[Paper Review] Fast mmwave Beam Alignment via Correlated Bandit Learning

Wen Wu, Nan Cheng+5 more|arXiv (Cornell University)|Sep 7, 2019

Millimeter-Wave Propagation and Modeling32 references4 citations

TL;DR

This paper proposes the Hierarchical Beam Alignment (HBA) algorithm, a correlated bandit learning approach that leverages spatial correlation among millimeter-wave beams and prior channel fluctuation knowledge to reduce beam alignment latency. By modeling beam selection as a stochastic multi-armed bandit problem, HBA accelerates convergence to the optimal beam pair, reducing alignment time from hundreds of milliseconds to just a few milliseconds in multipath environments, outperforming IEEE 802.11ad methods by orders of magnitude.

ABSTRACT

Beam alignment (BA) is to ensure the transmitter and receiver beams are accurately aligned to establish a reliable communication link in millimeter-wave (mmwave) systems. Existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair, which incurs significant BA latency on the order of seconds in the worst case. In this paper, we develop a learning algorithm to reduce BA latency, namely Hierarchical Beam Alignment (HBA) algorithm. We first formulate the BA problem as a stochastic multi-armed bandit problem with the objective to maximize the cumulative received signal strength within a certain period. The proposed algorithm takes advantage of the correlation structure among beams such that the information from nearby beams is extracted to identify the optimal beam, instead of searching the entire beam space. Furthermore, the prior knowledge on the channel fluctuation is incorporated in the proposed algorithm to further accelerate the BA process. Theoretical analysis indicates that the proposed algorithm is asymptotically optimal. Extensive simulation results demonstrate that the proposed algorithm can identify the optimal beam with a high probability and reduce the BA latency from hundreds of milliseconds to a few milliseconds in the multipath channel, as compared to the existing BA method in IEEE 802.11ad.

Motivation & Objective

• To address the high latency of beam alignment in mmWave systems, which can reach seconds using exhaustive search methods.
• To reduce beam alignment latency in mobile and dynamic environments where frequent re-alignment is required.
• To exploit spatial correlation among beams and prior knowledge of channel fluctuations to accelerate convergence without exhaustive scanning.
• To develop a learning-based beam alignment algorithm that is asymptotically optimal with bounded regret.
• To outperform existing standards like IEEE 802.11ad in both speed and reliability under realistic multipath conditions.

Proposed method

• Formulates beam alignment as a stochastic multi-armed bandit problem to maximize cumulative received signal strength over time.
• Models beam correlation using a multimodal function, enabling inference about nearby beams from limited measurements.
• Incorporates prior knowledge of channel fluctuation to reduce unnecessary exploration and improve convergence speed.
• Uses a hierarchical tree structure to partition beam candidates into three subtrees: optimal beams, near-optimal beams, and suboptimal beams.
• Applies regret analysis using bounds on beam subsets to prove asymptotic optimality, with theoretical regret bounded as $ O( ext{polylog}(T) imes T^{( heta+1)/( heta+2)}) $.
• Employs a dynamic beam selection strategy that prioritizes exploration in high-correlation regions while minimizing redundant measurements.

Experimental results

Research questions

• RQ1 Can beam correlation in mmWave channels be effectively exploited to reduce the number of beam measurements required for alignment?
• RQ2 How can prior knowledge of channel dynamics be integrated into a learning-based beam alignment framework to accelerate convergence?
• RQ3 What is the theoretical performance limit of beam alignment under correlated beam reward structures?
• RQ4 Can a bandit-based approach achieve sub-millisecond beam alignment latency in realistic multipath environments?
• RQ5 How does the proposed HBA algorithm compare in performance and latency to the IEEE 802.11ad beam alignment standard?

Key findings

• The HBA algorithm reduces beam alignment latency from hundreds of milliseconds to just a few milliseconds in multipath environments, representing a reduction by orders of magnitude compared to IEEE 802.11ad.
• Theoretical analysis proves that the regret of HBA is bounded and asymptotically optimal, with regret scaling as $ O( ext{polylog}(T) imes T^{( heta+1)/( heta+2)}) $.
• When the smoothness of the channel is known, the regret reduces to $ O( heta imes ext{polylog}(T) imes heta imes heta) $, achieving $ O( heta imes ext{polylog}(T) imes heta) $.
• The algorithm achieves high probability of identifying the optimal beam pair with significantly fewer measurements than exhaustive search.
• Even with coarse prior knowledge of channel fluctuations, HBA maintains strong performance, demonstrating robustness to imperfect channel models.
• Simulation results confirm that HBA effectively leverages beam correlation to avoid redundant exploration and accelerate convergence in dynamic mmWave environments.