Enhancing Network Throughput via the Equal Interval Frame Aggregation Scheme for IEEE 802.11ax WLANs

ZHU Yihua; XU Mengying

doi:10.23919/cje.2022.00.282

Volume 32 Issue 4

Jul. 2023

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Article Navigation > Chinese Journal of Electronics > 2023 > 32(4): 747-759

ZHU Yihua and XU Mengying, “Enhancing Network Throughput via the Equal Interval Frame Aggregation Scheme for IEEE 802.11ax WLANs,” Chinese Journal of Electronics, vol. 32, no. 4, pp. 747-759, 2023, doi: 10.23919/cje.2022.00.282

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ZHU Yihua and XU Mengying, “Enhancing Network Throughput via the Equal Interval Frame Aggregation Scheme for IEEE 802.11ax WLANs,” Chinese Journal of Electronics, vol. 32, no. 4, pp. 747-759, 2023, doi: 10.23919/cje.2022.00.282

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Enhancing Network Throughput via the Equal Interval Frame Aggregation Scheme for IEEE 802.11ax WLANs

doi: 10.23919/cje.2022.00.282

1.
School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

Funds: This work was supported by the National Natural Science Foundation of China (61772470, 61432015) and the National Key R&D Program of China (2019YFD0901605)

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Author Bio:
Yihua ZHU received the B.S. degree in mathematics from Zhejiang Normal University, Zhejiang, China, in July 1982; the M.S. degree in operation research and cybernetics from Shanghai University, Shanghai, China, in April 1993; and the Ph.D. degree in computer science and technology from Zhejiang University, Zhejiang, China, in March 2003. Dr. Zhu is a Professor at Zhejiang University of Technology, Hangzhou, Zhejiang, China. He is a Member of China Computer Federation Technical Committee on Internet of Things (IoT). His current research interests include IoT, WLANs, WSNs, RFID systems. He has served as Technical Program Committee Members or Co-chairs in the international conferences IEEE ICC, WCNC, GlobeCom, DCOSS, etc. (Email: yhzhu@zjut.edu.cn)

Mengying XU received the B.S. degree from Zhejiang A & F University, Zhejiang, China, in 2020. She is currently a candidate student for the M.S. degree in Zhejiang University of Technology. Her current research interests include channel access in the IEEE 802.11ax WLAN
Received Date: 2022-08-21
Accepted Date: 2023-01-06

Available Online: 2023-01-14

Publish Date: 2023-07-05

Abstract

Abstract

Frame aggregation is fully supported in the newly published IEEE 802.11ax standard to improve throughput. With frame aggregation, a mobile station combines multiple subframes into an aggregate MAC service data unit (A-MSDU) or an aggregate MAC protocol data unit (A-MPDU) for transmission. It is challenging for a mobile station in 802.11ax WLANs to set an appropriate number of subframes being included in an A-MSDU or A-MPDU. This problem is solved in this paper by the proposed equal interval frame aggregation (EIFA) scheme which lets a mobile station aggregate at most k subframes at a fixed time period of T. A novel Markov model is developed for deriving the probability of number of subframes in the data buffer at the mobile station, resulting in the throughput and packet delay in the EIFA. Moreover, the optimization problem of maximizing the throughput with the constraint on delay is formulated, and its solution leads to the optimal pair of parameters k and T for improving throughput in the EIFA scheme. Simulation results show the EIFA has a higher throughput than the ones in which the mobile station chooses the minimum, the maximum, or a random number of subframes.
- IEEE 802.11ax standard,
- Frame aggregation,
- WLAN,
- Throughput,
- Markov model,
- MAC

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References(43)

References

[1]	IEEE Std 802.11-1997:1997, 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, doi: 10.1109/IEEESTD.1997.85951.
[2]	IEEE Std 802.15.4-2006:2006, IEEE Standard for Information Technology-Local and Metropolitan Area Networks-Specific Requirements-Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low Rate Wireless Personal Area Networks (WPANs), doi: 10.1109/IEEESTD.2006.232110.
[3]	S. Tozlu, M. Senel, W. Mao, et al., “Wi-Fi enabled sensors for internet of things: A practical approach,” IEEE Communications Magazine, vol.50, no.6, pp.134–143, 2012. doi: 10.1109/MCOM.2012.6211498
[4]	Q. Q. Yang, G. Y. Zhou, W. H. Qin, et al., “Air-kare: A Wi-Fi based, multi-sensor, real-time indoor air quality monitor,” in Proceedings of 2015 IEEE International Wireless Symposium (IWS 2015), Shenzhen, China, pp.1–4, 2015.
[5]	R. R. Rajanna, S. Natarajan, and P. R. Vittal, “An IoT Wi-Fi connected sensor for real time heart rate variability monitoring,” in Proceedings of the 3rd International Conference on Circuits, Control, Communication and Computing (I4C), Bangalore, India, pp.1–4, 2018.
[6]	M. Sivachitra, J. J. D. Kumar, R. Gokul, et al., “Wi-Fi controlled multi-sensor robotic car,” in Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India, pp.1541–1546, 2022.
[7]	IEEE Std 802.11ax-2021:2021, 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 1: Enhancements for high-efficiency WLAN, doi: 10.1109/IEEESTD.2021.9442429.
[8]	D. J. Deng, Y. P. Lin, X. Yang, et al., “IEEE 802.11ax: Highly efficient WLANs for intelligent information infrastructure,” IEEE Communications Magazine, vol.55, no.12, pp.52–59, 2017. doi: 10.1109/MCOM.2017.1700285
[9]	IEEE, IEEE Std 802.11n^TM 2009:2009, 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 5: Enhancements for Higher Throughput, New York, NY, USA: IEEE.
[10]	D. Kotagiri, K. Nihei, and T. S. Li, “Distributed convolutional deep reinforcement learning based OFDMA MAC for 802.11ax,” in Proceedings of ICC 2021-IEEE International Conference on Communications, Montreal, QC, Canada, pp.1–6, 2021.
[11]	D. H. Xie, J. W. Zhang, A. M. Tang, et al., “Multi-dimensional busy-tone arbitration for OFDMA random access in IEEE 802.11ax,” IEEE Transactions on Wireless Communications, vol.19, no.6, pp.4080–4094, 2020. doi: 10.1109/TWC.2020.2979852
[12]	Y. Zheng, J. B. Wang, Q. H. Chen, et al., “Retransmission number aware channel access scheme for IEEE 802.11ax based WLAN,” Chinese Journal of Electronics, vol.29, no.2, pp.351–360, 2020. doi: 10.1049/cje.2020.01.014
[13]	W. J. Lee, W. Shin, J. A. Ruiz-de-Azua, et al., “NOMA-based uplink OFDMA collision reduction in 802.11ax networks,” in Proceedings of 2021 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea, Republic of, pp.212–214, 2021.
[14]	L. Lanante, C. Ghosh, and S. Roy, “Hybrid OFDMA random access with resource unit sensing for next-gen 802.11ax WLANs,” IEEE Transactions on Mobile Computing, vol.20, no.12, pp.3338–3350, 2021. doi: 10.1109/TMC.2020.3000503
[15]	R. G. Cheng, C. M. Yang, B. S. Firmansyah, et al., “Uplink OFDMA-based random access mechanism with bursty arrivals for IEEE 802.11ax systems,” IEEE Networking Letters, vol.4, no.1, pp.34–38, 2022. doi: 10.1109/LNET.2021.3138277
[16]	W. R. Ghanem, V. Jamali, Y. Sun, et al., “Resource allocation for multi-user downlink MISO OFDMA-URLLC systems,” IEEE Transactions on Communications, vol.68, no.11, pp.7184–7200, 2020. doi: 10.1109/TCOMM.2020.3017757
[17]	F. Khoramnejad, M. Rasti, H. Pedram, et al., “Load management, power and admission control in downlink cellular OFDMA networks,” IEEE Transactions on Mobile Computing, vol.20, no.4, pp.1477–1493, 2021. doi: 10.1109/TMC.2019.2962126
[18]	M. J. Xiao, L. S. Huang, K. Xing, et al., “Opportunistic data aggregation in low-duty-cycle wireless sensor networks with unreliable links,” Chinese Journal of Electronics, vol.22, no.3, pp.599–603, 2013.
[19]	Y. X. Lin and V. W. S. Wong, “WSN01-1: Frame aggregation and optimal frame size adaptation for IEEE 802.11n WLANs,” in Proceedings of IEEE Globecom 2006, San Francisco, CA, USA, pp.1–6, 2006.
[20]	M. M. S. Kowsar and S. Biswas, “Performance improvement of IEEE 802.11n WLANs via frame aggregation in NS-3,” in Proceedings of 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox’s Bazar, Bangladesh, pp.1–6, 2017.
[21]	S. Abdallah and S. D. Blostein, “Joint rate adaptation, frame aggregation and MIMO mode selection for IEEE 802.11ac,” in Proceedings of 2016 IEEE Wireless Communications and Networking Conference, Doha, Qatar, pp.1–6, 2016.
[22]	H. Assasa, A. Loch, and J. Widmer, “Packet mass transit: Improving frame aggregation in 60 GHz networks,” in Proceedings of the IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), Coimbra, Portugal, pp.1–7, 2016.
[23]	Y. Nomura, K. Mori, and H. Kobayashi, “Efficient frame aggregation with frame size adaptation for next generation MU-MIMO WLANs,” in Proceedings of the 9th International Conference on Next Generation Mobile Applications, Services and Technologies, Cambridge, UK, pp.288–293, 2015.
[24]	E. Charfi, L. Chaari, and L. Kamoun, “Joint urgency delay scheduler and adaptive aggregation technique in IEEE 802.11n networks,” in Proceedings of the 5th International Conference on Communications and Networking (COMNET), Tunis, Tunisia, pp.1–6, 2015.
[25]	X. L. Zhou and A. Boukerche, “AFLAS: An adaptive frame length aggregation scheme for vehicular networks,” IEEE Transactions on Vehicular Technology, vol.66, no.1, pp.855–867, 2017. doi: 10.1109/TVT.2016.2533160
[26]	M. Yazid and A. Ksentini, “Modeling and performance analysis of the main MAC and PHY features of the 802.11ac standard: A-MPDU aggregation vs spatial multiplexing,” IEEE Transactions on Vehicular Technology, vol.67, no.11, pp.10243–10257, 2018. doi: 10.1109/TVT.2018.2870127
[27]	J. Saldana, J. Ruiz-Mas, and J. Almodóvar, “Frame aggregation in central controlled 802.11 WLANs: The latency versus throughput tradeoff,” IEEE Communications Letters, vol.21, no.11, pp.2500–2503, 2017. doi: 10.1109/LCOMM.2017.2741940
[28]	J. Saldana, O. Topal, J. Ruiz-Mas, et al., “Finding the sweet spot for frame aggregation in 802.11 WLANs,” IEEE Communications Letters, vol.25, no.4, pp.1368–1372, 2021. doi: 10.1109/LCOMM.2020.3044231
[29]	A. Abedi, T. Brecht, and O. Abari, “Demystifying frame aggregation in 802.11 networks: Understanding and approximating optimality,” Computer Communications, vol.180, pp.259–270, 2021. doi: 10.1016/j.comcom.2021.09.019
[30]	M. Morimoto, K. Sanada, H. Hatano, et al., “Throughput and delay analysis for full-duplex WLANs with frame aggregation under non-saturated condition,” in Proceedings of the 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC), Lisbon, Portugal, pp.1–5, 2019.
[31]	K. Liu and C. L. Li, “An efficient adaptive frame aggregation scheme in vehicular Ad Hoc networks,” in Proceedings of the 9th International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, China, pp.1–6, 2017.
[32]	S. Kim and J. H. Yun, “Efficient frame construction for multi-user transmission in IEEE 802.11 WLANs,” IEEE Transactions on Vehicular Technology, vol.68, no.6, pp.5859–5870, 2019. doi: 10.1109/TVT.2019.2907281
[33]	J. L. Liu, M. W. Yao, and Z. L. Qiu, “Enhanced two-level frame aggregation with optimized aggregation level for IEEE 802.11n WLANs,” IEEE Communications Letters, vol.19, no.12, pp.2254–2257, 2015. doi: 10.1109/LCOMM.2015.2495108
[34]	K. Suzuki and S. Yamazaki, “Throughput maximization based on optimized frame-aggregation levels for IEEE 802.11 WLANs,” IEEE Communications Letters, vol.25, no.5, pp.1725–1728, 2021. doi: 10.1109/LCOMM.2021.3054401
[35]	B. S. Kim, H. Y. Hwang, and D. K. Sung, “Effect of frame aggregation on the throughput performance of IEEE 802.11n,” in Proceedings of 2008 IEEE Wireless Communications and Networking Conference, Las Vegas, NV, USA, pp.1740–1744, 2008.
[36]	S. Seytnazarov, J. G. Choi, and Y. T. Kim, “Enhanced mathematical modeling of aggregation-enabled WLANs with compressed BlockACK,” IEEE Transactions on Mobile Computing, vol.18, no.6, pp.1260–1273, 2019. doi: 10.1109/TMC.2018.2857806
[37]	I. Jabri, K. Mansour, I. Al-Oqily, et al., “Enhanced characterization and modeling of A-MPDU aggregation for IEEE 802.11n WLANs,” Transactions on Emerging Telecommunications Technologies, vol.33, no.1, article no.e4384, 2022. doi: 10.1002/ETT.4384
[38]	W. W. Lu, S. L. Gong, and Y. H. Zhu, “Timely data delivery for energy-harvesting IoT devices,” Chinese Journal of Electronics, vol.31, no.2, pp.322–336, 2022. doi: 10.1049/cje.2021.00.005
[39]	D. Skordoulis, Q. Ni, H. H. Chen, et al., “IEEE 802.11n MAC frame aggregation mechanisms for next-generation high-throughput WLANs,” IEEE Wireless Communications, vol.15, no.1, pp.40–47, 2008. doi: 10.1109/MWC.2008.4454703
[40]	Y. H. Zhu and V. C. M. Leung, “Efficient power management for infrastructure IEEE 802.11 WLANs,” IEEE Transactions on Wireless Communications, vol.9, no.7, pp.2196–2205, 2010. doi: 10.1109/TWC.2010.07.081493
[41]	Q. H. Chen and Y. H. Zhu, “Scheduling channel access based on target wake time mechanism in 802.11ax WLANs,” IEEE Transactions on Wireless Communications, vol.20, no.3, pp.1529–1543, 2021. doi: 10.1109/TWC.2020.3034173
[42]	S. M. Ross, Introduction to Probability Models, 11th ed., Academic Press, New York, NY, USA, doi: 10.1016/C2012-0-03564-8, 2014.
[43]	J. H. Holland, Adaptation in Natural and Artificial Systems, The MIT Press, Cambridge, MA, USA, 1992.