Fast Beam Alignment via Pure Exploration in Multi-armed Bandits
- URL: http://arxiv.org/abs/2210.12625v1
- Date: Sun, 23 Oct 2022 05:57:39 GMT
- Title: Fast Beam Alignment via Pure Exploration in Multi-armed Bandits
- Authors: Yi Wei and Zixin Zhong and Vincent Y. F. Tan
- Abstract summary: We develop a bandit-based fast BA algorithm to reduce BA latency for millimeter-wave (mmWave) communications.
Our algorithm is named Two-Phase Heteroscedastic Track-and-Stop (2PHT&S)
- Score: 91.11360914335384
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The beam alignment (BA) problem consists in accurately aligning the
transmitter and receiver beams to establish a reliable communication link in
wireless communication systems. Existing BA methods search the entire beam
space to identify the optimal transmit-receive beam pair. This incurs a
significant latency when the number of antennas is large. In this work, we
develop a bandit-based fast BA algorithm to reduce BA latency for
millimeter-wave (mmWave) communications. Our algorithm is named Two-Phase
Heteroscedastic Track-and-Stop (2PHT\&S). We first formulate the BA problem as
a pure exploration problem in multi-armed bandits in which the objective is to
minimize the required number of time steps given a certain fixed confidence
level. By taking advantage of the correlation structure among beams that the
information from nearby beams is similar and the heteroscedastic property that
the variance of the reward of an arm (beam) is related to its mean, the
proposed algorithm groups all beams into several beam sets such that the
optimal beam set is first selected and the optimal beam is identified in this
set after that. Theoretical analysis and simulation results on synthetic and
semi-practical channel data demonstrate the clear superiority of the proposed
algorithm vis-\`a-vis other baseline competitors.
Related papers
- Deep Learning and Image Super-Resolution-Guided Beam and Power
Allocation for mmWave Networks [80.37827344656048]
We develop a deep learning (DL)-guided hybrid beam and power allocation approach for millimeter-wave (mmWave) networks.
We exploit the synergy of supervised learning and super-resolution technology to enable low-overhead beam- and power allocation.
arXiv Detail & Related papers (2023-05-08T05:40:54Z) - Reliable Beamforming at Terahertz Bands: Are Causal Representations the
Way Forward? [85.06664206117088]
Multi-user wireless systems can meet metaverse requirements by utilizing terahertz bandwidth with massive number of antennas.
Existing solutions lack proper modeling of channel dynamics, resulting in inaccurate beamforming solutions in high-mobility scenarios.
Herein, a dynamic, semantically aware beamforming solution is proposed for the first time, utilizing novel artificial intelligence algorithms in variational causal inference.
arXiv Detail & Related papers (2023-03-14T16:02:46Z) - UB3: Best Beam Identification in Millimeter Wave Systems via Pure
Exploration Unimodal Bandits [7.253481390411171]
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.
Our algorithm is named Unimodal Bandit for Best Beam (UB3) and identifies the best beam with a high probability in a few rounds.
arXiv Detail & Related papers (2022-12-26T09:24:22Z) - Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication
Systems [1.7467279441152421]
beam alignment (BA) is a critical issue in millimeter wave communication (mmWave)
We present a novel beam alignment scheme on the basis of a machine learning strategy, Bayesian optimization (BO)
In this work, we consider the beam alignment issue to be a black box function and then use BO to find the possible optimal beam pair.
arXiv Detail & Related papers (2022-07-28T15:37:49Z) - Fast Simultaneous Gravitational Alignment of Multiple Point Sets [82.32416743939004]
This paper proposes a new resilient technique for simultaneous registration of multiple point sets by interpreting the latter as particle swarms rigidly moving in the mutually induced force fields.
Thanks to the improved simulation with altered physical laws and acceleration of globally multiply-linked point interactions, our Multi-Body Gravitational Approach (MBGA) is robust to noise and missing data.
In various experimental settings, MBGA is shown to outperform several baseline point set alignment approaches in terms of accuracy and runtime.
arXiv Detail & Related papers (2021-06-21T17:59:40Z) - Low-Latency Federated Learning over Wireless Channels with Differential
Privacy [142.5983499872664]
In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server.
In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement.
arXiv Detail & Related papers (2021-06-20T13:51:18Z) - Digital Beamforming Robust to Time-Varying Carrier Frequency Offset [21.18926642388997]
We present novel beamforming algorithms that are robust to signal corruptions arising from a time-variant carrier frequency offset.
We propose two atomic-norm-minimization (ANM)-based methods to design a weight vector that can be used to cancel interference when there exist unknown time-varying frequency drift in the pilot and interferer signals.
arXiv Detail & Related papers (2021-03-08T18:08:56Z) - On Single-User Interactive Beam Alignment in Next Generation Systems: A
Deep Learning Viewpoint [32.02074315139823]
Communication in high frequencies such as millimeter wave and terahertz suffer from high path-loss and intense shadowing.
Beam alignment strategies are used to find the direction of these channel clusters and adjust the width of the beam used for data transmission.
arXiv Detail & Related papers (2021-02-20T02:15:15Z) - Learning to Beamform in Heterogeneous Massive MIMO Networks [48.62625893368218]
It is well-known problem of finding the optimal beamformers in massive multiple-input multiple-output (MIMO) networks.
We propose a novel deep learning based paper algorithm to address this problem.
arXiv Detail & Related papers (2020-11-08T12:48:06Z) - Beamforming Learning for mmWave Communication: Theory and Experimental
Validation [23.17604790640996]
We propose a beam design technique that reduces the search time and does not require CSI while guaranteeing a minimum beamforming gain.
We evaluate the efficacy of the proposed scheme in terms of building the codebook and assessing its performance through real-life measurements.
arXiv Detail & Related papers (2019-12-28T05:46:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.