Beamforming Learning for mmWave Communication: Theory and Experimental
Validation
- URL: http://arxiv.org/abs/1912.12406v1
- Date: Sat, 28 Dec 2019 05:46:39 GMT
- Title: Beamforming Learning for mmWave Communication: Theory and Experimental
Validation
- Authors: ohaned Chraiti, Dmitry Chizhik, Jinfeng Du, Reinaldo A. Valenzuela,
Ali Ghrayeb and Chadi Assi
- Abstract summary: 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.
- Score: 23.17604790640996
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: To establish reliable and long-range millimeter-wave (mmWave) communication,
beamforming is deemed to be a promising solution. Although beamforming can be
done in the digital and analog domains, both approaches are hindered by several
constraints when it comes to mmWave communications. For example, performing
fully digital beamforming in mmWave systems involves using many radio frequency
(RF) chains, which are expensive and consume high power. This necessitates
finding more efficient ways for using fewer RF chains while taking advantage of
the large antenna arrays. One way to overcome this challenge is to employ
(partially or fully) analog beamforming through proper configuration of
phase-shifters. Existing works on mmWave analog beam design either rely on the
knowledge of the channel state information (CSI) per antenna within the array,
require a large search time (e.g., exhaustive search) or do not guarantee a
minimum beamforming gain (e.g., codebook based beamforming). In this paper, we
propose a beam design technique that reduces the search time and does not
require CSI while guaranteeing a minimum beamforming gain. The key idea derives
from observations drawn from real-life measurements. It was observed that for a
given propagation environment (e.g., coverage area of a mmWave BS) the
azimuthal angles of dominant signals could be more probable from certain angles
than others. Thus, pre-collected measurements could used to build a beamforming
codebook that regroups the most probable beam designs. We invoke Bayesian
learning for measurements clustering. We evaluate the efficacy of the proposed
scheme in terms of building the codebook and assessing its performance through
real-life measurements. We demonstrate that the training time required by the
proposed scheme is only 5% of that of exhaustive search. This crucial gain is
obtained while achieving a minimum targeted beamforming gain.
Related papers
- HoloBeam: Learning Optimal Beamforming in Far-Field Holographic
Metasurface Transceivers [5.402030962296633]
Holographic Metasurface Transceivers (HMTs) are emerging as cost-effective substitutes to large antenna arrays for beamforming in Millimeter and TeraHertz wave communication.
To achieve desired channel gains through beamforming in HMT, phase-shifts of a large number of elements need to be appropriately set, which is challenging.
We develop a learning algorithm using a it fixed-budget multi-armed bandit framework to beamform and maximize received signal strength at the receiver for far-field regions.
arXiv Detail & Related papers (2023-12-30T03:29:32Z) - One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from
Electromagnetic Solvers [57.441926088870325]
Deep Image Prior (DIP) is a technique that optimized the weights of a randomly-d convolutional neural network to fit a signal from noisy or under-determined measurements.
Relative to publicly available implementations of Vector Fitting (VF), our method shows superior performance on nearly all test examples.
arXiv Detail & Related papers (2023-06-06T20:28:37Z) - 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) - Deep Learning for Hybrid Beamforming with Finite Feedback in GSM Aided
mmWave MIMO Systems [9.320559153486885]
Hybrid beamforming is widely recognized as an important technique for millimeter wave (mmWave) multiple input multiple output (MIMO) systems.
With the help of deep learning, the GSM hybrid beamformers are designed via unsupervised learning in an end-to-end way.
arXiv Detail & Related papers (2023-02-15T11:42:43Z) - 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) - Fast Beam Alignment via Pure Exploration in Multi-armed Bandits [91.11360914335384]
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)
arXiv Detail & Related papers (2022-10-23T05:57:39Z) - 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) - Performance of teleportation-based error correction circuits for bosonic
codes with noisy measurements [58.720142291102135]
We analyze the error-correction capabilities of rotation-symmetric codes using a teleportation-based error-correction circuit.
We find that with the currently achievable measurement efficiencies in microwave optics, bosonic rotation codes undergo a substantial decrease in their break-even potential.
arXiv Detail & Related papers (2021-08-02T16:12:13Z) - Applying Deep-Learning-Based Computer Vision to Wireless Communications:
Methodologies, Opportunities, and Challenges [100.45137961106069]
Deep learning (DL) has seen great success in the computer vision (CV) field.
This article introduces ideas about applying DL-based CV in wireless communications.
arXiv Detail & Related papers (2020-06-10T11:37:49Z)
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.