Abstract
In millimeter-wave (mmWave) networks, where faster signal attenuation is compensated by the use of highly directional antennas, the effects of high mobility may seriously harm the link quality and, hence, the overall system performance. In this paper, we study the channel access in unlicensed mmWave networks with mobile clients, with particular emphasis on initial beamforming training and beam refinement protocol as per IEEE 802.11ad/ay standard. We explicitly model beamforming procedures and corresponding overhead for directional mmWave antennas and provide a method for maximizing the average data rate over the variable length of the 802.11ad/ay beacon interval in different mobility scenarios. We illustrate the impact of the client speed and mobility patterns by examples of three variations of the discrete random walk mobility model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
QUALCOMM has already announced new 802.11ay chipsets, QCA6431 and QCA6421, intended for mobile use; however, the corresponding hardware specifications have not been published as of July 2019.
- 2.
Here, we focus only on the scheduled operation, although DTI may as well contain contention-based access periods (CBAPs).
- 3.
In fact, one of the clients may succeed. We leave the evaluation of initial random access collisions out of the scope of this paper. The detailed description of the protocol may be found in [17].
- 4.
In reality, the antenna pattern is not omnidirectional. Moreover, devices may use several directional antennas, and all of them participate in sector sweep. For the sake of clarity, we omit consideration of more complex procedures with multiple antennas but note that the respective modifications can be easily incorporated into our system.
References
Jones, N.: The top 10 wireless technologies and trends that will drive innovation (2019)
Ghasempour, Y., da Silva, C.R., Cordeiro, C., Knightly, E.W.: IEEE 802.11ay: next-generation 60 GHz communication for 100 Gb/s Wi-Fi. IEEE Commun. Mag. 55(12), 186–192 (2017)
IEEE 802.11 Working group, IEEE 802.11 TGay use cases (2015)
Cordeiro, C., Akhmetov, D., Park, M.: IEEE 802.11ad: Introduction and performance evaluation of the first multi-Gbps Wi-Fi technology. In: Proceedings of the 2010 ACM International Workshop on mmWave Communications: From Circuits to Networks, pp. 3–8. ACM (2010)
Kim, J., Molisch, A.F.: Enabling Gigabit services for IEEE 802.11ad-capable high-speed train networks. In: 2013 IEEE Radio and Wireless Symposium, pp. 145–147. IEEE (2013)
Verma, L., Fakharzadeh, M., Choi, S.: Wi-Fi on steroids: 802.11ac and 802.11ad. IEEE Wirel. Commun. 20(6), 30–35 (2013)
Nitsche, T., Cordeiro, C., Flores, A.B., Knightly, E.W., Perahia, E., Widmer, J.: IEEE 802.11ad: directional 60 GHz communication for multi-Gigabit-per-second Wi-Fi. IEEE Commun. Mag. 52(12), 132–141 (2014)
Zeman, K., et al.: Emerging 5G applications over mmWave: Hands-on assessment of WiGig radios. In: 2017 40th International Conference on Telecommunications and Signal Processing (TSP), pp. 86–90. IEEE (2017)
da Silva, C.R., Kosloff, J., Chen, C., Lomayev, A., Cordeiro, C.: Beamforming training for IEEE 802.11ay millimeter wave systems. In: 2018 Information Theory and Applications Workshop (ITA), pp. 1–9. IEEE (2018)
da Silva, C.R., Lomayev, A., Chen, C., Cordeiro, C.: Analysis and simulation of the IEEE 802.11ay single-carrier PHY. In: 2018 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2018)
Va, V., Shimizu, T., Bansal, G., Heath, R.W.: Online learning for position-aided millimeter wave beam training. IEEE Access 7, 30507–30526 (2019)
Kumari, P., Eltayeb, M.E., Heath, R.W.: Sparsity-aware adaptive beamforming design for IEEE 802.11ad-based joint communication-radar. In: 2018 IEEE Radar Conference (RadarConf18), pp. 0923–0928. IEEE (2018)
Va, V., Choi, J., Shimizu, T., Bansal, G., Heath, R.W.: Inverse multipath fingerprinting for millimeter wave V2I beam alignment. IEEE Trans. Veh. Technol. 67(5), 4042–4058 (2018)
Samuylov, A. et al.: Characterizing spatial correlation of blockage statistics in urban mmWave systems. In: 2016 IEEE Globecom Workshops (GC Wkshps), pp. 1–7. IEEE (2016)
Gapeyenko, M., et al.: On the temporal effects of mobile blockers in urban millimeter-wave cellular scenarios. IEEE Trans. Veh. Technol. 66(11), 10124–10138 (2017)
Chukhno, O., Chukhno, N., Galinina, O., Gaidamaka, Y., Andreev, S., Samouylov, K.: Analyzing effects of directional deafness on mmWave channel access in unlicensed bands. In: accepted to IEEE GLOBECOM 2019 (2019)
IEEE 802.11 Working group, Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications. Amendment 3: enhancements for very high throughput in the 60 GHz band (2012)
Galinina, O., Pyattaev, A., Johnsson, K., Turlikov, A., Andreev, S., Koucheryavy, Y.: Assessing system-level energy efficiency of mmwave-based wearable networks. IEEE J. Sel. Areas Commun. 34(4), 923–937 (2016)
Acknowledgment
The publication has been prepared with the support of the “RUDN University Program 5-100” (recipients Nadezhda Chukhno, Olga Chukhno, Konstantin Samouylov). The reported study was funded by RFBR, project numbers 17-07-00845 and 18-07-00576 (recipients Yuliya Gaidamaka, Sergey Shorgin). This work has been developed within the framework of the COST Action CA15104, Inclusive Radio Communication Networks for 5G and beyond (IRACON).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Chukhno, N., Chukhno, O., Shorgin, S., Samouylov, K., Galinina, O., Gaidamaka, Y. (2019). Maximizing Achievable Data Rate in Unlicensed mmWave Networks with Mobile Clients. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2019 2019. Lecture Notes in Computer Science(), vol 11660. Springer, Cham. https://doi.org/10.1007/978-3-030-30859-9_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-30859-9_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30858-2
Online ISBN: 978-3-030-30859-9
eBook Packages: Computer ScienceComputer Science (R0)