Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Shahab, Muhammad"'
Autor:
Shahab, Muhammad Luthfi, Susanto, Hadi
This research introduces an extended application of neural networks for solving nonlinear partial differential equations (PDEs). A neural network, combined with a pseudo-arclength continuation, is proposed to construct bifurcation diagrams from param
Externí odkaz:
http://arxiv.org/abs/2407.19707
Autor:
Putri, Endah Rokhmati Merdika, Shahab, Muhammad Luthfi, Iqbal, Mohammad, Mukhlash, Imam, Hakam, Amirul, Mardianto, Lutfi, Susanto, Hadi
We propose a new method, called a deep-genetic algorithm (deep-GA), to accelerate the performance of the so-called deep-BSDE method, which is a deep learning algorithm to solve high dimensional partial differential equations through their correspondi
Externí odkaz:
http://arxiv.org/abs/2311.11558
Publikováno v:
IEEE Open Journal of the Communications Society, vol. 4, pp. 1499-1515, 2023
We consider the multi-user detection (MUD) problem in uplink grant-free non-orthogonal multiple access (NOMA), where the access point has to identify the total number and correct identity of the active Internet of Things (IoT) devices and decode thei
Externí odkaz:
http://arxiv.org/abs/2209.06392
Autor:
Salari, Ayoob, Shirvanimoghaddam, Mahyar, Shahab, Muhammad Basit, Li, Yonghui, Johnson, Sarah
This paper studies the joint channel estimation and signal detection for the uplink power-domain non-orthogonal multiple access. The proposed technique performs both detection and estimation without the need of pilot symbols by using a clustering tec
Externí odkaz:
http://arxiv.org/abs/2202.12514
Autor:
Salari, Ayoob, Shirvanimoghaddam, Mahyar, Shahab, Muhammad Basit, Arablouei, Reza, Johnson, Sarah
We propose a joint channel estimation and signal detection approach for the uplink non-orthogonal multiple access (NOMA) using unsupervised machine learning. We apply a Gaussian mixture model (GMM) to cluster the received signals, and accordingly opt
Externí odkaz:
http://arxiv.org/abs/2201.06245
Due to an increasing number of unmanned aerial vehicles (UAVs) with generally limited battery power, energy efficient data transmission schemes with massive connectivity capabilities are required for future wireless networks. Non-orthogonal multiple
Externí odkaz:
http://arxiv.org/abs/2011.01449
Autor:
Salari, Ayoob, Shirvanimoghaddam, Mahyar, Shahab, Muhammad Basit, Arablouei, Reza, Johnson, Sarah
We propose a joint channel estimation and signal detection technique for the uplink non-orthogonal multiple access using an unsupervised clustering approach. We apply the Gaussian mixture model to cluster received signals and accordingly optimize the
Externí odkaz:
http://arxiv.org/abs/2010.03091
In this letter, role switching and power allocation schemes are proposed to tackle user mobility in non-orthogonal multiple access (NOMA) systems. When cell center user (CCU) and cell edge user (CEU) come very close or even cross each other in NOMA p
Externí odkaz:
http://arxiv.org/abs/1912.05132
Massive machine-type communications (mMTC) is one of the main three focus areas in the 5th generation (5G) of mobile standards to enable connectivity of a massive number of internet of things (IoT) devices with little or no human intervention. In con
Externí odkaz:
http://arxiv.org/abs/1910.06529
In this paper, joint transmission coordinated multipoint (JT-CoMP) is exploited by using virtual user pairing non-orthogonal multiple access (VP-NOMA), termed as JT-CoMP VP-NOMA. The technique combines both VP-NOMA for enhancing ergodic sum capacity
Externí odkaz:
http://arxiv.org/abs/1903.10674