio (PDR).
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References
IEEE Standard for Information Technology. (2021).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 1: Enhancements for high-efficiency WLAN, IEEE standard 802.11ax-2021
Deng, D.-J., et al. (2017). IEEE 802.11ax: Highly efficient WLANs for intelligent information infrastructure. IEEE Communications Magazine, 55(12), 52–59.
Khorov, E., Kiryanov, A., Lyakhov, A., & Bianchi, G. (2019). A tutorial on IEEE 802.11ax high efficiency WLANs. IEEE Communications Surveys & Tutorials, 21(1), 197–216.
Xie, D., Zhang, J., Tang, A., & Wang, X. (2020). Multi-dimensional busy-tone arbitration for OFDMA random access in IEEE 802.11ax. IEEE Transactions on Wireless Communication, 19(6), 4080–4094.
Omar, H. A., Abboud, K., Cheng, N., Malekshan, K. R., Gamage, A. T., & Zhuang, W. (2016). A survey on high efficiency wireless local area networks: Next generation WiFi. IEEE Communications Surveys & Tutorials, 18(4), 2315–2344.
Daldoul, Y., Meddour, D.-E., & Ksentini, A. (2020). Performance evaluation of OFDMA and MU-MIMO in 802.11ax networks. Computer Networks, 182, 107477.
Lanante, L., Uwai, H. O. T., Nagao, Y., Kurosaki, M., & Ghosh, C. (2017). Performance analysis of the 802.11 ax UL OFDMA random access protocol in dense networks. In 2017 IEEE International Conference on Communications (ICC) (pp. 1-6). Paris, France, IEEE
Yang, H., Deng, D. J., & Chen, K. C. (2017). Performance analysis of IEEE 802.11 ax UL OFDMA-based random access mechanism. In GLOBECOM 2017-2017 IEEE Global Communications Conference, (pp. 1–6). Singapore, IEEE
Naik, G., Bhattarai, S., & Park, J. M. (2018). Performance analysis of uplink multi-user OFDMA in IEEE 802.11 ax. In 2018 IEEE International Conference on Communications (ICC) (pp. 1–6). Kansas City, MO, USA, IEEE.
Cheng, R.-G., Yang, C.-M., Firmansyah, B. S., & Harwahyu, R. (2022). Uplink OFDMA-based random access mechanism with bursty arrivals for IEEE 802.11ax systems. IEEE Networking Letters, 4(1), 34–38.
Bankov, D., Didenko, A., Khorov, E., & Lyakhov, A. (2018). OFDMA uplink scheduling in IEEE 802.11 ax networks. In 2018 IEEE International Conference on Communications (ICC), (pp. 1–6). Kansas City, MO, USA, IEEE
Lee, K.-h. (2019). Performance analysis of the IEEE 802.11ax MAC protocol for heterogeneous Wi-Fi networks in non-saturated conditions. Sensors, 19(7), 1540.
Avdotin, E., Bankov, D., Khorov, E., & Lyakhov, A. (2019). Enabling massive real-time applications in IEEE 802.11be networks. In 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), (pp. 1–6)
Bianchi, G. (2000). Performance analysis of the IEEE 802.11 distributed coordination function. IEEE Journal on Selected Areas Communications, 18(3), 535–547.
Qu, Q., Li, B., Yang, M., et al. (2019). Survey and performance evaluation of the upcoming next generation WLANs standard-IEEE 802.11 ax. Mobile Networks and Applications, 24(5), 1461–1474.
Wang, J., Wu, M., Chen, Q., Zheng, Y., & Zhu, Y. H. (2019). Probability complementary transmission scheme for uplink OFDMA-based random access in 802.11 ax WLAN. In 2019 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-7). IEEE
Kim, J., Lee, H., & Bahk, S. (2019). CRUI: Collision reduction and utilization improvement in OFDMA-based 802.11ax networks. In Proceedings IEEE GLOBECOM, (pp. 1–6)
Kosek-Szott, K., & Domino, K. (2022). An efficient backoff procedure for IEEE 802.11ax uplink OFDMA-based random access. IEEE Access, 10, 8855–8863.
Uwai, T., Miyamoto, T., Nagao, Y., Lanante, L., Kurosaki, M., & Ochi, H. (2016). Adaptive backoff mechanism for OFDMA random access with finite service period in IEEE802. 11ax. In 2016 IEEE (CSCN), (pp. 1–6). Berlin, Germany, IEEE
Yang, A., Li, B., Yang, M., et al. (2021). Utility optimization of grouping-based uplink OFDMA random access for the next generation WLANs. Wireless Networks, 27(1), 809–823.
Bai, J., Fang, H., Suh, J., Aboul-Magd, O., Au, E., & Wang, X. (2019). An adaptive grouping scheme in ultra-dense IEEE 802.11ax network using buffer state report based two-stage mechanism. China Communication, 16(9), 31–44.
Lyakhov, A., & Vishnevsky, V. (2003). Optional tools of the Wi-Fi protocol: Study in saturation. In Proceedings International “Workshop Distributed Computer and Communication Networks (Stochastic modelling and optimization)” (DCCN-2003), (pp. 28–44).
Avdotin, E., Bankov, D., Khorov, E., & Lyakhov. A. (2019). Enabling Massive Real-Time Applications in IEEE 802.11be networks. In 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), (pp. 1–6). IEEE
Abate, J., & Whitt, W. (1992). Numerical inversion of probability generating functions. Operations Research Letters, 12(4), 245–251.
Karagiannis, T., Molle, M., Faloutsos, M. & Broido, A. (2004). A nonstationary Poisson view of internet traffic. In Proceedings IEEE INFOCOM, (pp. 1558–1569). Hong Kong, China
Akbar, I. A., & Tranter, W. H. (2007). Dynamic spectrum allocation in cognitive radio using hidden Markov models: Poisson distributed case. In Proceedings 2007 IEEE SoutheastCon, (pp. 196-201). IEEE
Wang, B., Ji, Z., Liu, K. J. R., & Clancy, T. C. (2009). Primary-prioritized Markov approach for dynamic spectrum allocation. IEEE Transactions on Wireless Communication, 8(4), 1854–1865.
Praveen Kumar, D., Amgoth, T., & Rao Annavarapu, C. S. (2019). Machine learning algorithms for wireless sensor networks: A survey. Information Fusion, 49, 1–25.
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Zhu, D., Pan, G. Analytical model considering varying numbers of RA-RUs to determine how to allocate the RA-RUs in practice. Wireless Netw 30, 253–265 (2024). https://doi.org/10.1007/s11276-023-03474-7
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DOI: https://doi.org/10.1007/s11276-023-03474-7