کنفرانس بین المللی فناوری اطلاعات و دانش

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

Effective Design of Reversible 2×2 Vedic Multiplier With Low Cost

Mojtaba Noorallahzadeh - Mohammad Mosleh - Ali Shahidikia

Fast Duplicate Bug Reports Detector Training using Sampling for Dimension Reduction

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

FedCloak: Backdoor-Based Covert Channels in Federated Learning

Mohammad Matin Rezaeifard - Fatemeh Zahedi - Seyed Arsalan Vasegh Rahim Parvar - Reza Ebrahimi Atani

BMPA- DSL: Binary Marine Predators Algorithm to Identify Driver's Different Levels of Stress

Mahtab Vaezi - Mehdi Nasri - Farhad Azimifar - Mahdi Mosleh

Knowledge Extraction from Technical Reports Based on Large Language Models: An Exploratory Study

Parsa Bakhtiari - Hassan Bashiri - Alireza Khalilipour - Masoud Nasiripour - Moharram Challenger

Intelligent Transportation System (ITS) Using Internet of Things (IoT)

Engineer Reza Khalilian - Dr. Abdalhossein Rezai - Dr. Sayyed Mohammad Reza Talakesh

بهبود تشخیص نفوذ به شبکه اینترنت اشیاء با استفاده از مدل ترکیبی الگوریتم های بهینه‌سازی ازدحام ذرات، گرگ خاکستری و جنگل تصادفی

مهدی علیرضانژاد - عمار عبیس حسین المعموری

Violence detection using one-dimensional convolutional networks

Narges Honarjoo - Ali Abdari - Dr Azadeh Mansouri

جانمایی توزیع‌شده محتوا برای ذخیره‌سازی موقت در شبکه‌های سلولی کوچک با حضور کاربران مخرب

زهرا رشیدی - دکتر وصال حکمی - حانیه سلمان‌طاهری زهرا رشیدی - وصال حکمی - حانیه سلمان‌طاهری -

سیستم توصیه گر برای خرید لوازم آرایشی و بهداشتی مبتنی بر الگوریتم جنگل تصادفی

فاطمه رمضانی خوزستانی - مجید رفیعی

بیشتر