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

TL;DR

This paper introduces UB3, a pure exploration unimodal bandit algorithm that efficiently identifies the best beam in millimeter wave systems, reducing latency and computational complexity for beam alignment in 5G networks.

Contribution

We propose UB3, a novel unimodal bandit algorithm that leverages the structure of received signal strengths to quickly and reliably find the optimal beam in millimeter wave communications.

Findings

UB3 achieves high probability of correct beam identification in fewer rounds.

The error exponent of UB3 is independent of the number of beams.

UB3 outperforms existing algorithms in simulation tests.

Abstract

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.