Výsledky vyhledávání - "Mehrabi, Mehrtash"

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Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network

Autor: Mehrabi, Mehrtash, Masoudimansour, Walid, Zhang, Yingxue, Chuai, Jie, Chen, Zhitang, Coates, Mark, Hao, Jianye, Geng, Yanhui

The mobile communication enabled by cellular networks is the one of the main foundations of our modern society. Optimizing the performance of cellular networks and providing massive connectivity with improved coverage and user experience has a consid

Externí odkaz: http://arxiv.org/abs/2301.03412

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Activity Detection for Grant-Free NOMA in Massive IoT Networks

Autor: Mehrabi, Mehrtash, Mohammadkarimi, Mostafa, Ardakani, Masoud

Recently, grant-free transmission paradigm has been introduced for massive Internet of Things (IoT) networks to save both time and bandwidth and transmit the message with low latency. In order to accurately decode the message of each device at the ba

Externí odkaz: http://arxiv.org/abs/2301.01274

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Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation

Autor: Mehrabi, Mehrtash, Zhang, Yingxue

One of the challenges in studying the interactions in large graphs is to learn their diverse pattern and various interaction types. Hence, considering only one distribution and model to study all nodes and ignoring their diversity and local features

Externí odkaz: http://arxiv.org/abs/2211.09740

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Decision Directed Channel Estimation Based on Deep Neural Network k-step Predictor for MIMO Communications in 5G

Autor: Mehrabi, Mehrtash, Mohammadkarimi, Mostafa, Ardakani, Masoud, Jing, Yindi

We consider the use of deep neural network (DNN) to develop a decision-directed (DD)-channel estimation (CE) algorithm for multiple-input multiple-output (MIMO)-space-time block coded systems in highly dynamic vehicular environments. We propose the u

Externí odkaz: http://arxiv.org/abs/1901.03435

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Deep Learning Based Sphere Decoding

Autor: Mohammadkarimi, Mostafa, Mehrabi, Mehrtash, Ardakani, Masoud, Jing, Yindi

Publikováno v: IEEE Trans. Wireless Commun. vol. 18, no. 9, pp. 4368-4378, June. 2019

In this paper, a deep learning (DL)-based sphere decoding algorithm is proposed, where the radius of the decoding hypersphere is learned by a deep neural network (DNN). The performance achieved by the proposed algorithm is very close to the optimal m

Externí odkaz: http://arxiv.org/abs/1807.03162

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On minimum distance of locally repairable codes

Autor: Mehrabi, Mehrtash, Ardakani, Massoud

Distributed and cloud storage systems are used to reliably store large-scale data. Erasure codes have been recently proposed and used in real-world distributed and cloud storage systems such as Google File System, Microsoft Azure Storage, and Faceboo

Externí odkaz: http://arxiv.org/abs/1702.02642

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Optimal Locally Repairable Codes with Improved Update Complexity

Autor: Mehrabi, Mehrtash, Shahabinejad, Mostafa, Ardakani, Masoud, Khabbazian, Majid

For a systematic erasure code, update complexity (UC) is defined as the maximum number of parity blocks needed to be changed when some information blocks are updated. Locally repairable codes (LRCs) have been recently proposed and used in real-world

Externí odkaz: http://arxiv.org/abs/1606.09463

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