کنفرانس بین المللی فناوری اطلاعات و دانش

English

صفحه اصلی / سیزدهمین کنفرانس بین المللی فناوری اطلاعات و دانش

A Deep Neural Network-based Method for MmWave Time-varying Channel Estimation

نویسندگان :

Amirhossein Molazadeh¹ Zahra Maroufi² Mehrdad Ardebilipour³

1- دانشگاه خواجه نصیر‌الدین طوسی 2- دانشگاه خواجه نصیر‌الدین طوسی 3- دانشگاه خواجه نصیر‌الدین طوسی

کلمات کلیدی :

mmwave communication،hybrid beamforming،machine learning،channel estimation،deap neural network

چکیده :

A time-varying channel model makes estimating the channel coefficients challenging for the millimeter wave (mmWave) multi user multi-input multi-output (MIMO) communication, attributable to the many coefficients that have to be estimated with a limited number of measurements as well as the severe propagation loss experienced by the mmWave band. Thus, it is proposed to divide the channel estimation in time-varying mmWave systems in two stages, using a frame structure and assuming that angles of arrival/departure (AoAs/AoDs) vary much more slowly than path gains. MmWave channels have a sparse nature that is leveraged in the first stage to formulate the estimate of AoAs/AoDs as a block-sparse signal recovery problem. By the obtained estimate of the AoAs/AoDs, in the second stage the beamforming that maximize the desired pilot power is utilized in order to measure the path gains accurately. In this article, we propose the Deep Neural Network based Angle Estimation (DNNAE) algorithm by defining a deep neural network structure with appropriate input and output. Accordingly, we provide a method based on machine learning to increase the accuracy of channel AoDs/AoAs estimation. Therefore, without the need to update the angle grid area and with low complexity, we obtain a suitable estimation accuracy. Simulation results demonstrate that with the proposed DNNAE scheme, we outperform the previously proposed Adaptive Angle Estimation (AAE) algorithm despite the much lower computational complexity.

لیست مقالات

لیست مقالات بایگانی شده

Fast Duplicate Bug Reports Detector Training using Sampling for Dimension Reduction

Behzad Soleimani Neysiani - Saeed Doostali - Seyed Morteza Babamir - Zahra Aminoroaya

Improving Privacy Protection in a Collaborative Blockchain-based E-Health Records System

Arman Emam-Hoseini - Samane Sobuti - دکتر سیاوش خرسندی - Alireza Hashemi-Golpayeghani

Movable Antenna Design for UAV-Aided Federated Learning via Deep Reinforcement Learning

MOHSEN Ahmadzadeh - Saeid Pakravan - Ghosheh Abed Hodtani

Analysing effect of news polarity on stock market prediction: a machine learning approach

Golshid Ranjbaran - Dr Mohammad-Shahram Moin - Dr Sasan H Alizadeh - Dr Abbas Koochari

Evaluating LLMs in Persian News Summarization

Arya VarastehNezhad - Reza Tavasoli - Mostafa Masumi - Seyed Soroush Majd - Mehrnoush Shamsfard

تشخیص ارتباط معنایی در استک‌اورفلو با رمزگذار جمله جهانی

مجید دلیری - جعفر حبیبی - عیسی انامرادنژاد

Real-Time EEG-Based Analysis Of Stress-Inducing Stimuli

Mohsen Mahmoudi - Fattaneh Taghiyareh - Yasamin Akhavein - Elnaz Ghorbani

A novel approach audio watermarking based on (GBT,DCT,SVD)

Mahdi Mosleh

ارائه یک الگوریتم سلسله مراتبی جهت تشخیص نفوذ در شبکه های کامپیوتری

دکتر باقر رحیم پور کامی - سیدمحمد سیدی برشی باقر رحیم پور کامی - سیدمحمد سیدی برشی -

A Demand Response Schema in Industry: Smart Scheduling Approach for Industrial Processes

Negin Shafinezhad - Hamid Abrishami - Maryam Mahmoodi

بیشتر

ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 42.0.3