Low Overhead Dynamic Channel Selection with Cluster-Based Spatial-Temporal Station Reporting in Wireless Networks

Authors: Zeyad Abdelmageid, Xianbin Wang

Abstract:

Choosing the operational channel for a WLAN access point (AP) in WLAN networks has been a static channel assignment process initiated by the user during the deployment process of the AP, which fails to cope with the dynamic conditions of the assigned channel at the station side afterwards. However, the dramatically growing number of Wi-Fi APs and stations operating in the unlicensed band has led to dynamic, distributed and often severe interference. This highlights the urgent need for the AP to dynamically select the best overall channel of operation for the basic service set (BSS) by considering the distributed and changing channel conditions at all stations. Consequently, dynamic channel selection algorithms which consider feedback from the station side have been developed. Despite the significant performance improvement, existing channel selection algorithms suffer from very high feedback overhead. Feedback latency from the STAs, due the high overhead, can cause the eventually selected channel to no longer be optimal for operation due to the dynamic sharing nature of the unlicensed band. This has inspired us to develop our own dynamic channel selection algorithm with reduced overhead through the proposed low-overhead, cluster-based station reporting mechanism. The main idea behind the cluster-based station reporting is the observation that STAs which are very close to each other tend to have very similar channel conditions. Instead of requesting each STA to report on every candidate channel while causing high overhead, the AP divides STAs into clusters then assigns each STA in each cluster one channel to report feedback on. With proper design of the cluster based reporting, the AP does not lose any information about the channel conditions at the station side while reducing feedback overhead. The simulation results show equal performance and at times better performance with a fraction of the overhead. We believe that this algorithm has great potential in designing future dynamic channel selection algorithms with low overhead.

Keywords: Channel assignment, Wi-Fi networks, clustering, DBSCAN, overhead.

References:

[1] E.Khorov, I. Levitsky and I. F. Akyildiz, ”Current Status and Directions of IEEE 802.11be, the Future Wi-Fi 7,” in IEEE Access, vol. 8, pp. 88664-88688, 2020, doi: 10.1109/ACCESS.2020.2993448.
[2] A. Akella, G. Judd, S. Seshan, and P. Steenkiste, “Self-Management in Chaotic Wireless Deployments,” Proc. ACM MobiCom, 2005.
[3] Gimenez-Guzman, Jose Crespo-Sen, David Mars´a-Maestre, Ivan. (2020). A Cluster-Based Channel Assignment Technique in IEEE 802.11 Networks. Telecom. 1. 228-241. 10.3390/telecom1030016.
[4] Mahonen, P.; Riihijarvi, J.; Petrova, M. Automatic channel allocation for small wireless local area networks using graph colouring algorithm approach. In Proceedings of the 2004 IEEE 15th International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE Cat. No. 04TH8754), Barcelona, Spain,5–8 September 2004; Volume 1, pp. 536–539.17]
[5] Cui, Y.; Li, W.; Cheng, X. Partially overlapping channel assignment based on “node orthogonality” for 802.11 wireless networks. In Proceedings of the 2011 Proceedings IEEE INFOCOM, Shanghai, China, 10–15 April 2011; pp. 361–365.
[18]
[6] Abeysekera, B.H.S.; Ishihara, K.; Inoue, Y.; Mizoguchi, M. Network-controlled channel allocation scheme for IEEE 802.11 wireless LANs: Experimental and simulation study. In Proceedings of the 2014 IEEE 79th Vehicular Technology Conference (VTC Spring), Seoul, Korea, 18–21 May 2014; pp. 1–5.
[7] Yue, X.; Wong, C.F.; Chan, S.H.G. CACAO: Distributed client-assisted channel assignment optimization for
[8] Kwon, Y.M.; Choi, K.; Kim, M.; Chung, M.Y. Distributed channel selection scheme based on the number of interfering stations in WLAN. Ad Hoc Netw. 2016, 39, 45–55.
[9] Handrizal, M.Z.; Noraziah, A.; Abdalla, A. An improved of channel allocation for wlan using vertex merge algorithm. In Proceedings of the International Conference on Computational Science and Information Management (ICoCSIM), Toba Lake, Indonesia, 3–5 December 2012; Volume 1, pp. 205–213.
[10] Green, D.B.; Obaidat, A. An accurate line of sight propagation performance model for ad-hoc 802.11 wireless LAN (WLAN) devices. In Proceedings of the 2002 IEEE International Conference on Communications, Conference Proceedings (ICC 2002 Cat. No. 02CH37333), New York, NY, USA, 28 April–2 May 2002; Volume 5, pp. 3424–3428.
[11] sWeb.Stanford.Edu, 2021, https://web.stanford.edu/class/cs345a/slides/12- clustering.pdf.
[12] Ji-Xian Zhang, Qiu-Hai Zhong, Ya-Ping Dai and Zheng Liu, ”A new de-noising method based on wavelet transform and transforming Hampel filter,” SICE 2003 Annual Conference (IEEE Cat. No.03TH8734), 2003, pp. 2147-2151 Vol.2.
[13] R. K. Pearson, Y. Neuvo, J. Astola and M. Gabbouj, ”The class of generalized hampel filters,” 2015 23rd European Signal Processing Conference (EUSIPCO), 2015, pp. 2501-2505, doi: 10.1109/EUSIPCO.2015.7362835.