# UB3: Best Beam Identification in Millimeter Wave Systems via Pure Exploration Unimodal Bandits

arXiv: https://arxiv.org/abs/2301.03456 · HTML: https://mlsanity.com/paper/2301.03456/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 26 Dec 2022.

## Builds on (most cited first, 10 of 10)

- [On the Complexity of Best Arm Identification in Multi-Armed Bandit Models](https://mlsanity.com/paper/1407.4443/) (2014)
- [Tight (Lower) Bounds for the Fixed Budget Best Arm Identification Bandit Problem](https://mlsanity.com/paper/1605.09004/) (2016)
- [Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms](https://mlsanity.com/paper/1405.5096/) (2014)
- [Millimeter Wave Channel Modeling and Cellular Capacity Evaluation](https://mlsanity.com/paper/1312.4921/) (2013)
- [Fast mmwave Beam Alignment via Correlated Bandit Learning](https://mlsanity.com/paper/1909.03313/) (2019)
- [Directional Initial Access for Millimeter Wave Cellular Systems](https://mlsanity.com/paper/1511.06483/) (2015)
- [Deep Reinforcement Learning based Blind mmWave MIMO Beam Alignment](https://mlsanity.com/paper/2001.09251/) (2020)
- [Deep learning-based beam alignment in mmWave vehicular networks](https://mlsanity.com/paper/1910.11402/) (2019)
- [Enhanced Random Access and Beam Training for mmWave Wireless Local Networks with High User Density](https://mlsanity.com/paper/1709.03734/) (2017)
- [Forced-exploration free Strategies for Unimodal Bandits](https://mlsanity.com/paper/2006.16569/) (2020)

## Built on by (most cited first, 1 of 1)

- [HoloBeam: Learning Optimal Beamforming in Far-Field Holographic Metasurface Transceivers](https://mlsanity.com/paper/2401.05420/) (2023)

## Abstract (arXiv)

Millimeter wave (mmWave) communications have a broad spectrum and can support data rates in the order of gigabits per second, as envisioned in 5G systems. However, they cannot be used for long distances due to their sensitivity to attenuation loss. To enable their use in the 5G network, it requires that the transmission energy be focused in sharp pencil beams. As any misalignment between the transmitter and receiver beam pair can reduce the data rate significantly, it is important that they are aligned as much as possible. To find the best transmit-receive beam pair, recent beam alignment (BA) techniques examine the entire beam space, which might result in a large amount of BA latency. Recent works propose to adaptively select the beams such that the cumulative reward measured in terms of received signal strength or throughput is maximized. In this paper, we develop an algorithm that exploits the unimodal structure of the received signal strengths of the beams to identify the best beam in a finite time using pure exploration strategies. Strategies that identify the best beam in a fixed time slot are more suitable for wireless network protocol design than cumulative reward maximization strategies that continuously perform exploration and exploitation. Our algorithm is named Unimodal Bandit for Best Beam (UB3) and identifies the best beam with a high probability in a few rounds. We prove that the error exponent in the probability does not depend on the number of beams and show that this is indeed the case by establishing a lower bound for the unimodal bandits. We demonstrate that UB3 outperforms the state-of-the-art algorithms through extensive simulations. Moreover, our algorithm is simple to implement and has lower computational complexity.