Hadia El-Hennawy

The rapid increase in interest for Vehicle-to-Everything (V2X) networks has created significant challenges in efficient radio resource management. This paper addresses the problem of joint subcarrier assignment and power allocation to maximize the spectral efficiency of the system. First, this paper mathematically formulates resource allocation and power allocation as an optimization problem, which is solved using conventional optimization methodologies to establish a baseline for performance benchmarking. To overcome the high computational complexity associated with traditional optimization, we subsequently propose a Multi-Agent Deep Q-Network (Multi-DQN) agent framework based on deep reinforcement learning (DRL). The proposed agent learns optimal allocation strategies through interaction with the environment, enabling adaptive and real-time decision-making. The system performance is investigated in different environments under both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, addressing a gap in prior approaches. Simulation results demonstrate that the proposed Multi-DQN agent approach significantly outperforms the enhanced conventional benchmark, achieving higher spectral efficiency (SE) while substantially reducing the computational complexity.

This paper presents a compact solution for the design and analysis of the Active Integrated Antenna Array (AIAA) to improve its performance under high mutual coupling and mismatch losses. A simple compensation network (CN) is introduced to eliminate the mismatches in AIAA introduced by the antenna elements’ mutual coupling, which negatively impacts the power amplifier (PA) performance rather than previously proposed complex methods. The AIAA consists of a wideband GaN power amplifier (PA) transistor operating from 3 to 5 GHz, a wideband 4-element antenna array with λg/2 spacing between elements at 3.8 GHz operating from 3.6 to 4 GHz for 5G applications, and a compensation network designed to mitigate mismatch effects. To verify the proposed approach, two AIAA prototypes are measured, one with the CNs connected between each PA and the corresponding antenna element, and the other one with the antenna elements directly connected to the PAs. Experimental results show that the minimum realized gain of the proposed AIAA is more than 19.3 dBi over the operating bandwidth, with an enhancement of more than 2.5 dBi when compared to the conventional AIAA, where no CNs are used. Simulations and experimental results are presented along with detailed design and measurement procedures.

A compact, single-layer W-band microstrip antenna for forward-looking ADAS radar in the 77–79 GHz band is presented. The 16.5 × 22 mm² PCB element integrates a linear microstrip taper, two shorting vias, and a slot-loaded cavity to stabilize input reactance and broaden the in-band match. Full-wave simulations and launcher-based measurements using WR-12 TRL de-embedding and anechoic-chamber substitution confirm S₁₁ ≤ −10 dB across 77–79 GHz. At 77/79 GHz, the antenna achieves end-fire realized gains of ≈9.9/≈11.2 dBi. The main beam is end-fire (peak near θ ≈ 90°), with −3 dB beamwidths of ≈36° in the θ-cut at φ = 0 (pointing ≈ 61°/56°) and ≈11.6° in the φ-cut at θ = 90°. First sidelobes are about −2.3/−2.5 dB (θ-cut) and −3.1/−3.4 dB (φ-cut). Cross-polarization is ≥18 dB below co-polarization, and the simulated radiation efficiency reaches ≈85% at 77 GHz and ≈80% at 79 GHz. A controlled thermal sweep (25–105 °C) yields < 100 MHz resonance drift while maintaining ≥ 10 dB return loss. Due to its planar architecture and clean feed integration, compact module packaging in short- to medium-range automotive radars.

Intelligent Reflecting Surfaces (IRS) and Multiple-Input Single-Output (MISO) technologies are essential in the fifth generation (5G) networks and beyond. IRS optimizes the signal propagation and the coverage and is a viable approach to address the issues caused by fading channels that limits the spectral efficiency, while MIMO enhances data rates, reliability, and spectral efficiency by using multiple antennas at both transmitter and receiver ends. This paper proposes an IRS-assisted MISO system using the Orthogonal Space-Time Block Code (OSTBC) scheme to enhance the channel reliability and reduce the Bit Error Rate (BER) in wireless communication systems. The proposed system exploits the benefits from the transmit diversity gain of the OSTBC scheme as well as from the bit energy to noise power spectral density (E_b/N_o) improvement of the IRS technology. The presented work explores these combined technologies across different modulation schemes. The obtained results outperform the similar previously published works by considering higher-order modulation schemes as well as the deployment of rate ¾ OSTBC-assisted IRS. Moreover, the obtained results demonstrate that the integration of OSTBC with IRS can yield significant performance improvements in terms of E_b/N_o by 7 dB and 13 dB when using 16 reflecting elements and 64 reflecting elements, respectively.

For the deployment of Sixth Generation (6G) networks, integrating Massive Multiple-Input Multiple-Output (Massive MIMO) systems with Intelligent Reflecting Surfaces (IRS) is highly recommended due to its significant benefits in reducing communication losses for Non-Line-of-Sight (NLoS) conditions. However, the use of passive IRS presents challenges in channel estimation, mainly due to the significant feedback overhead required in Frequency Division Duplex (FDD)-based Massive MIMO systems. To address these challenges, this paper introduces a novel Denoising Gated Recurrent Unit with a Dropout-based Channel state information Network (DGD-CNet). The proposed DGD-CNet model is specifically designed for FDD-based IRS-aided Massive MIMO systems, aiming to reduce the feedback overhead while improving the channel estimation accuracy. By leveraging the Dropout (DO) technique with the Gated Recurrent Unit (GRU), the DGD-CNet model enhances the channel estimation accuracy and effectively captures both spatial structures and time correlation in time-varying channels. The results show that the proposed DGD-CNet model outperformed existing models in the literature, achieving at least a 26% improvement in Normalized Mean Square Error (NMSE), a 2% increase in correlation coefficient, and a 4% in system accuracy under Low-Compression Ratio (Low-CR) in indoor situations. Additionally, the proposed model demonstrates effectiveness across different CRs and in outdoor scenarios.

Intelligent reflecting surfaces (IRS) are an innovative technique that dramatically increases system efficiency. The integration of massive multiple-input multiple-output (massive MIMO) and IRS has been considered the most efficient route to 6G networks. An important challenge in IRS-aided massive MIMO wireless systems is channel estimation. With a rise in the number of IRS-reflecting elements and IRS-assisted users, channel training overhead becomes too large, resulting in large transmission delays and poor data transfer rates. To overcome this problem, an enhanced compressive sensing (CS) method to determine reliable channel state information (CSI) in IRS-aided massive MIMO systems is proposed, which combines enhanced compressive sensing with a deep denoising convolution neural network (CsiNet-DeCNN). By using deep learning methods to denoise channel data, our proposed model is validated numerically, indicating that it is accurate with low NMSE. Further, the results indicate that CsiNet-DeCNN performs better than traditional CS methods in estimating channel parameters.

This paper presents a printed multiple-input multiple-output (MIMO) antenna with the advantages of compact size, good MIMO diversity performance and simple geometry for fifth-generation (5G) millimeter-wave (mm-Wave) applications. The antenna offers a novel Ultra-Wide Band (UWB) operation from 25 to 50 GHz, using a Defective Ground Structure (DGS) technology. Firstly, its compact size makes it suitable for integrating different telecommunication devices for various applications, with a prototype fabricated having a total size of 33 mm × 33 mm × 0.233 mm. Second, the mutual coupling between the individual elements severely impacts the diversity properties of the MIMO antenna system. An effective technique of orthogonally positioning the antenna elements to each other increased their isolation; thus, the MIMO system provides the best diversity performance. The performance of the proposed MIMO antenna was investigated in terms of S-parameters and MIMO diversity parameters to ensure its suitability for future 5G mm-Wave applications. Finally, the proposed work was verified by measurements and exhibited a good match between simulated and measured results. It achieves UWB, high isolation, low mutual coupling, and good MIMO diversity performance, making it a good candidate and seamlessly housed in 5G mm-Wave applications.

The work on perfecting the rapid proliferation of wireless technologies resulted in the development of wireless modeling standards, protocols, and control of wireless manipulators. Several mobile communication technology applications in different fields are dramatically revolutionized to deliver more value at less cost. Multiple-access Edge Computing (MEC) offers excellent advantages for Beyond 5G (B5G) and Sixth-Generation (6G) networks, reducing latency and bandwidth usage while increasing the capability of the edge to deliver multiple services to end users in real time. We propose a Cluster-based Multi-User Multi-Server (CMUMS) caching algorithm to optimize the MEC content caching mechanism and control the distribution of high-popular tasks. As part of our work, we address the problem of integer optimization of the content that will be cached and the list of hosting servers. Therefore, a higher direct hit rate will be achieved, a lower indirect hit rate will be achieved, and the overall time delay will be reduced. As a result of the implementation of this system model, maximum utilization of resources and development of a completely new level of services and innovative approaches will be possible.

Optical space communication as a relay satellite system has been employed. The use of laser satellite communication (SatCom) can alleviate the issues that RF satellite communication encounters. In this study, channel turbulence, additional technologies, intensity modulation direct detection (IM/DD), decoding, and forwarding (DF) are used to evaluate the effectiveness of a proposed laser uplink satellite relay communication model in terms of outage probability (OP) and bit error rate (BER) indicators. This article looks at the necessary channel properties to analyze how operating wavelength affects system performance and compares our results with those of the optical direct link. The performance of the system is investigated by looking at the necessary system parameters, which show the superiority of the proposed system over the optical downlink SatCom, which can improve the required aperture radius up to 10% and the overall system performance up to 57%.