Increasing demand for higher data-rate wireless connectivity with lower latency is fueling the explorations of millimeter-wave (mmWave) spectrum and massive MIMO communications. Both technologies are recognized as the key enablers of 5G and beyond 5G (B5G) networks. Hybrid beamforming is one of the most promising energy and cost-effective approaches to realize mmWave massive MIMO communications with lower complexity and smaller training overhead. With the motivation of giving more insights and in-deep technical recommendations to B5G network designers regarding hybrid beamforming, we present a hybrid beamforming taxonomy in terms of channel state information (CSI) availability, frequency bandwidth, architecture complexity, analog beamformer components, number of users, connectivity to RF chains, and the digital and analog beamforming design. Furthermore, we provide a comprehensive survey on the state-of-the-art use-cases for each classification followed by identification of the future challenges and open research issues.

Taxonomy and Performance Evaluation of Hybrid Beamforming for 5G and Beyond Systems

Hybrid beamforming (HB) has emerged as a promising technology to support ultra high transmission capacity and with low complexity for Millimeter Wave (mmWave) multiple-input and multiple-output (MIMO) system. However, the design of digital and analog beamformer is a challenge task with non-convex optimization, especially for the multi-user scenario. Recently, the blooming of deep learning research provides a new vision for the signal processing of communication system. In this work, we propose a deep neural network based HB for the multi-User mmWave massive MIMO system, referred as DNHB. The HB system is formulated as an autoencoder neural network, which is trained in a style of end-to-end self-supervised learning. With the strong representation capability of deep neural network, the proposed DNHB exhibits superior performance than the traditional linear processing methods. According to the simulation results, DNHB outperforms about 2 dB in terms of bit error rate (BER) performance compared with existing methods.

Deep Neural Hybrid Beamforming for Multi-User mmWave Massive MIMO System

Hybrid Beamforming/Combining for Millimeter Wave MIMO: A Machine Learning Approach

As a critical enabler for beyond fifth-generation (B5G) technology, millimeter wave (mmWave) beamforming for mmWave has been studied for many years. Multi-input multi-output (MIMO) system, which is the baseline for beamforming operation, rely heavily on multiple antennas to stream data in mmWave wireless communication systems. High-speed mmWave applications face challenges such as blockage and latency overhead. In addition, the efficiency of the mobile systems is severely impacted by the high training overhead required to discover the best beamforming vectors in large antenna array mmWave systems. In order to mitigate the stated challenges, in this paper, we propose a novel deep reinforcement learning (DRL) based coordinated beamforming scheme where multiple base stations serve one mobile station (MS) jointly. The constructed solution then uses a proposed DRL model and predicts the suboptimal beamforming vectors at the base stations (BSs) out of possible beamforming codebook candidates. This solution enables a complete system that facilitates highly mobile mmWave applications with dependable coverage, minimal training overhead, and low latency. Numerical results demonstrate that our proposed algorithm remarkably increases the achievable sum rate capacity for the highly mobile mmWave massive MIMO scenario while ensuring low training and latency overhead.

/pdf/deep-reinforcement-learning-based-coordinated-beamforming-bbhcfbg8.pdf

Deep Reinforcement Learning-Based Coordinated Beamforming for mmWave Massive MIMO Vehicular Networks

Hybrid beamforming (HB) based millimeter wave (mm-Wave) transmission is a promising technology for providing low latency and high data rate transmission. However, due to the high path loss, the performance of mmWave-based HB is significantly impacted by channel degradation, especially when the number of the available transmission path is less than the transmission streams. Driven by deep learning (DL) based beamforming, we propose a non-orthogonal precoding based HB scheme for the deterioration of channel transmission. Instead of directly applying the neural network as the beamforming function, we present the insight and theoretical analysis of the beamforming strategy in the DL based HB. By studying the beamforming behavior, we designed and formulated deep learning driven hybrid beamforming method. Compared with the existing DL-based hybrid beamforming, the neural network in the proposed method acts as a general neural codebook, which avoids network training even channel changed. In the overloaded channel condition, the proposed deep learning driven non-orthogonal precoding can solve the problem of channel degradation, which improves the robustness for hybrid beamforming. Simulation results show that the proposed method can achieve significant performance improvement over existing ones in overloaded transmission conditions. We also designed and implemented our scheme over the FPGA platform, which shows a much lower hardware cost than other existing schemes.

Deep Learning Driven Non-Orthogonal Precoding for Millimeter Wave Communications

Hybrid beamforming is a promising technology to reduce power consumption and provide high spectrum efficiency for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. However, it is intractable to obtain global optima for similar constrained joint optimization problems by limitation of hardware architecture. In this work, we proposed a constrained deep neural network (constrained-DNN) based hybrid beamforming for mmWave massive MIMO system, which employs neural networks to replace the beamforming matrices in traditional hybrid beamforming to achieve end-to-end autonomous hybrid beamforming. Traditional hybrid beamforming optimization problem is transformed into a neural network optimization problem, which break the limitation of non-convex optimization. We also present numerical results on the performance of the proposed algorithms, which exhibits significant improvement on bit error rate (BER) performance compared with existing hybrid beamforming schemes.

Constrained Deep Neural Network Based Hybrid Beamforming for Millimeter Wave Massive MIMO Systems

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Siyu Luo

Author Tools

Chat about Author

Papers

Constrained Deep Neural Network Based Hybrid Beamforming for Millimeter Wave Massive MIMO Systems