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Radio-resource Allocation and Beamforming Algorithms

SUMMARY

5G wireless communication systems will feature antenna arrays with narrow beam patterns at the base and user stations. Because the beams are narrow, they can be steered/beamformed along viable transmission paths between base and user stations, yielding directional communications. The number of paths will depend on the propagation environment or channel. For example, if there are many buildings or obstacles in the surroundings, there will be many paths due to the transmitted signal reflecting off obstacles into the receiver. To enable transmission, the beams must first be aligned with the paths (beamforming training). Then up-to-date information is required about the channel to react accordingly (beam tracking). This will introduce new challenges, especially in the case of mobility.

Another challenge is that many wireless protocols have been designed with omnidirectional communication in mind. Highly directional communication, on the other hand, will impose drastic changes on radio-resource allocation.

A realistic platform that accurately characterizes the propagation environment is vital to evaluate and develop the type of efficient beamforming and radio-resource allocation algorithms and models as well as antenna array systems that will address these challenges. Until now, research and development has been conducted separately using tools and platforms that are not integrated and generally not compatible. This presents a major impediment to the development and deployment of future generation millimeter wave systems. Our response is to develop a realistic platform that includes accurate characterization of the channel propagation environment.

 

APPROACH TO EVALUATING BEAMFORMING & RADIO RESOURCE ALLOCATION

Evaluating performance in complex environments is not always achievable using real-world experiments due to the significant amount of resources required. Our set of tools (currently under development) allows us to accurately evaluate system performance of complex environments utilizing simulations.

For this task, we use the output of the millimeter-wave channel measurement and modeling project and more particularly the Quasi-Deterministic (Q-D) channel model. The Quasi-Deterministic (QD) channel model is a mmWave-specific map-based model that was adopted by the IEEE 802.11ay task group.

We developed a Q-D channel realization software to provide the space, time, and phase characteristics of each channel path to the ns-3 system level simulator in order to accurately represent the mmWave communication channel.

An external tool, provided by IMDEA, is used to implement the Phased Antenna Array model, allowing the usage and evaluation of any kind of phased antenna arrays in ns-3.

Finally, new PER/SNR curves, specifically tailored for the mmWave environment, are generated using link-level simulations and populated in ns-3 [1].

This set of tools allows the precise study of mmWave beamform training and tracking as well as radio-resource allocation algorithms as they comprise accurate channel, error and phased antenna array models.  They make it possible to identify the factors that impact algorithm performance and how to modify those algorithms to improve their performance in the context of mmWave communications.

 

PUBLICATIONS

[1] A. Bodi, J. Zhang, J. Wang, and C. Gentile, " Physical-Layer Analysis of IEEE 802.11ay using a Channel Fading Model from Mobile Measurements", in IEEE  International Conference on Communications (ICC 2019), May 2019.

[2] H. Assasa, T. Ropitault, S. Lee and N. Golmie, " Enhancing the ns-3 IEEE 802.11ad Model Fidelity: Beam Codebooks, Multi-Antenna Beamforming Training, and Quasi-Deterministic mmWave Channel", in Workshop on ns-3 (WNS3 2019), June 2019.

[3] M. Kim, T. Ropitault, S. Lee and N. Golmie, "Efficient MU-MIMO Beamforming Protocol for IEEE 802.11ay WLANs," in IEEE Communications Letters, Vol. 22, No. 1, January 2019.

[4]  Y. Kim, S. Lee, and T. Ropitault, "Adaptive Scheduling for Asymmetric Beamforming Training in IEEE 802.11ay-based Environments", in Proceedings of IEEE Wireless Communications and Networking Conference (WCNC 2019), April 2019.

[5] C. Pielli, T. Ropitault, and M. Zorzi    "The Potential of mmWaves in Smart Industry: Manufacturing at 60 GHz", in International Conference on Ad-Hoc Networks and Wireless, AdHoc Now 2018, pp. 64-67.

[6] M. Kim, T. Ropitault, S. Lee and N. Golmie, "A Throughput Study for Channel Bonding in IEEE 802.11ac Networks," in IEEE Communications Letters, vol. 21, no. 12, pp. 2682-2685, Dec. 2017.

[7] T. Ropitault and N. Golmie, "ETP algorithm: Increasing spatial reuse in wireless LANs dense environment using ETX," 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[8] T. Ropitault, "Evaluation of RTOT algorithm: A first implementation of OBSS_PD-based SR method for IEEE 802.11ax," 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)

Created October 13, 2016, Updated April 12, 2019