Browsing by Author "Salh, Adeb"

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Citation - WoS: 15
Citation - Scopus: 20
Low Computational Complexity for Optimizing Energy Efficiency in Mm-Wave Hybrid Precoding System for 5g
(Ieee-Inst Electrical Electronics Engineers Inc, 2022) Salh, Adeb; Audah, Lukman; Abdullah, Qazwan; Aydoğdu, Ömer; Alhartomi, Mohammed A.; Alsamhi, Saeed Hamood; Shah, Nor Shahida M.
Millimeter-wave (mm-wave) communication is the spectral frontier to meet the anticipated significant volume of high data traffic processing in next-generation systems. The primary challenges in mm-wave can be overcome by reducing complexity and power consumption by large antenna arrays for massive multiple-input multiple-output (mMIMO) systems. However, the circuit power consumption is expected to increase rapidly. The precoding in mm-wave mMIMO systems cannot be successfully achieved at baseband using digital precoders, owing to the high cost and power consumption of signal mixers and analog-to-digital converters. Nevertheless, hybrid analog-digital precoders are considered a cost-effective solution. In this work, we introduce a novel method for optimizing energy efficiency (EE) in the upper-bound multiuser (MU) - mMIMO system and the cost efficiency of quantized hybrid precoding (HP) design. We propose effective alternating minimization algorithms based on the zero gradient method to establish fully-connected structures (FCSs) and partially-connected structures (PCSs). In the alternating minimization algorithms, low complexity is proposed by enforcing an orthogonal constraint on the digital precoders to realize the joint optimization of computational complexity and communication power. Therefore, the alternating minimization algorithm enhances HP by improving the performance of the FCS through advanced phase extraction, which involves high complexity. Meanwhile, the alternating minimization algorithm develops a PCS to achieve low complexity using HP. The simulation results demonstrate that the proposed algorithm for MU - mMIMO systems improves EE. The power-saving ratio is also enhanced for PCS and FCS by 48.3% and 17.12%, respectively.
Citation - WoS: 1
Citation - Scopus: 2
Sustainability and Latency Reduction Through Federated Learning-Powered Digital Twins in Iot Devices
(Ieee, 2024) Abdullah, Qazwan; Salh, Adeb; Ahmed, Mustafa Sami; Shah, Nor Shahida Mohd; Aydogdu, Omer; Hussain, Ghasan Ali
The rapid advancement of emerging technologies and the Internet of Things (IoT), including the evolution of Digital Twins (DT), necessitates an accelerated pace in the Beyond Fifth Generation (B5G). This is crucial to establish widespread wireless access by ensuring resilient and immediate wireless connectivity within the real network environment. This article uses edge networks and DTs with blockchain technology. Ensuring robust real-time data processing while providing a scalable and secure solution is the aim. Bridging the gap between digital systems and physical edge networks is the goal. In this research, we bridge the gap between physical edge networks and digital systems by introducing Networks with Digital Twin Edges (NDITE), which combine digital twins and edge networks. Next, we propose a blockchain-driven federated learning method in NDITE to improve data privacy and communication security. We schedule relaying users and manage bandwidth resources using DT-powered Deep Reinforcement Learning (DRL) to increase efficiency. According to the simulation results, the suggested DRL agent-based DT can minimize the weighted cost of transmission policy of edge computing strategies and choose 47.5% of computing tasks to be completed locally with 1 MHz of bandwidth. It can also exploit the optimal policy.