AI-Empowered Hybrid MIMO Beamforming
- URL: http://arxiv.org/abs/2303.01723v1
- Date: Fri, 3 Mar 2023 06:04:20 GMT
- Title: AI-Empowered Hybrid MIMO Beamforming
- Authors: Nir Shlezinger, Mengyuan Ma, Ortal Lavi, Nhan Thanh Nguyen, Yonina C.
Eldar, Markku Juntti
- Abstract summary: Hybrid multiple-input multiple-output (MIMO) systems implement part of their beamforming in analog and part in digital.
Recent years have witnessed a growing interest in using data-aided artificial intelligence (AI) tools for hybrid beamforming design.
This article reviews candidate strategies to leverage data to improve real-time hybrid beamforming design.
- Score: 85.48860461696417
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Hybrid multiple-input multiple-output (MIMO) is an attractive technology for
realizing extreme massive MIMO systems envisioned for future wireless
communications in a scalable and power-efficient manner. However, the fact that
hybrid MIMO systems implement part of their beamforming in analog and part in
digital makes the optimization of their beampattern notably more challenging
compared with conventional fully digital MIMO. Consequently, recent years have
witnessed a growing interest in using data-aided artificial intelligence (AI)
tools for hybrid beamforming design. This article reviews candidate strategies
to leverage data to improve real-time hybrid beamforming design. We discuss the
architectural constraints and characterize the core challenges associated with
hybrid beamforming optimization. We then present how these challenges are
treated via conventional optimization, and identify different AI-aided design
approaches. These can be roughly divided into purely data-driven deep learning
models and different forms of deep unfolding techniques for combining AI with
classical optimization.We provide a systematic comparative study between
existing approaches including both numerical evaluations and qualitative
measures. We conclude by presenting future research opportunities associated
with the incorporation of AI in hybrid MIMO systems.
Related papers
- Automatically Learning Hybrid Digital Twins of Dynamical Systems [56.69628749813084]
Digital Twins (DTs) simulate the states and temporal dynamics of real-world systems.
DTs often struggle to generalize to unseen conditions in data-scarce settings.
In this paper, we propose an evolutionary algorithm ($textbfHDTwinGen$) to autonomously propose, evaluate, and optimize HDTwins.
arXiv Detail & Related papers (2024-10-31T07:28:22Z) - Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning [50.332027356848094]
AI-based applications are deployed at intelligent controllers to carry out functionalities like scheduling or power control.
The mapping between context and AI model parameters is ideally done in a zero-shot fashion.
This paper introduces a general methodology for the online optimization of AMS mappings.
arXiv Detail & Related papers (2024-06-22T11:17:50Z) - Machine Learning Insides OptVerse AI Solver: Design Principles and
Applications [74.67495900436728]
We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver.
We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem.
We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
arXiv Detail & Related papers (2024-01-11T15:02:15Z) - Enhancing SMT-based Weighted Model Integration by Structure Awareness [10.812681884889697]
Weighted Model Integration (WMI) emerged as a unifying formalism for probabilistic inference in hybrid domains.
We develop an algorithm that combines SMT-based enumeration, an efficient technique in formal verification, with an effective encoding of the problem structure.
arXiv Detail & Related papers (2023-02-13T08:55:12Z) - Applying Autonomous Hybrid Agent-based Computing to Difficult
Optimization Problems [56.821213236215634]
This paper focuses on a proposed hybrid version of the EMAS.
It covers selection and introduction of a number of hybrid operators and defining rules for starting the hybrid steps of the main algorithm.
Those hybrid steps leverage existing, well-known and proven to be efficient metaheuristics, and integrate their results into the main algorithm.
arXiv Detail & Related papers (2022-10-24T13:28:35Z) - Unsupervised Learning Based Hybrid Beamforming with Low-Resolution Phase
Shifters for MU-MIMO Systems [7.585540240110219]
Existing hybrid beamforming designs based on infinite-resolution phase shifters (PSs) are impractical due to hardware cost and power consumption.
We propose an unsupervised-learning-based scheme to jointly design the analog precoder and combiner with low-resolution PSs.
We transform the analog precoder and combiner design problem into a phase classification problem and propose a generic neural network architecture.
arXiv Detail & Related papers (2022-02-04T02:45:40Z) - Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with
Heterogeneous Learning Tasks [53.1636151439562]
Mobile edge computing (MEC) provides a natural platform for AI applications.
We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS)
Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
arXiv Detail & Related papers (2020-12-25T07:08:50Z) - Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming [1.290382979353427]
Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems.
This paper proposes a novel RSSI-based unsupervised deep learning method to design the hybrid beamforming.
arXiv Detail & Related papers (2020-06-30T18:10:36Z)
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.