Overview
Random Unitary Beamforming (RUB) is a beamforming technique that was originally developed for Multiple-input multiple output (MIMO) wireless systems. In Free-space optical (FSO) communication, it’s often unknown which beam shape will best travel to the end receiver at that particular moment. RUB takes a probabilistic approach. Rather than calculating the best beam, the transmitter tries a few different orthogonal beam patterns and see which works best. At a given moment, the interactions of various factors such as turbulence, scintillation and alignment errors might make any beam shape ideal at that moment.
This is a lower complexity method to manage beam shape selection versus full channel state information at the transmitter (CSIT).
Process
The transmitter has multiple spatial modes, such as Hermite Gaussian, Laguerre-Gaussian, or simply distinct aperture phase fronts available. It then forms a unitary matrix of beamforming weights, with each beam being distinct and of equal power from one another. The transmitter sends short pilot bursts using one or more of these available beams, and the receiver determines which beam performed the best, and returns this information to the transmitter. As conditions may change over time, this process repeats on a set cycle.
Benefits for FSO
RUB is beneficial mostly in environments where high turbulence or scintillation is present where random beams will periodically align with conditions, there is a limited ability for the receiver to give feedback, and the transmission is multi-modal or multi-aperture.
References
Viswanath, P., Tse, D. N. C., & Laroia, R. (2002). Opportunistic beamforming using dumb antennas. IEEE Transactions on Information Theory, 48(6), 1277–1294.
https://collaborate.princeton.edu/en/publications/opportunistic-beamforming-using-dumb-antennas-2/
Sharif, M., & Hassibi, B. (2005). On the capacity of MIMO broadcast channels with partial side information. IEEE Transactions on Information Theory, 51(2), 506–522.
https://www.babak.caltech.edu/pubs/papers/mimobc.pdf
Y. Tsuchiya, T. Ohtsuki and T. Kaneko, “Random Beamforming Using Low Feedback and Low Latency Power Allocation,” 2007 IEEE 66th Vehicular Technology Conference, Baltimore, MD, USA, 2007, pp. 402-406
https://ieeexplore.ieee.org/document/4349745
Al-Imran, Mostafa Zaman Chowdhury, Rafat Bin Mofidul, Yeong Min Jang, Machine learning and deep learning in FSO communication: A comprehensive survey, ICT Express, 2025
https://www.sciencedirect.com/science/article/pii/S2405959525001614